The Brain as an Efficient and Robust Adaptive Learner.

PubMed

Denève, Sophie; Alemi, Alireza; Bourdoukan, Ralph

2017-06-07

Understanding how the brain learns to compute functions reliably, efficiently, and robustly with noisy spiking activity is a fundamental challenge in neuroscience. Most sensory and motor tasks can be described as dynamical systems and could presumably be learned by adjusting connection weights in a recurrent biological neural network. However, this is greatly complicated by the credit assignment problem for learning in recurrent networks, e.g., the contribution of each connection to the global output error cannot be determined based only on locally accessible quantities to the synapse. Combining tools from adaptive control theory and efficient coding theories, we propose that neural circuits can indeed learn complex dynamic tasks with local synaptic plasticity rules as long as they associate two experimentally established neural mechanisms. First, they should receive top-down feedbacks driving both their activity and their synaptic plasticity. Second, inhibitory interneurons should maintain a tight balance between excitation and inhibition in the circuit. The resulting networks could learn arbitrary dynamical systems and produce irregular spike trains as variable as those observed experimentally. Yet, this variability in single neurons may hide an extremely efficient and robust computation at the population level. Copyright © 2017 Elsevier Inc. All rights reserved.

Efficient l1 -norm-based low-rank matrix approximations for large-scale problems using alternating rectified gradient method.

PubMed

Kim, Eunwoo; Lee, Minsik; Choi, Chong-Ho; Kwak, Nojun; Oh, Songhwai

2015-02-01

Low-rank matrix approximation plays an important role in the area of computer vision and image processing. Most of the conventional low-rank matrix approximation methods are based on the l2 -norm (Frobenius norm) with principal component analysis (PCA) being the most popular among them. However, this can give a poor approximation for data contaminated by outliers (including missing data), because the l2 -norm exaggerates the negative effect of outliers. Recently, to overcome this problem, various methods based on the l1 -norm, such as robust PCA methods, have been proposed for low-rank matrix approximation. Despite the robustness of the methods, they require heavy computational effort and substantial memory for high-dimensional data, which is impractical for real-world problems. In this paper, we propose two efficient low-rank factorization methods based on the l1 -norm that find proper projection and coefficient matrices using the alternating rectified gradient method. The proposed methods are applied to a number of low-rank matrix approximation problems to demonstrate their efficiency and robustness. The experimental results show that our proposals are efficient in both execution time and reconstruction performance unlike other state-of-the-art methods.

Learning Optimized Local Difference Binaries for Scalable Augmented Reality on Mobile Devices.

PubMed

Xin Yang; Kwang-Ting Cheng

2014-06-01

The efficiency, robustness and distinctiveness of a feature descriptor are critical to the user experience and scalability of a mobile augmented reality (AR) system. However, existing descriptors are either too computationally expensive to achieve real-time performance on a mobile device such as a smartphone or tablet, or not sufficiently robust and distinctive to identify correct matches from a large database. As a result, current mobile AR systems still only have limited capabilities, which greatly restrict their deployment in practice. In this paper, we propose a highly efficient, robust and distinctive binary descriptor, called Learning-based Local Difference Binary (LLDB). LLDB directly computes a binary string for an image patch using simple intensity and gradient difference tests on pairwise grid cells within the patch. To select an optimized set of grid cell pairs, we densely sample grid cells from an image patch and then leverage a modified AdaBoost algorithm to automatically extract a small set of critical ones with the goal of maximizing the Hamming distance between mismatches while minimizing it between matches. Experimental results demonstrate that LLDB is extremely fast to compute and to match against a large database due to its high robustness and distinctiveness. Compared to the state-of-the-art binary descriptors, primarily designed for speed, LLDB has similar efficiency for descriptor construction, while achieving a greater accuracy and faster matching speed when matching over a large database with 2.3M descriptors on mobile devices.

Scalable Robust Principal Component Analysis Using Grassmann Averages.

PubMed

Hauberg, Sren; Feragen, Aasa; Enficiaud, Raffi; Black, Michael J

2016-11-01

In large datasets, manual data verification is impossible, and we must expect the number of outliers to increase with data size. While principal component analysis (PCA) can reduce data size, and scalable solutions exist, it is well-known that outliers can arbitrarily corrupt the results. Unfortunately, state-of-the-art approaches for robust PCA are not scalable. We note that in a zero-mean dataset, each observation spans a one-dimensional subspace, giving a point on the Grassmann manifold. We show that the average subspace corresponds to the leading principal component for Gaussian data. We provide a simple algorithm for computing this Grassmann Average ( GA), and show that the subspace estimate is less sensitive to outliers than PCA for general distributions. Because averages can be efficiently computed, we immediately gain scalability. We exploit robust averaging to formulate the Robust Grassmann Average (RGA) as a form of robust PCA. The resulting Trimmed Grassmann Average ( TGA) is appropriate for computer vision because it is robust to pixel outliers. The algorithm has linear computational complexity and minimal memory requirements. We demonstrate TGA for background modeling, video restoration, and shadow removal. We show scalability by performing robust PCA on the entire Star Wars IV movie; a task beyond any current method. Source code is available online.

Efficient and robust model-to-image alignment using 3D scale-invariant features.

PubMed

Toews, Matthew; Wells, William M

2013-04-01

This paper presents feature-based alignment (FBA), a general method for efficient and robust model-to-image alignment. Volumetric images, e.g. CT scans of the human body, are modeled probabilistically as a collage of 3D scale-invariant image features within a normalized reference space. Features are incorporated as a latent random variable and marginalized out in computing a maximum a posteriori alignment solution. The model is learned from features extracted in pre-aligned training images, then fit to features extracted from a new image to identify a globally optimal locally linear alignment solution. Novel techniques are presented for determining local feature orientation and efficiently encoding feature intensity in 3D. Experiments involving difficult magnetic resonance (MR) images of the human brain demonstrate FBA achieves alignment accuracy similar to widely-used registration methods, while requiring a fraction of the memory and computation resources and offering a more robust, globally optimal solution. Experiments on CT human body scans demonstrate FBA as an effective system for automatic human body alignment where other alignment methods break down. Copyright © 2012 Elsevier B.V. All rights reserved.

Efficient and Robust Model-to-Image Alignment using 3D Scale-Invariant Features

PubMed Central

Toews, Matthew; Wells, William M.

2013-01-01

This paper presents feature-based alignment (FBA), a general method for efficient and robust model-to-image alignment. Volumetric images, e.g. CT scans of the human body, are modeled probabilistically as a collage of 3D scale-invariant image features within a normalized reference space. Features are incorporated as a latent random variable and marginalized out in computing a maximum a-posteriori alignment solution. The model is learned from features extracted in pre-aligned training images, then fit to features extracted from a new image to identify a globally optimal locally linear alignment solution. Novel techniques are presented for determining local feature orientation and efficiently encoding feature intensity in 3D. Experiments involving difficult magnetic resonance (MR) images of the human brain demonstrate FBA achieves alignment accuracy similar to widely-used registration methods, while requiring a fraction of the memory and computation resources and offering a more robust, globally optimal solution. Experiments on CT human body scans demonstrate FBA as an effective system for automatic human body alignment where other alignment methods break down. PMID:23265799

Robust and efficient pharmacokinetic parameter non-linear least squares estimation for dynamic contrast enhanced MRI of the prostate.

PubMed

Kargar, Soudabeh; Borisch, Eric A; Froemming, Adam T; Kawashima, Akira; Mynderse, Lance A; Stinson, Eric G; Trzasko, Joshua D; Riederer, Stephen J

2018-05-01

To describe an efficient numerical optimization technique using non-linear least squares to estimate perfusion parameters for the Tofts and extended Tofts models from dynamic contrast enhanced (DCE) MRI data and apply the technique to prostate cancer. Parameters were estimated by fitting the two Tofts-based perfusion models to the acquired data via non-linear least squares. We apply Variable Projection (VP) to convert the fitting problem from a multi-dimensional to a one-dimensional line search to improve computational efficiency and robustness. Using simulation and DCE-MRI studies in twenty patients with suspected prostate cancer, the VP-based solver was compared against the traditional Levenberg-Marquardt (LM) strategy for accuracy, noise amplification, robustness to converge, and computation time. The simulation demonstrated that VP and LM were both accurate in that the medians closely matched assumed values across typical signal to noise ratio (SNR) levels for both Tofts models. VP and LM showed similar noise sensitivity. Studies using the patient data showed that the VP method reliably converged and matched results from LM with approximate 3× and 2× reductions in computation time for the standard (two-parameter) and extended (three-parameter) Tofts models. While LM failed to converge in 14% of the patient data, VP converged in the ideal 100%. The VP-based method for non-linear least squares estimation of perfusion parameters for prostate MRI is equivalent in accuracy and robustness to noise, while being more reliably (100%) convergent and computationally about 3× (TM) and 2× (ETM) faster than the LM-based method. Copyright © 2017 Elsevier Inc. All rights reserved.

Robust efficient estimation of heart rate pulse from video.

PubMed

Xu, Shuchang; Sun, Lingyun; Rohde, Gustavo Kunde

2014-04-01

We describe a simple but robust algorithm for estimating the heart rate pulse from video sequences containing human skin in real time. Based on a model of light interaction with human skin, we define the change of blood concentration due to arterial pulsation as a pixel quotient in log space, and successfully use the derived signal for computing the pulse heart rate. Various experiments with different cameras, different illumination condition, and different skin locations were conducted to demonstrate the effectiveness and robustness of the proposed algorithm. Examples computed with normal illumination show the algorithm is comparable with pulse oximeter devices both in accuracy and sensitivity.

Robust efficient estimation of heart rate pulse from video

PubMed Central

Xu, Shuchang; Sun, Lingyun; Rohde, Gustavo Kunde

2014-01-01

We describe a simple but robust algorithm for estimating the heart rate pulse from video sequences containing human skin in real time. Based on a model of light interaction with human skin, we define the change of blood concentration due to arterial pulsation as a pixel quotient in log space, and successfully use the derived signal for computing the pulse heart rate. Various experiments with different cameras, different illumination condition, and different skin locations were conducted to demonstrate the effectiveness and robustness of the proposed algorithm. Examples computed with normal illumination show the algorithm is comparable with pulse oximeter devices both in accuracy and sensitivity. PMID:24761294

Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face Recognition.

PubMed

Ding, Changxing; Choi, Jonghyun; Tao, Dacheng; Davis, Larry S

2016-03-01

To perform unconstrained face recognition robust to variations in illumination, pose and expression, this paper presents a new scheme to extract "Multi-Directional Multi-Level Dual-Cross Patterns" (MDML-DCPs) from face images. Specifically, the MDML-DCPs scheme exploits the first derivative of Gaussian operator to reduce the impact of differences in illumination and then computes the DCP feature at both the holistic and component levels. DCP is a novel face image descriptor inspired by the unique textural structure of human faces. It is computationally efficient and only doubles the cost of computing local binary patterns, yet is extremely robust to pose and expression variations. MDML-DCPs comprehensively yet efficiently encodes the invariant characteristics of a face image from multiple levels into patterns that are highly discriminative of inter-personal differences but robust to intra-personal variations. Experimental results on the FERET, CAS-PERL-R1, FRGC 2.0, and LFW databases indicate that DCP outperforms the state-of-the-art local descriptors (e.g., LBP, LTP, LPQ, POEM, tLBP, and LGXP) for both face identification and face verification tasks. More impressively, the best performance is achieved on the challenging LFW and FRGC 2.0 databases by deploying MDML-DCPs in a simple recognition scheme.

Fast and Robust Registration of Multimodal Remote Sensing Images via Dense Orientated Gradient Feature

NASA Astrophysics Data System (ADS)

Ye, Y.

2017-09-01

This paper presents a fast and robust method for the registration of multimodal remote sensing data (e.g., optical, LiDAR, SAR and map). The proposed method is based on the hypothesis that structural similarity between images is preserved across different modalities. In the definition of the proposed method, we first develop a pixel-wise feature descriptor named Dense Orientated Gradient Histogram (DOGH), which can be computed effectively at every pixel and is robust to non-linear intensity differences between images. Then a fast similarity metric based on DOGH is built in frequency domain using the Fast Fourier Transform (FFT) technique. Finally, a template matching scheme is applied to detect tie points between images. Experimental results on different types of multimodal remote sensing images show that the proposed similarity metric has the superior matching performance and computational efficiency than the state-of-the-art methods. Moreover, based on the proposed similarity metric, we also design a fast and robust automatic registration system for multimodal images. This system has been evaluated using a pair of very large SAR and optical images (more than 20000 × 20000 pixels). Experimental results show that our system outperforms the two popular commercial software systems (i.e. ENVI and ERDAS) in both registration accuracy and computational efficiency.

Comparisons of Robustness and Sensitivity between Cancer and Normal Cells by Microarray Data

PubMed Central

Chu, Liang-Hui; Chen, Bor-Sen

2008-01-01

Robustness is defined as the ability to uphold performance in face of perturbations and uncertainties, and sensitivity is a measure of the system deviations generated by perturbations to the system. While cancer appears as a robust but fragile system, few computational and quantitative evidences demonstrate robustness tradeoffs in cancer. Microarrays have been widely applied to decipher gene expression signatures in human cancer research, and quantification of global gene expression profiles facilitates precise prediction and modeling of cancer in systems biology. We provide several efficient computational methods based on system and control theory to compare robustness and sensitivity between cancer and normal cells by microarray data. Measurement of robustness and sensitivity by linear stochastic model is introduced in this study, which shows oscillations in feedback loops of p53 and demonstrates robustness tradeoffs that cancer is a robust system with some extreme fragilities. In addition, we measure sensitivity of gene expression to perturbations in other gene expression and kinetic parameters, discuss nonlinear effects in feedback loops of p53 and extend our method to robustness-based cancer drug design. PMID:19259409

A Practical, Robust Methodology for Acquiring New Observation Data Using Computationally Expensive Groundwater Models

NASA Astrophysics Data System (ADS)

Siade, Adam J.; Hall, Joel; Karelse, Robert N.

2017-11-01

Regional groundwater flow models play an important role in decision making regarding water resources; however, the uncertainty embedded in model parameters and model assumptions can significantly hinder the reliability of model predictions. One way to reduce this uncertainty is to collect new observation data from the field. However, determining where and when to obtain such data is not straightforward. There exist a number of data-worth and experimental design strategies developed for this purpose. However, these studies often ignore issues related to real-world groundwater models such as computational expense, existing observation data, high-parameter dimension, etc. In this study, we propose a methodology, based on existing methods and software, to efficiently conduct such analyses for large-scale, complex regional groundwater flow systems for which there is a wealth of available observation data. The method utilizes the well-established d-optimality criterion, and the minimax criterion for robust sampling strategies. The so-called Null-Space Monte Carlo method is used to reduce the computational burden associated with uncertainty quantification. And, a heuristic methodology, based on the concept of the greedy algorithm, is proposed for developing robust designs with subsets of the posterior parameter samples. The proposed methodology is tested on a synthetic regional groundwater model, and subsequently applied to an existing, complex, regional groundwater system in the Perth region of Western Australia. The results indicate that robust designs can be obtained efficiently, within reasonable computational resources, for making regional decisions regarding groundwater level sampling.

Robust Nonrigid Multimodal Image Registration using Local Frequency Maps*

PubMed Central

Jian, Bing; Vemuri, Baba C.; Marroquin, José L.

2008-01-01

Automatic multi-modal image registration is central to numerous tasks in medical imaging today and has a vast range of applications e.g., image guidance, atlas construction, etc. In this paper, we present a novel multi-modal 3D non-rigid registration algorithm where in 3D images to be registered are represented by their corresponding local frequency maps efficiently computed using the Riesz transform as opposed to the popularly used Gabor filters. The non-rigid registration between these local frequency maps is formulated in a statistically robust framework involving the minimization of the integral squared error a.k.a. L2E (L2 error). This error is expressed as the squared difference between the true density of the residual (which is the squared difference between the non-rigidly transformed reference and the target local frequency representations) and a Gaussian or mixture of Gaussians density approximation of the same. The non-rigid transformation is expressed in a B-spline basis to achieve the desired smoothness in the transformation as well as computational efficiency. The key contributions of this work are (i) the use of Riesz transform to achieve better efficiency in computing the local frequency representation in comparison to Gabor filter-based approaches, (ii) new mathematical model for local-frequency based non-rigid registration, (iii) analytic computation of the gradient of the robust non-rigid registration cost function to achieve efficient and accurate registration. The proposed non-rigid L2E-based registration is a significant extension of research reported in literature to date. We present experimental results for registering several real data sets with synthetic and real non-rigid misalignments. PMID:17354721

Improved object optimal synthetic description, modeling, learning, and discrimination by GEOGINE computational kernel

NASA Astrophysics Data System (ADS)

Fiorini, Rodolfo A.; Dacquino, Gianfranco

2005-03-01

GEOGINE (GEOmetrical enGINE), a state-of-the-art OMG (Ontological Model Generator) based on n-D Tensor Invariants for n-Dimensional shape/texture optimal synthetic representation, description and learning, was presented in previous conferences elsewhere recently. Improved computational algorithms based on the computational invariant theory of finite groups in Euclidean space and a demo application is presented. Progressive model automatic generation is discussed. GEOGINE can be used as an efficient computational kernel for fast reliable application development and delivery in advanced biomedical engineering, biometric, intelligent computing, target recognition, content image retrieval, data mining technological areas mainly. Ontology can be regarded as a logical theory accounting for the intended meaning of a formal dictionary, i.e., its ontological commitment to a particular conceptualization of the world object. According to this approach, "n-D Tensor Calculus" can be considered a "Formal Language" to reliably compute optimized "n-Dimensional Tensor Invariants" as specific object "invariant parameter and attribute words" for automated n-Dimensional shape/texture optimal synthetic object description by incremental model generation. The class of those "invariant parameter and attribute words" can be thought as a specific "Formal Vocabulary" learned from a "Generalized Formal Dictionary" of the "Computational Tensor Invariants" language. Even object chromatic attributes can be effectively and reliably computed from object geometric parameters into robust colour shape invariant characteristics. As a matter of fact, any highly sophisticated application needing effective, robust object geometric/colour invariant attribute capture and parameterization features, for reliable automated object learning and discrimination can deeply benefit from GEOGINE progressive automated model generation computational kernel performance. Main operational advantages over previous, similar approaches are: 1) Progressive Automated Invariant Model Generation, 2) Invariant Minimal Complete Description Set for computational efficiency, 3) Arbitrary Model Precision for robust object description and identification.

Robust Smoothing: Smoothing Parameter Selection and Applications to Fluorescence Spectroscopy∂

PubMed Central

Lee, Jong Soo; Cox, Dennis D.

2009-01-01

Fluorescence spectroscopy has emerged in recent years as an effective way to detect cervical cancer. Investigation of the data preprocessing stage uncovered a need for a robust smoothing to extract the signal from the noise. Various robust smoothing methods for estimating fluorescence emission spectra are compared and data driven methods for the selection of smoothing parameter are suggested. The methods currently implemented in R for smoothing parameter selection proved to be unsatisfactory, and a computationally efficient procedure that approximates robust leave-one-out cross validation is presented. PMID:20729976

Robust dynamical decoupling for quantum computing and quantum memory.

PubMed

Souza, Alexandre M; Alvarez, Gonzalo A; Suter, Dieter

2011-06-17

Dynamical decoupling (DD) is a popular technique for protecting qubits from the environment. However, unless special care is taken, experimental errors in the control pulses used in this technique can destroy the quantum information instead of preserving it. Here, we investigate techniques for making DD sequences robust against different types of experimental errors while retaining good decoupling efficiency in a fluctuating environment. We present experimental data from solid-state nuclear spin qubits and introduce a new DD sequence that is suitable for quantum computing and quantum memory.

A Robust and Efficient Method for Steady State Patterns in Reaction-Diffusion Systems

PubMed Central

Lo, Wing-Cheong; Chen, Long; Wang, Ming; Nie, Qing

2012-01-01

An inhomogeneous steady state pattern of nonlinear reaction-diffusion equations with no-flux boundary conditions is usually computed by solving the corresponding time-dependent reaction-diffusion equations using temporal schemes. Nonlinear solvers (e.g., Newton’s method) take less CPU time in direct computation for the steady state; however, their convergence is sensitive to the initial guess, often leading to divergence or convergence to spatially homogeneous solution. Systematically numerical exploration of spatial patterns of reaction-diffusion equations under different parameter regimes requires that the numerical method be efficient and robust to initial condition or initial guess, with better likelihood of convergence to an inhomogeneous pattern. Here, a new approach that combines the advantages of temporal schemes in robustness and Newton’s method in fast convergence in solving steady states of reaction-diffusion equations is proposed. In particular, an adaptive implicit Euler with inexact solver (AIIE) method is found to be much more efficient than temporal schemes and more robust in convergence than typical nonlinear solvers (e.g., Newton’s method) in finding the inhomogeneous pattern. Application of this new approach to two reaction-diffusion equations in one, two, and three spatial dimensions, along with direct comparisons to several other existing methods, demonstrates that AIIE is a more desirable method for searching inhomogeneous spatial patterns of reaction-diffusion equations in a large parameter space. PMID:22773849

Towards a Versatile Tele-Education Platform for Computer Science Educators Based on the Greek School Network

ERIC Educational Resources Information Center

Paraskevas, Michael; Zarouchas, Thomas; Angelopoulos, Panagiotis; Perikos, Isidoros

2013-01-01

Now days the growing need for highly qualified computer science educators in modern educational environments is commonplace. This study examines the potential use of Greek School Network (GSN) to provide a robust and comprehensive e-training course for computer science educators in order to efficiently exploit advanced IT services and establish a…

Computational efficiency for the surface renewal method

NASA Astrophysics Data System (ADS)

Kelley, Jason; Higgins, Chad

2018-04-01

Measuring surface fluxes using the surface renewal (SR) method requires programmatic algorithms for tabulation, algebraic calculation, and data quality control. A number of different methods have been published describing automated calibration of SR parameters. Because the SR method utilizes high-frequency (10 Hz+) measurements, some steps in the flux calculation are computationally expensive, especially when automating SR to perform many iterations of these calculations. Several new algorithms were written that perform the required calculations more efficiently and rapidly, and that tested for sensitivity to length of flux averaging period, ability to measure over a large range of lag timescales, and overall computational efficiency. These algorithms utilize signal processing techniques and algebraic simplifications that demonstrate simple modifications that dramatically improve computational efficiency. The results here complement efforts by other authors to standardize a robust and accurate computational SR method. Increased speed of computation time grants flexibility to implementing the SR method, opening new avenues for SR to be used in research, for applied monitoring, and in novel field deployments.

Seed robustness of oriented relative fuzzy connectedness: core computation and its applications

NASA Astrophysics Data System (ADS)

Tavares, Anderson C. M.; Bejar, Hans H. C.; Miranda, Paulo A. V.

2017-02-01

In this work, we present a formal definition and an efficient algorithm to compute the cores of Oriented Relative Fuzzy Connectedness (ORFC), a recent seed-based segmentation technique. The core is a region where the seed can be moved without altering the segmentation, an important aspect for robust techniques and reduction of user effort. We show how ORFC cores can be used to build a powerful hybrid image segmentation approach. We also provide some new theoretical relations between ORFC and Oriented Image Foresting Transform (OIFT), as well as their cores. Experimental results among several methods show that the hybrid approach conserves high accuracy, avoids the shrinking problem and provides robustness to seed placement inside the desired object due to the cores properties.

Analysis, calculation and utilization of the k-balance attribute in interdependent networks

NASA Astrophysics Data System (ADS)

Liu, Zheng; Li, Qing; Wang, Dan; Xu, Mingwei

2018-05-01

Interdependent networks, where two networks depend on each other, are becoming more and more significant in modern systems. From previous work, it can be concluded that interdependent networks are more vulnerable than a single network. The robustness in interdependent networks deserves special attention. In this paper, we propose a metric of robustness from a new perspective-the balance. First, we define the balance-coefficient of the interdependent system. Based on precise analysis and derivation, we prove some significant theories and provide an efficient algorithm to compute the balance-coefficient. Finally, we propose an optimal solution to reduce the balance-coefficient to enhance the robustness of the given system. Comprehensive experiments confirm the efficiency of our algorithms.

Use of the parameterised finite element method to robustly and efficiently evolve the edge of a moving cell.

PubMed

Neilson, Matthew P; Mackenzie, John A; Webb, Steven D; Insall, Robert H

2010-11-01

In this paper we present a computational tool that enables the simulation of mathematical models of cell migration and chemotaxis on an evolving cell membrane. Recent models require the numerical solution of systems of reaction-diffusion equations on the evolving cell membrane and then the solution state is used to drive the evolution of the cell edge. Previous work involved moving the cell edge using a level set method (LSM). However, the LSM is computationally very expensive, which severely limits the practical usefulness of the algorithm. To address this issue, we have employed the parameterised finite element method (PFEM) as an alternative method for evolving a cell boundary. We show that the PFEM is far more efficient and robust than the LSM. We therefore suggest that the PFEM potentially has an essential role to play in computational modelling efforts towards the understanding of many of the complex issues related to chemotaxis.

Learning to assign binary weights to binary descriptor

NASA Astrophysics Data System (ADS)

Huang, Zhoudi; Wei, Zhenzhong; Zhang, Guangjun

2016-10-01

Constructing robust binary local feature descriptors are receiving increasing interest due to their binary nature, which can enable fast processing while requiring significantly less memory than their floating-point competitors. To bridge the performance gap between the binary and floating-point descriptors without increasing the computational cost of computing and matching, optimal binary weights are learning to assign to binary descriptor for considering each bit might contribute differently to the distinctiveness and robustness. Technically, a large-scale regularized optimization method is applied to learn float weights for each bit of the binary descriptor. Furthermore, binary approximation for the float weights is performed by utilizing an efficient alternatively greedy strategy, which can significantly improve the discriminative power while preserve fast matching advantage. Extensive experimental results on two challenging datasets (Brown dataset and Oxford dataset) demonstrate the effectiveness and efficiency of the proposed method.

Robust and Imperceptible Watermarking of Video Streams for Low Power Devices

NASA Astrophysics Data System (ADS)

Ishtiaq, Muhammad; Jaffar, M. Arfan; Khan, Muhammad A.; Jan, Zahoor; Mirza, Anwar M.

With the advent of internet, every aspect of life is going online. From online working to watching videos, everything is now available on the internet. With the greater business benefits, increased availability and other online business advantages, there is a major challenge of security and ownership of data. Videos downloaded from an online store can easily be shared among non-intended or unauthorized users. Invisible watermarking is used to hide copyright protection information in the videos. The existing methods of watermarking are less robust and imperceptible and also the computational complexity of these methods does not suit low power devices. In this paper, we have proposed a new method to address the problem of robustness and imperceptibility. Experiments have shown that our method has better robustness and imperceptibility as well as our method is computationally efficient than previous approaches in practice. Hence our method can easily be applied on low power devices.

On-Line Robust Modal Stability Prediction using Wavelet Processing

NASA Technical Reports Server (NTRS)

Brenner, Martin J.; Lind, Rick

1998-01-01

Wavelet analysis for filtering and system identification has been used to improve the estimation of aeroservoelastic stability margins. The conservatism of the robust stability margins is reduced with parametric and nonparametric time- frequency analysis of flight data in the model validation process. Nonparametric wavelet processing of data is used to reduce the effects of external disturbances and unmodeled dynamics. Parametric estimates of modal stability are also extracted using the wavelet transform. Computation of robust stability margins for stability boundary prediction depends on uncertainty descriptions derived from the data for model validation. The F-18 High Alpha Research Vehicle aeroservoelastic flight test data demonstrates improved robust stability prediction by extension of the stability boundary beyond the flight regime. Guidelines and computation times are presented to show the efficiency and practical aspects of these procedures for on-line implementation. Feasibility of the method is shown for processing flight data from time- varying nonstationary test points.

A kriging metamodel-assisted robust optimization method based on a reverse model

NASA Astrophysics Data System (ADS)

Zhou, Hui; Zhou, Qi; Liu, Congwei; Zhou, Taotao

2018-02-01

The goal of robust optimization methods is to obtain a solution that is both optimum and relatively insensitive to uncertainty factors. Most existing robust optimization approaches use outer-inner nested optimization structures where a large amount of computational effort is required because the robustness of each candidate solution delivered from the outer level should be evaluated in the inner level. In this article, a kriging metamodel-assisted robust optimization method based on a reverse model (K-RMRO) is first proposed, in which the nested optimization structure is reduced into a single-loop optimization structure to ease the computational burden. Ignoring the interpolation uncertainties from kriging, K-RMRO may yield non-robust optima. Hence, an improved kriging-assisted robust optimization method based on a reverse model (IK-RMRO) is presented to take the interpolation uncertainty of kriging metamodel into consideration. In IK-RMRO, an objective switching criterion is introduced to determine whether the inner level robust optimization or the kriging metamodel replacement should be used to evaluate the robustness of design alternatives. The proposed criterion is developed according to whether or not the robust status of the individual can be changed because of the interpolation uncertainties from the kriging metamodel. Numerical and engineering cases are used to demonstrate the applicability and efficiency of the proposed approach.

Robust linearized image reconstruction for multifrequency EIT of the breast.

PubMed

Boverman, Gregory; Kao, Tzu-Jen; Kulkarni, Rujuta; Kim, Bong Seok; Isaacson, David; Saulnier, Gary J; Newell, Jonathan C

2008-10-01

Electrical impedance tomography (EIT) is a developing imaging modality that is beginning to show promise for detecting and characterizing tumors in the breast. At Rensselaer Polytechnic Institute, we have developed a combined EIT-tomosynthesis system that allows for the coregistered and simultaneous analysis of the breast using EIT and X-ray imaging. A significant challenge in EIT is the design of computationally efficient image reconstruction algorithms which are robust to various forms of model mismatch. Specifically, we have implemented a scaling procedure that is robust to the presence of a thin highly-resistive layer of skin at the boundary of the breast and we have developed an algorithm to detect and exclude from the image reconstruction electrodes that are in poor contact with the breast. In our initial clinical studies, it has been difficult to ensure that all electrodes make adequate contact with the breast, and thus procedures for the use of data sets containing poorly contacting electrodes are particularly important. We also present a novel, efficient method to compute the Jacobian matrix for our linearized image reconstruction algorithm by reducing the computation of the sensitivity for each voxel to a quadratic form. Initial clinical results are presented, showing the potential of our algorithms to detect and localize breast tumors.

Robust computation of dipole electromagnetic fields in arbitrarily anisotropic, planar-stratified environments.

PubMed

Sainath, Kamalesh; Teixeira, Fernando L; Donderici, Burkay

2014-01-01

We develop a general-purpose formulation, based on two-dimensional spectral integrals, for computing electromagnetic fields produced by arbitrarily oriented dipoles in planar-stratified environments, where each layer may exhibit arbitrary and independent anisotropy in both its (complex) permittivity and permeability tensors. Among the salient features of our formulation are (i) computation of eigenmodes (characteristic plane waves) supported in arbitrarily anisotropic media in a numerically robust fashion, (ii) implementation of an hp-adaptive refinement for the numerical integration to evaluate the radiation and weakly evanescent spectra contributions, and (iii) development of an adaptive extension of an integral convergence acceleration technique to compute the strongly evanescent spectrum contribution. While other semianalytic techniques exist to solve this problem, none have full applicability to media exhibiting arbitrary double anisotropies in each layer, where one must account for the whole range of possible phenomena (e.g., mode coupling at interfaces and nonreciprocal mode propagation). Brute-force numerical methods can tackle this problem but only at a much higher computational cost. The present formulation provides an efficient and robust technique for field computation in arbitrary planar-stratified environments. We demonstrate the formulation for a number of problems related to geophysical exploration.

Robust and efficient anomaly detection using heterogeneous representations

NASA Astrophysics Data System (ADS)

Hu, Xing; Hu, Shiqiang; Xie, Jinhua; Zheng, Shiyou

2015-05-01

Various approaches have been proposed for video anomaly detection. Yet these approaches typically suffer from one or more limitations: they often characterize the pattern using its internal information, but ignore its external relationship which is important for local anomaly detection. Moreover, the high-dimensionality and the lack of robustness of pattern representation may lead to problems, including overfitting, increased computational cost and memory requirements, and high false alarm rate. We propose a video anomaly detection framework which relies on a heterogeneous representation to account for both the pattern's internal information and external relationship. The internal information is characterized by slow features learned by slow feature analysis from low-level representations, and the external relationship is characterized by the spatial contextual distances. The heterogeneous representation is compact, robust, efficient, and discriminative for anomaly detection. Moreover, both the pattern's internal information and external relationship can be taken into account in the proposed framework. Extensive experiments demonstrate the robustness and efficiency of our approach by comparison with the state-of-the-art approaches on the widely used benchmark datasets.

OGS#PETSc approach for robust and efficient simulations of strongly coupled hydrothermal processes in EGS reservoirs

NASA Astrophysics Data System (ADS)

Watanabe, Norihiro; Blucher, Guido; Cacace, Mauro; Kolditz, Olaf

2016-04-01

A robust and computationally efficient solution is important for 3D modelling of EGS reservoirs. This is particularly the case when the reservoir model includes hydraulic conduits such as induced or natural fractures, fault zones, and wellbore open-hole sections. The existence of such hydraulic conduits results in heterogeneous flow fields and in a strengthened coupling between fluid flow and heat transport processes via temperature dependent fluid properties (e.g. density and viscosity). A commonly employed partitioned solution (or operator-splitting solution) may not robustly work for such strongly coupled problems its applicability being limited by small time step sizes (e.g. 5-10 days) whereas the processes have to be simulated for 10-100 years. To overcome this limitation, an alternative approach is desired which can guarantee a robust solution of the coupled problem with minor constraints on time step sizes. In this work, we present a Newton-Raphson based monolithic coupling approach implemented in the OpenGeoSys simulator (OGS) combined with the Portable, Extensible Toolkit for Scientific Computation (PETSc) library. The PETSc library is used for both linear and nonlinear solvers as well as MPI-based parallel computations. The suggested method has been tested by application to the 3D reservoir site of Groß Schönebeck, in northern Germany. Results show that the exact Newton-Raphson approach can also be limited to small time step sizes (e.g. one day) due to slight oscillations in the temperature field. The usage of a line search technique and modification of the Jacobian matrix were necessary to achieve robust convergence of the nonlinear solution. For the studied example, the proposed monolithic approach worked even with a very large time step size of 3.5 years.

Low cost and efficient kurtosis-based deflationary ICA method: application to MRS sources separation problem.

PubMed

Saleh, M; Karfoul, A; Kachenoura, A; Senhadji, L; Albera, L

2016-08-01

Improving the execution time and the numerical complexity of the well-known kurtosis-based maximization method, the RobustICA, is investigated in this paper. A Newton-based scheme is proposed and compared to the conventional RobustICA method. A new implementation using the nonlinear Conjugate Gradient one is investigated also. Regarding the Newton approach, an exact computation of the Hessian of the considered cost function is provided. The proposed approaches and the considered implementations inherit the global plane search of the initial RobustICA method for which a better convergence speed for a given direction is still guaranteed. Numerical results on Magnetic Resonance Spectroscopy (MRS) source separation show the efficiency of the proposed approaches notably the quasi-Newton one using the BFGS method.

Engineering the quantum states of light in a Kerr-nonlinear resonator by two-photon driving

NASA Astrophysics Data System (ADS)

Puri, Shruti; Boutin, Samuel; Blais, Alexandre

2017-04-01

Photonic cat states stored in high-Q resonators show great promise for hardware efficient universal quantum computing. We propose an approach to efficiently prepare such cat states in a Kerr-nonlinear resonator by the use of a two-photon drive. Significantly, we show that this preparation is robust against single-photon loss. An outcome of this observation is that a two-photon drive can eliminate undesirable phase evolution induced by a Kerr nonlinearity. By exploiting the concept of transitionless quantum driving, we moreover demonstrate how non-adiabatic initialization of cat states is possible. Finally, we present a universal set of quantum logical gates that can be performed on the engineered eigenspace of such a two-photon driven resonator and discuss a possible realization using superconducting circuits. The robustness of the engineered subspace to higher-order circuit nonlinearities makes this implementation favorable for scalable quantum computation.

Exploiting Self-organization in Bioengineered Systems: A Computational Approach.

PubMed

Davis, Delin; Doloman, Anna; Podgorski, Gregory J; Vargis, Elizabeth; Flann, Nicholas S

2017-01-01

The productivity of bioengineered cell factories is limited by inefficiencies in nutrient delivery and waste and product removal. Current solution approaches explore changes in the physical configurations of the bioreactors. This work investigates the possibilities of exploiting self-organizing vascular networks to support producer cells within the factory. A computational model simulates de novo vascular development of endothelial-like cells and the resultant network functioning to deliver nutrients and extract product and waste from the cell culture. Microbial factories with vascular networks are evaluated for their scalability, robustness, and productivity compared to the cell factories without a vascular network. Initial studies demonstrate that at least an order of magnitude increase in production is possible, the system can be scaled up, and the self-organization of an efficient vascular network is robust. The work suggests that bioengineered multicellularity may offer efficiency improvements difficult to achieve with physical engineering approaches.

Computational Aerothermodynamics in Aeroassist Applications

NASA Technical Reports Server (NTRS)

Gnoffo, Peter A.

2001-01-01

Aeroassisted planetary entry uses atmospheric drag to decelerate spacecraft from super-orbital to orbital or suborbital velocities. Numerical simulation of flow fields surrounding these spacecraft during hypersonic atmospheric entry is required to define aerothermal loads. The severe compression in the shock layer in front of the vehicle and subsequent, rapid expansion into the wake are characterized by high temperature, thermo-chemical nonequilibrium processes. Implicit algorithms required for efficient, stable computation of the governing equations involving disparate time scales of convection, diffusion, chemical reactions, and thermal relaxation are discussed. Robust point-implicit strategies are utilized in the initialization phase; less robust but more efficient line-implicit strategies are applied in the endgame. Applications to ballutes (balloon-like decelerators) in the atmospheres of Venus, Mars, Titan, Saturn, and Neptune and a Mars Sample Return Orbiter (MSRO) are featured. Examples are discussed where time-accurate simulation is required to achieve a steady-state solution.

Autonomous facial recognition system inspired by human visual system based logarithmical image visualization technique

NASA Astrophysics Data System (ADS)

Wan, Qianwen; Panetta, Karen; Agaian, Sos

2017-05-01

Autonomous facial recognition system is widely used in real-life applications, such as homeland border security, law enforcement identification and authentication, and video-based surveillance analysis. Issues like low image quality, non-uniform illumination as well as variations in poses and facial expressions can impair the performance of recognition systems. To address the non-uniform illumination challenge, we present a novel robust autonomous facial recognition system inspired by the human visual system based, so called, logarithmical image visualization technique. In this paper, the proposed method, for the first time, utilizes the logarithmical image visualization technique coupled with the local binary pattern to perform discriminative feature extraction for facial recognition system. The Yale database, the Yale-B database and the ATT database are used for computer simulation accuracy and efficiency testing. The extensive computer simulation demonstrates the method's efficiency, accuracy, and robustness of illumination invariance for facial recognition.

Multigrid Computations of 3-D Incompressible Internal and External Viscous Rotating Flows

NASA Technical Reports Server (NTRS)

Sheng, Chunhua; Taylor, Lafayette K.; Chen, Jen-Ping; Jiang, Min-Yee; Whitfield, David L.

1996-01-01

This report presents multigrid methods for solving the 3-D incompressible viscous rotating flows in a NASA low-speed centrifugal compressor and a marine propeller 4119. Numerical formulations are given in both the rotating reference frame and the absolute frame. Comparisons are made for the accuracy, efficiency, and robustness between the steady-state scheme and the time-accurate scheme for simulating viscous rotating flows for complex internal and external flow applications. Prospects for further increase in efficiency and accuracy of unsteady time-accurate computations are discussed.

Possibility-based robust design optimization for the structural-acoustic system with fuzzy parameters

NASA Astrophysics Data System (ADS)

Yin, Hui; Yu, Dejie; Yin, Shengwen; Xia, Baizhan

2018-03-01

The conventional engineering optimization problems considering uncertainties are based on the probabilistic model. However, the probabilistic model may be unavailable because of the lack of sufficient objective information to construct the precise probability distribution of uncertainties. This paper proposes a possibility-based robust design optimization (PBRDO) framework for the uncertain structural-acoustic system based on the fuzzy set model, which can be constructed by expert opinions. The objective of robust design is to optimize the expectation and variability of system performance with respect to uncertainties simultaneously. In the proposed PBRDO, the entropy of the fuzzy system response is used as the variability index; the weighted sum of the entropy and expectation of the fuzzy response is used as the objective function, and the constraints are established in the possibility context. The computations for the constraints and objective function of PBRDO are a triple-loop and a double-loop nested problem, respectively, whose computational costs are considerable. To improve the computational efficiency, the target performance approach is introduced to transform the calculation of the constraints into a double-loop nested problem. To further improve the computational efficiency, a Chebyshev fuzzy method (CFM) based on the Chebyshev polynomials is proposed to estimate the objective function, and the Chebyshev interval method (CIM) is introduced to estimate the constraints, thereby the optimization problem is transformed into a single-loop one. Numerical results on a shell structural-acoustic system verify the effectiveness and feasibility of the proposed methods.

Securing SIFT: Privacy-preserving Outsourcing Computation of Feature Extractions Over Encrypted Image Data.

PubMed

Hu, Shengshan; Wang, Qian; Wang, Jingjun; Qin, Zhan; Ren, Kui

2016-05-13

Advances in cloud computing have greatly motivated data owners to outsource their huge amount of personal multimedia data and/or computationally expensive tasks onto the cloud by leveraging its abundant resources for cost saving and flexibility. Despite the tremendous benefits, the outsourced multimedia data and its originated applications may reveal the data owner's private information, such as the personal identity, locations or even financial profiles. This observation has recently aroused new research interest on privacy-preserving computations over outsourced multimedia data. In this paper, we propose an effective and practical privacy-preserving computation outsourcing protocol for the prevailing scale-invariant feature transform (SIFT) over massive encrypted image data. We first show that previous solutions to this problem have either efficiency/security or practicality issues, and none can well preserve the important characteristics of the original SIFT in terms of distinctiveness and robustness. We then present a new scheme design that achieves efficiency and security requirements simultaneously with the preservation of its key characteristics, by randomly splitting the original image data, designing two novel efficient protocols for secure multiplication and comparison, and carefully distributing the feature extraction computations onto two independent cloud servers. We both carefully analyze and extensively evaluate the security and effectiveness of our design. The results show that our solution is practically secure, outperforms the state-of-theart, and performs comparably to the original SIFT in terms of various characteristics, including rotation invariance, image scale invariance, robust matching across affine distortion, addition of noise and change in 3D viewpoint and illumination.

SecSIFT: Privacy-preserving Outsourcing Computation of Feature Extractions Over Encrypted Image Data.

PubMed

Hu, Shengshan; Wang, Qian; Wang, Jingjun; Qin, Zhan; Ren, Kui

2016-05-13

Advances in cloud computing have greatly motivated data owners to outsource their huge amount of personal multimedia data and/or computationally expensive tasks onto the cloud by leveraging its abundant resources for cost saving and flexibility. Despite the tremendous benefits, the outsourced multimedia data and its originated applications may reveal the data owner's private information, such as the personal identity, locations or even financial profiles. This observation has recently aroused new research interest on privacy-preserving computations over outsourced multimedia data. In this paper, we propose an effective and practical privacy-preserving computation outsourcing protocol for the prevailing scale-invariant feature transform (SIFT) over massive encrypted image data. We first show that previous solutions to this problem have either efficiency/security or practicality issues, and none can well preserve the important characteristics of the original SIFT in terms of distinctiveness and robustness. We then present a new scheme design that achieves efficiency and security requirements simultaneously with the preservation of its key characteristics, by randomly splitting the original image data, designing two novel efficient protocols for secure multiplication and comparison, and carefully distributing the feature extraction computations onto two independent cloud servers. We both carefully analyze and extensively evaluate the security and effectiveness of our design. The results show that our solution is practically secure, outperforms the state-of-theart, and performs comparably to the original SIFT in terms of various characteristics, including rotation invariance, image scale invariance, robust matching across affine distortion, addition of noise and change in 3D viewpoint and illumination.

Investigation of progressive failure robustness and alternate load paths for damage tolerant structures

NASA Astrophysics Data System (ADS)

Marhadi, Kun Saptohartyadi

Structural optimization for damage tolerance under various unforeseen damage scenarios is computationally challenging. It couples non-linear progressive failure analysis with sampling-based stochastic analysis of random damage. The goal of this research was to understand the relationship between alternate load paths available in a structure and its damage tolerance, and to use this information to develop computationally efficient methods for designing damage tolerant structures. Progressive failure of a redundant truss structure subjected to small random variability was investigated to identify features that correlate with robustness and predictability of the structure's progressive failure. The identified features were used to develop numerical surrogate measures that permit computationally efficient deterministic optimization to achieve robustness and predictability of progressive failure. Analysis of damage tolerance on designs with robust progressive failure indicated that robustness and predictability of progressive failure do not guarantee damage tolerance. Damage tolerance requires a structure to redistribute its load to alternate load paths. In order to investigate the load distribution characteristics that lead to damage tolerance in structures, designs with varying degrees of damage tolerance were generated using brute force stochastic optimization. A method based on principal component analysis was used to describe load distributions (alternate load paths) in the structures. Results indicate that a structure that can develop alternate paths is not necessarily damage tolerant. The alternate load paths must have a required minimum load capability. Robustness analysis of damage tolerant optimum designs indicates that designs are tailored to specified damage. A design Optimized under one damage specification can be sensitive to other damages not considered. Effectiveness of existing load path definitions and characterizations were investigated for continuum structures. A load path definition using a relative compliance change measure (U* field) was demonstrated to be the most useful measure of load path. This measure provides quantitative information on load path trajectories and qualitative information on the effectiveness of the load path. The use of the U* description of load paths in optimizing structures for effective load paths was investigated.

Efficient Computation of Info-Gap Robustness for Finite Element Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stull, Christopher J.; Hemez, Francois M.; Williams, Brian J.

2012-07-05

A recent research effort at LANL proposed info-gap decision theory as a framework by which to measure the predictive maturity of numerical models. Info-gap theory explores the trade-offs between accuracy, that is, the extent to which predictions reproduce the physical measurements, and robustness, that is, the extent to which predictions are insensitive to modeling assumptions. Both accuracy and robustness are necessary to demonstrate predictive maturity. However, conducting an info-gap analysis can present a formidable challenge, from the standpoint of the required computational resources. This is because a robustness function requires the resolution of multiple optimization problems. This report offers anmore » alternative, adjoint methodology to assess the info-gap robustness of Ax = b-like numerical models solved for a solution x. Two situations that can arise in structural analysis and design are briefly described and contextualized within the info-gap decision theory framework. The treatments of the info-gap problems, using the adjoint methodology are outlined in detail, and the latter problem is solved for four separate finite element models. As compared to statistical sampling, the proposed methodology offers highly accurate approximations of info-gap robustness functions for the finite element models considered in the report, at a small fraction of the computational cost. It is noted that this report considers only linear systems; a natural follow-on study would extend the methodologies described herein to include nonlinear systems.« less

Deterministic and robust generation of single photons from a single quantum dot with 99.5% indistinguishability using adiabatic rapid passage.

PubMed

Wei, Yu-Jia; He, Yu-Ming; Chen, Ming-Cheng; Hu, Yi-Nan; He, Yu; Wu, Dian; Schneider, Christian; Kamp, Martin; Höfling, Sven; Lu, Chao-Yang; Pan, Jian-Wei

2014-11-12

Single photons are attractive candidates of quantum bits (qubits) for quantum computation and are the best messengers in quantum networks. Future scalable, fault-tolerant photonic quantum technologies demand both stringently high levels of photon indistinguishability and generation efficiency. Here, we demonstrate deterministic and robust generation of pulsed resonance fluorescence single photons from a single semiconductor quantum dot using adiabatic rapid passage, a method robust against fluctuation of driving pulse area and dipole moments of solid-state emitters. The emitted photons are background-free, have a vanishing two-photon emission probability of 0.3% and a raw (corrected) two-photon Hong-Ou-Mandel interference visibility of 97.9% (99.5%), reaching a precision that places single photons at the threshold for fault-tolerant surface-code quantum computing. This single-photon source can be readily scaled up to multiphoton entanglement and used for quantum metrology, boson sampling, and linear optical quantum computing.

An Assessment of Artificial Compressibility and Pressure Projection Methods for Incompressible Flow Simulations

NASA Technical Reports Server (NTRS)

Kwak, Dochan; Kiris, C.; Smith, Charles A. (Technical Monitor)

1998-01-01

Performance of the two commonly used numerical procedures, one based on artificial compressibility method and the other pressure projection method, are compared. These formulations are selected primarily because they are designed for three-dimensional applications. The computational procedures are compared by obtaining steady state solutions of a wake vortex and unsteady solutions of a curved duct flow. For steady computations, artificial compressibility was very efficient in terms of computing time and robustness. For an unsteady flow which requires small physical time step, pressure projection method was found to be computationally more efficient than an artificial compressibility method. This comparison is intended to give some basis for selecting a method or a flow solution code for large three-dimensional applications where computing resources become a critical issue.

Towards a more efficient and robust representation of subsurface hydrological processes in Earth System Models

NASA Astrophysics Data System (ADS)

Rosolem, R.; Rahman, M.; Kollet, S. J.; Wagener, T.

2017-12-01

Understanding the impacts of land cover and climate changes on terrestrial hydrometeorology is important across a range of spatial and temporal scales. Earth System Models (ESMs) provide a robust platform for evaluating these impacts. However, current ESMs lack the representation of key hydrological processes (e.g., preferential water flow, and direct interactions with aquifers) in general. The typical "free drainage" conceptualization of land models can misrepresent the magnitude of those interactions, consequently affecting the exchange of energy and water at the surface as well as estimates of groundwater recharge. Recent studies show the benefits of explicitly simulating the interactions between subsurface and surface processes in similar models. However, such parameterizations are often computationally demanding resulting in limited application for large/global-scale studies. Here, we take a different approach in developing a novel parameterization for groundwater dynamics. Instead of directly adding another complex process to an established land model, we examine a set of comprehensive experimental scenarios using a very robust and establish three-dimensional hydrological model to develop a simpler parameterization that represents the aquifer to land surface interactions. The main goal of our developed parameterization is to simultaneously maximize the computational gain (i.e., "efficiency") while minimizing simulation errors in comparison to the full 3D model (i.e., "robustness") to allow for easy implementation in ESMs globally. Our study focuses primarily on understanding both the dynamics for groundwater recharge and discharge, respectively. Preliminary results show that our proposed approach significantly reduced the computational demand while model deviations from the full 3D model are considered to be small for these processes.

Robust, Optimal Water Infrastructure Planning Under Deep Uncertainty Using Metamodels

NASA Astrophysics Data System (ADS)

Maier, H. R.; Beh, E. H. Y.; Zheng, F.; Dandy, G. C.; Kapelan, Z.

2015-12-01

Optimal long-term planning plays an important role in many water infrastructure problems. However, this task is complicated by deep uncertainty about future conditions, such as the impact of population dynamics and climate change. One way to deal with this uncertainty is by means of robustness, which aims to ensure that water infrastructure performs adequately under a range of plausible future conditions. However, as robustness calculations require computationally expensive system models to be run for a large number of scenarios, it is generally computationally intractable to include robustness as an objective in the development of optimal long-term infrastructure plans. In order to overcome this shortcoming, an approach is developed that uses metamodels instead of computationally expensive simulation models in robustness calculations. The approach is demonstrated for the optimal sequencing of water supply augmentation options for the southern portion of the water supply for Adelaide, South Australia. A 100-year planning horizon is subdivided into ten equal decision stages for the purpose of sequencing various water supply augmentation options, including desalination, stormwater harvesting and household rainwater tanks. The objectives include the minimization of average present value of supply augmentation costs, the minimization of average present value of greenhouse gas emissions and the maximization of supply robustness. The uncertain variables are rainfall, per capita water consumption and population. Decision variables are the implementation stages of the different water supply augmentation options. Artificial neural networks are used as metamodels to enable all objectives to be calculated in a computationally efficient manner at each of the decision stages. The results illustrate the importance of identifying optimal staged solutions to ensure robustness and sustainability of water supply into an uncertain long-term future.

Robust image matching via ORB feature and VFC for mismatch removal

NASA Astrophysics Data System (ADS)

Ma, Tao; Fu, Wenxing; Fang, Bin; Hu, Fangyu; Quan, Siwen; Ma, Jie

2018-03-01

Image matching is at the base of many image processing and computer vision problems, such as object recognition or structure from motion. Current methods rely on good feature descriptors and mismatch removal strategies for detection and matching. In this paper, we proposed a robust image match approach based on ORB feature and VFC for mismatch removal. ORB (Oriented FAST and Rotated BRIEF) is an outstanding feature, it has the same performance as SIFT with lower cost. VFC (Vector Field Consensus) is a state-of-the-art mismatch removing method. The experiment results demonstrate that our method is efficient and robust.

An adaptive proper orthogonal decomposition method for model order reduction of multi-disc rotor system

NASA Astrophysics Data System (ADS)

Jin, Yulin; Lu, Kuan; Hou, Lei; Chen, Yushu

2017-12-01

The proper orthogonal decomposition (POD) method is a main and efficient tool for order reduction of high-dimensional complex systems in many research fields. However, the robustness problem of this method is always unsolved, although there are some modified POD methods which were proposed to solve this problem. In this paper, a new adaptive POD method called the interpolation Grassmann manifold (IGM) method is proposed to address the weakness of local property of the interpolation tangent-space of Grassmann manifold (ITGM) method in a wider parametric region. This method is demonstrated here by a nonlinear rotor system of 33-degrees of freedom (DOFs) with a pair of liquid-film bearings and a pedestal looseness fault. The motion region of the rotor system is divided into two parts: simple motion region and complex motion region. The adaptive POD method is compared with the ITGM method for the large and small spans of parameter in the two parametric regions to present the advantage of this method and disadvantage of the ITGM method. The comparisons of the responses are applied to verify the accuracy and robustness of the adaptive POD method, as well as the computational efficiency is also analyzed. As a result, the new adaptive POD method has a strong robustness and high computational efficiency and accuracy in a wide scope of parameter.

Continuum topology optimization considering uncertainties in load locations based on the cloud model

NASA Astrophysics Data System (ADS)

Liu, Jie; Wen, Guilin

2018-06-01

Few researchers have paid attention to designing structures in consideration of uncertainties in the loading locations, which may significantly influence the structural performance. In this work, cloud models are employed to depict the uncertainties in the loading locations. A robust algorithm is developed in the context of minimizing the expectation of the structural compliance, while conforming to a material volume constraint. To guarantee optimal solutions, sufficient cloud drops are used, which in turn leads to low efficiency. An innovative strategy is then implemented to enormously improve the computational efficiency. A modified soft-kill bi-directional evolutionary structural optimization method using derived sensitivity numbers is used to output the robust novel configurations. Several numerical examples are presented to demonstrate the effectiveness and efficiency of the proposed algorithm.

Evolution of a designless nanoparticle network into reconfigurable Boolean logic

NASA Astrophysics Data System (ADS)

Bose, S. K.; Lawrence, C. P.; Liu, Z.; Makarenko, K. S.; van Damme, R. M. J.; Broersma, H. J.; van der Wiel, W. G.

2015-12-01

Natural computers exploit the emergent properties and massive parallelism of interconnected networks of locally active components. Evolution has resulted in systems that compute quickly and that use energy efficiently, utilizing whatever physical properties are exploitable. Man-made computers, on the other hand, are based on circuits of functional units that follow given design rules. Hence, potentially exploitable physical processes, such as capacitive crosstalk, to solve a problem are left out. Until now, designless nanoscale networks of inanimate matter that exhibit robust computational functionality had not been realized. Here we artificially evolve the electrical properties of a disordered nanomaterials system (by optimizing the values of control voltages using a genetic algorithm) to perform computational tasks reconfigurably. We exploit the rich behaviour that emerges from interconnected metal nanoparticles, which act as strongly nonlinear single-electron transistors, and find that this nanoscale architecture can be configured in situ into any Boolean logic gate. This universal, reconfigurable gate would require about ten transistors in a conventional circuit. Our system meets the criteria for the physical realization of (cellular) neural networks: universality (arbitrary Boolean functions), compactness, robustness and evolvability, which implies scalability to perform more advanced tasks. Our evolutionary approach works around device-to-device variations and the accompanying uncertainties in performance. Moreover, it bears a great potential for more energy-efficient computation, and for solving problems that are very hard to tackle in conventional architectures.

Characterization of Meta-Materials Using Computational Electromagnetic Methods

NASA Technical Reports Server (NTRS)

Deshpande, Manohar; Shin, Joon

2005-01-01

An efficient and powerful computational method is presented to synthesize a meta-material to specified electromagnetic properties. Using the periodicity of meta-materials, the Finite Element Methodology (FEM) is developed to estimate the reflection and transmission through the meta-material structure for a normal plane wave incidence. For efficient computations of the reflection and transmission over a wide band frequency range through a meta-material a Finite Difference Time Domain (FDTD) approach is also developed. Using the Nicholson-Ross method and the Genetic Algorithms, a robust procedure to extract electromagnetic properties of meta-material from the knowledge of its reflection and transmission coefficients is described. Few numerical examples are also presented to validate the present approach.

Robust stability of linear systems: Some computational considerations

NASA Technical Reports Server (NTRS)

Laub, A. J.

1979-01-01

The cases of both additive and multiplicative perturbations were discussed and a number of relationships between the two cases were given. A number of computational aspects of the theory were also discussed, including a proposed new method for evaluating general transfer or frequency response matrices. The new method is numerically stable and efficient, requiring only operations to update for new values of the frequency parameter.

Computing NLTE Opacities -- Node Level Parallel Calculation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Holladay, Daniel

Presentation. The goal: to produce a robust library capable of computing reasonably accurate opacities inline with the assumption of LTE relaxed (non-LTE). Near term: demonstrate acceleration of non-LTE opacity computation. Far term (if funded): connect to application codes with in-line capability and compute opacities. Study science problems. Use efficient algorithms that expose many levels of parallelism and utilize good memory access patterns for use on advanced architectures. Portability to multiple types of hardware including multicore processors, manycore processors such as KNL, GPUs, etc. Easily coupled to radiation hydrodynamics and thermal radiative transfer codes.

Change Detection of Mobile LIDAR Data Using Cloud Computing

NASA Astrophysics Data System (ADS)

Liu, Kun; Boehm, Jan; Alis, Christian

2016-06-01

Change detection has long been a challenging problem although a lot of research has been conducted in different fields such as remote sensing and photogrammetry, computer vision, and robotics. In this paper, we blend voxel grid and Apache Spark together to propose an efficient method to address the problem in the context of big data. Voxel grid is a regular geometry representation consisting of the voxels with the same size, which fairly suites parallel computation. Apache Spark is a popular distributed parallel computing platform which allows fault tolerance and memory cache. These features can significantly enhance the performance of Apache Spark and results in an efficient and robust implementation. In our experiments, both synthetic and real point cloud data are employed to demonstrate the quality of our method.

Efficient and robust computation of PDF features from diffusion MR signal.

PubMed

Assemlal, Haz-Edine; Tschumperlé, David; Brun, Luc

2009-10-01

We present a method for the estimation of various features of the tissue micro-architecture using the diffusion magnetic resonance imaging. The considered features are designed from the displacement probability density function (PDF). The estimation is based on two steps: first the approximation of the signal by a series expansion made of Gaussian-Laguerre and Spherical Harmonics functions; followed by a projection on a finite dimensional space. Besides, we propose to tackle the problem of the robustness to Rician noise corrupting in-vivo acquisitions. Our feature estimation is expressed as a variational minimization process leading to a variational framework which is robust to noise. This approach is very flexible regarding the number of samples and enables the computation of a large set of various features of the local tissues structure. We demonstrate the effectiveness of the method with results on both synthetic phantom and real MR datasets acquired in a clinical time-frame.

Hierarchical layered and semantic-based image segmentation using ergodicity map

NASA Astrophysics Data System (ADS)

Yadegar, Jacob; Liu, Xiaoqing

2010-04-01

Image segmentation plays a foundational role in image understanding and computer vision. Although great strides have been made and progress achieved on automatic/semi-automatic image segmentation algorithms, designing a generic, robust, and efficient image segmentation algorithm is still challenging. Human vision is still far superior compared to computer vision, especially in interpreting semantic meanings/objects in images. We present a hierarchical/layered semantic image segmentation algorithm that can automatically and efficiently segment images into hierarchical layered/multi-scaled semantic regions/objects with contextual topological relationships. The proposed algorithm bridges the gap between high-level semantics and low-level visual features/cues (such as color, intensity, edge, etc.) through utilizing a layered/hierarchical ergodicity map, where ergodicity is computed based on a space filling fractal concept and used as a region dissimilarity measurement. The algorithm applies a highly scalable, efficient, and adaptive Peano- Cesaro triangulation/tiling technique to decompose the given image into a set of similar/homogenous regions based on low-level visual cues in a top-down manner. The layered/hierarchical ergodicity map is built through a bottom-up region dissimilarity analysis. The recursive fractal sweep associated with the Peano-Cesaro triangulation provides efficient local multi-resolution refinement to any level of detail. The generated binary decomposition tree also provides efficient neighbor retrieval mechanisms for contextual topological object/region relationship generation. Experiments have been conducted within the maritime image environment where the segmented layered semantic objects include the basic level objects (i.e. sky/land/water) and deeper level objects in the sky/land/water surfaces. Experimental results demonstrate the proposed algorithm has the capability to robustly and efficiently segment images into layered semantic objects/regions with contextual topological relationships.

A novel iris localization algorithm using correlation filtering

NASA Astrophysics Data System (ADS)

Pohit, Mausumi; Sharma, Jitu

2015-06-01

Fast and efficient segmentation of iris from the eye images is a primary requirement for robust database independent iris recognition. In this paper we have presented a new algorithm for computing the inner and outer boundaries of the iris and locating the pupil centre. Pupil-iris boundary computation is based on correlation filtering approach, whereas iris-sclera boundary is determined through one dimensional intensity mapping. The proposed approach is computationally less extensive when compared with the existing algorithms like Hough transform.

omniClassifier: a Desktop Grid Computing System for Big Data Prediction Modeling

PubMed Central

Phan, John H.; Kothari, Sonal; Wang, May D.

2016-01-01

Robust prediction models are important for numerous science, engineering, and biomedical applications. However, best-practice procedures for optimizing prediction models can be computationally complex, especially when choosing models from among hundreds or thousands of parameter choices. Computational complexity has further increased with the growth of data in these fields, concurrent with the era of “Big Data”. Grid computing is a potential solution to the computational challenges of Big Data. Desktop grid computing, which uses idle CPU cycles of commodity desktop machines, coupled with commercial cloud computing resources can enable research labs to gain easier and more cost effective access to vast computing resources. We have developed omniClassifier, a multi-purpose prediction modeling application that provides researchers with a tool for conducting machine learning research within the guidelines of recommended best-practices. omniClassifier is implemented as a desktop grid computing system using the Berkeley Open Infrastructure for Network Computing (BOINC) middleware. In addition to describing implementation details, we use various gene expression datasets to demonstrate the potential scalability of omniClassifier for efficient and robust Big Data prediction modeling. A prototype of omniClassifier can be accessed at http://omniclassifier.bme.gatech.edu/. PMID:27532062

Effective dimensional reduction algorithm for eigenvalue problems for thin elastic structures: A paradigm in three dimensions

PubMed Central

Ovtchinnikov, Evgueni E.; Xanthis, Leonidas S.

2000-01-01

We present a methodology for the efficient numerical solution of eigenvalue problems of full three-dimensional elasticity for thin elastic structures, such as shells, plates and rods of arbitrary geometry, discretized by the finite element method. Such problems are solved by iterative methods, which, however, are known to suffer from slow convergence or even convergence failure, when the thickness is small. In this paper we show an effective way of resolving this difficulty by invoking a special preconditioning technique associated with the effective dimensional reduction algorithm (EDRA). As an example, we present an algorithm for computing the minimal eigenvalue of a thin elastic plate and we show both theoretically and numerically that it is robust with respect to both the thickness and discretization parameters, i.e. the convergence does not deteriorate with diminishing thickness or mesh refinement. This robustness is sine qua non for the efficient computation of large-scale eigenvalue problems for thin elastic structures. PMID:10655469

A Taylor Expansion-Based Adaptive Design Strategy for Global Surrogate Modeling With Applications in Groundwater Modeling

NASA Astrophysics Data System (ADS)

Mo, Shaoxing; Lu, Dan; Shi, Xiaoqing; Zhang, Guannan; Ye, Ming; Wu, Jianfeng; Wu, Jichun

2017-12-01

Global sensitivity analysis (GSA) and uncertainty quantification (UQ) for groundwater modeling are challenging because of the model complexity and significant computational requirements. To reduce the massive computational cost, a cheap-to-evaluate surrogate model is usually constructed to approximate and replace the expensive groundwater models in the GSA and UQ. Constructing an accurate surrogate requires actual model simulations on a number of parameter samples. Thus, a robust experimental design strategy is desired to locate informative samples so as to reduce the computational cost in surrogate construction and consequently to improve the efficiency in the GSA and UQ. In this study, we develop a Taylor expansion-based adaptive design (TEAD) that aims to build an accurate global surrogate model with a small training sample size. TEAD defines a novel hybrid score function to search informative samples, and a robust stopping criterion to terminate the sample search that guarantees the resulted approximation errors satisfy the desired accuracy. The good performance of TEAD in building global surrogate models is demonstrated in seven analytical functions with different dimensionality and complexity in comparison to two widely used experimental design methods. The application of the TEAD-based surrogate method in two groundwater models shows that the TEAD design can effectively improve the computational efficiency of GSA and UQ for groundwater modeling.

A robust high-order lattice adaptive notch filter and its application to narrowband noise cancellation

NASA Astrophysics Data System (ADS)

Kim, Seong-woo; Park, Young-cheol; Seo, Young-soo; Youn, Dae Hee

2014-12-01

In this paper, we propose a high-order lattice adaptive notch filter (LANF) that can robustly track multiple sinusoids. Unlike the conventional cascade structure, the proposed high-order LANF has robust tracking characteristics regardless of the frequencies of reference sinusoids and initial notch frequencies. The proposed high-order LANF is applied to a narrowband adaptive noise cancellation (ANC) to mitigate the effect of the broadband disturbance in the reference signal. By utilizing the gradient adaptive lattice (GAL) ANC algorithm and approximately combining it with the proposed high-order LANF, a computationally efficient narrowband ANC system is obtained. Experimental results demonstrate the robustness of the proposed high-order LANF and the effectiveness of the obtained narrowband ANC system.

On the impact of approximate computation in an analog DeSTIN architecture.

PubMed

Young, Steven; Lu, Junjie; Holleman, Jeremy; Arel, Itamar

2014-05-01

Deep machine learning (DML) holds the potential to revolutionize machine learning by automating rich feature extraction, which has become the primary bottleneck of human engineering in pattern recognition systems. However, the heavy computational burden renders DML systems implemented on conventional digital processors impractical for large-scale problems. The highly parallel computations required to implement large-scale deep learning systems are well suited to custom hardware. Analog computation has demonstrated power efficiency advantages of multiple orders of magnitude relative to digital systems while performing nonideal computations. In this paper, we investigate typical error sources introduced by analog computational elements and their impact on system-level performance in DeSTIN--a compositional deep learning architecture. These inaccuracies are evaluated on a pattern classification benchmark, clearly demonstrating the robustness of the underlying algorithm to the errors introduced by analog computational elements. A clear understanding of the impacts of nonideal computations is necessary to fully exploit the efficiency of analog circuits.

Fast and robust generation of feature maps for region-based visual attention.

PubMed

Aziz, Muhammad Zaheer; Mertsching, Bärbel

2008-05-01

Visual attention is one of the important phenomena in biological vision which can be followed to achieve more efficiency, intelligence, and robustness in artificial vision systems. This paper investigates a region-based approach that performs pixel clustering prior to the processes of attention in contrast to late clustering as done by contemporary methods. The foundation steps of feature map construction for the region-based attention model are proposed here. The color contrast map is generated based upon the extended findings from the color theory, the symmetry map is constructed using a novel scanning-based method, and a new algorithm is proposed to compute a size contrast map as a formal feature channel. Eccentricity and orientation are computed using the moments of obtained regions and then saliency is evaluated using the rarity criteria. The efficient design of the proposed algorithms allows incorporating five feature channels while maintaining a processing rate of multiple frames per second. Another salient advantage over the existing techniques is the reusability of the salient regions in the high-level machine vision procedures due to preservation of their shapes and precise locations. The results indicate that the proposed model has the potential to efficiently integrate the phenomenon of attention into the main stream of machine vision and systems with restricted computing resources such as mobile robots can benefit from its advantages.

Calculations of 3D compressible flows using an efficient low diffusion upwind scheme

NASA Astrophysics Data System (ADS)

Hu, Zongjun; Zha, Gecheng

2005-01-01

A newly suggested E-CUSP upwind scheme is employed for the first time to calculate 3D flows of propulsion systems. The E-CUSP scheme contains the total energy in the convective vector and is fully consistent with the characteristic directions. The scheme is proved to have low diffusion and high CPU efficiency. The computed cases in this paper include a transonic nozzle with circular-to-rectangular cross-section, a transonic duct with shock wave/turbulent boundary layer interaction, and a subsonic 3D compressor cascade. The computed results agree well with the experiments. The new scheme is proved to be accurate, efficient and robust for the 3D calculations of the flows in this paper.

Improved Feature Matching for Mobile Devices with IMU.

PubMed

Masiero, Andrea; Vettore, Antonio

2016-08-05

Thanks to the recent diffusion of low-cost high-resolution digital cameras and to the development of mostly automated procedures for image-based 3D reconstruction, the popularity of photogrammetry for environment surveys is constantly increasing in the last years. Automatic feature matching is an important step in order to successfully complete the photogrammetric 3D reconstruction: this step is the fundamental basis for the subsequent estimation of the geometry of the scene. This paper reconsiders the feature matching problem when dealing with smart mobile devices (e.g., when using the standard camera embedded in a smartphone as imaging sensor). More specifically, this paper aims at exploiting the information on camera movements provided by the inertial navigation system (INS) in order to make the feature matching step more robust and, possibly, computationally more efficient. First, a revised version of the affine scale-invariant feature transform (ASIFT) is considered: this version reduces the computational complexity of the original ASIFT, while still ensuring an increase of correct feature matches with respect to the SIFT. Furthermore, a new two-step procedure for the estimation of the essential matrix E (and the camera pose) is proposed in order to increase its estimation robustness and computational efficiency.

Transonic Navier-Stokes wing solutions using a zonal approach. Part 2: High angle-of-attack simulation

NASA Technical Reports Server (NTRS)

Chaderjian, N. M.

1986-01-01

A computer code is under development whereby the thin-layer Reynolds-averaged Navier-Stokes equations are to be applied to realistic fighter-aircraft configurations. This transonic Navier-Stokes code (TNS) utilizes a zonal approach in order to treat complex geometries and satisfy in-core computer memory constraints. The zonal approach has been applied to isolated wing geometries in order to facilitate code development. Part 1 of this paper addresses the TNS finite-difference algorithm, zonal methodology, and code validation with experimental data. Part 2 of this paper addresses some numerical issues such as code robustness, efficiency, and accuracy at high angles of attack. Special free-stream-preserving metrics proved an effective way to treat H-mesh singularities over a large range of severe flow conditions, including strong leading-edge flow gradients, massive shock-induced separation, and stall. Furthermore, lift and drag coefficients have been computed for a wing up through CLmax. Numerical oil flow patterns and particle trajectories are presented both for subcritical and transonic flow. These flow simulations are rich with complex separated flow physics and demonstrate the efficiency and robustness of the zonal approach.

Fast hydrological model calibration based on the heterogeneous parallel computing accelerated shuffled complex evolution method

NASA Astrophysics Data System (ADS)

Kan, Guangyuan; He, Xiaoyan; Ding, Liuqian; Li, Jiren; Hong, Yang; Zuo, Depeng; Ren, Minglei; Lei, Tianjie; Liang, Ke

2018-01-01

Hydrological model calibration has been a hot issue for decades. The shuffled complex evolution method developed at the University of Arizona (SCE-UA) has been proved to be an effective and robust optimization approach. However, its computational efficiency deteriorates significantly when the amount of hydrometeorological data increases. In recent years, the rise of heterogeneous parallel computing has brought hope for the acceleration of hydrological model calibration. This study proposed a parallel SCE-UA method and applied it to the calibration of a watershed rainfall-runoff model, the Xinanjiang model. The parallel method was implemented on heterogeneous computing systems using OpenMP and CUDA. Performance testing and sensitivity analysis were carried out to verify its correctness and efficiency. Comparison results indicated that heterogeneous parallel computing-accelerated SCE-UA converged much more quickly than the original serial version and possessed satisfactory accuracy and stability for the task of fast hydrological model calibration.

DANoC: An Efficient Algorithm and Hardware Codesign of Deep Neural Networks on Chip.

PubMed

Zhou, Xichuan; Li, Shengli; Tang, Fang; Hu, Shengdong; Lin, Zhi; Zhang, Lei

2017-07-18

Deep neural networks (NNs) are the state-of-the-art models for understanding the content of images and videos. However, implementing deep NNs in embedded systems is a challenging task, e.g., a typical deep belief network could exhaust gigabytes of memory and result in bandwidth and computational bottlenecks. To address this challenge, this paper presents an algorithm and hardware codesign for efficient deep neural computation. A hardware-oriented deep learning algorithm, named the deep adaptive network, is proposed to explore the sparsity of neural connections. By adaptively removing the majority of neural connections and robustly representing the reserved connections using binary integers, the proposed algorithm could save up to 99.9% memory utility and computational resources without undermining classification accuracy. An efficient sparse-mapping-memory-based hardware architecture is proposed to fully take advantage of the algorithmic optimization. Different from traditional Von Neumann architecture, the deep-adaptive network on chip (DANoC) brings communication and computation in close proximity to avoid power-hungry parameter transfers between on-board memory and on-chip computational units. Experiments over different image classification benchmarks show that the DANoC system achieves competitively high accuracy and efficiency comparing with the state-of-the-art approaches.

A novel binary shape context for 3D local surface description

NASA Astrophysics Data System (ADS)

Dong, Zhen; Yang, Bisheng; Liu, Yuan; Liang, Fuxun; Li, Bijun; Zang, Yufu

2017-08-01

3D local surface description is now at the core of many computer vision technologies, such as 3D object recognition, intelligent driving, and 3D model reconstruction. However, most of the existing 3D feature descriptors still suffer from low descriptiveness, weak robustness, and inefficiency in both time and memory. To overcome these challenges, this paper presents a robust and descriptive 3D Binary Shape Context (BSC) descriptor with high efficiency in both time and memory. First, a novel BSC descriptor is generated for 3D local surface description, and the performance of the BSC descriptor under different settings of its parameters is analyzed. Next, the descriptiveness, robustness, and efficiency in both time and memory of the BSC descriptor are evaluated and compared to those of several state-of-the-art 3D feature descriptors. Finally, the performance of the BSC descriptor for 3D object recognition is also evaluated on a number of popular benchmark datasets, and an urban-scene dataset is collected by a terrestrial laser scanner system. Comprehensive experiments demonstrate that the proposed BSC descriptor obtained high descriptiveness, strong robustness, and high efficiency in both time and memory and achieved high recognition rates of 94.8%, 94.1% and 82.1% on the considered UWA, Queen, and WHU datasets, respectively.

A semi-Lagrangian transport method for kinetic problems with application to dense-to-dilute polydisperse reacting spray flows

NASA Astrophysics Data System (ADS)

Doisneau, François; Arienti, Marco; Oefelein, Joseph C.

2017-01-01

For sprays, as described by a kinetic disperse phase model strongly coupled to the Navier-Stokes equations, the resolution strategy is constrained by accuracy objectives, robustness needs, and the computing architecture. In order to leverage the good properties of the Eulerian formalism, we introduce a deterministic particle-based numerical method to solve transport in physical space, which is simple to adapt to the many types of closures and moment systems. The method is inspired by the semi-Lagrangian schemes, developed for Gas Dynamics. We show how semi-Lagrangian formulations are relevant for a disperse phase far from equilibrium and where the particle-particle coupling barely influences the transport; i.e., when particle pressure is negligible. The particle behavior is indeed close to free streaming. The new method uses the assumption of parcel transport and avoids to compute fluxes and their limiters, which makes it robust. It is a deterministic resolution method so that it does not require efforts on statistical convergence, noise control, or post-processing. All couplings are done among data under the form of Eulerian fields, which allows one to use efficient algorithms and to anticipate the computational load. This makes the method both accurate and efficient in the context of parallel computing. After a complete verification of the new transport method on various academic test cases, we demonstrate the overall strategy's ability to solve a strongly-coupled liquid jet with fine spatial resolution and we apply it to the case of high-fidelity Large Eddy Simulation of a dense spray flow. A fuel spray is simulated after atomization at Diesel engine combustion chamber conditions. The large, parallel, strongly coupled computation proves the efficiency of the method for dense, polydisperse, reacting spray flows.

A semi-Lagrangian transport method for kinetic problems with application to dense-to-dilute polydisperse reacting spray flows

DOE Office of Scientific and Technical Information (OSTI.GOV)

Doisneau, François, E-mail: fdoisne@sandia.gov; Arienti, Marco, E-mail: marient@sandia.gov; Oefelein, Joseph C., E-mail: oefelei@sandia.gov

For sprays, as described by a kinetic disperse phase model strongly coupled to the Navier–Stokes equations, the resolution strategy is constrained by accuracy objectives, robustness needs, and the computing architecture. In order to leverage the good properties of the Eulerian formalism, we introduce a deterministic particle-based numerical method to solve transport in physical space, which is simple to adapt to the many types of closures and moment systems. The method is inspired by the semi-Lagrangian schemes, developed for Gas Dynamics. We show how semi-Lagrangian formulations are relevant for a disperse phase far from equilibrium and where the particle–particle coupling barelymore » influences the transport; i.e., when particle pressure is negligible. The particle behavior is indeed close to free streaming. The new method uses the assumption of parcel transport and avoids to compute fluxes and their limiters, which makes it robust. It is a deterministic resolution method so that it does not require efforts on statistical convergence, noise control, or post-processing. All couplings are done among data under the form of Eulerian fields, which allows one to use efficient algorithms and to anticipate the computational load. This makes the method both accurate and efficient in the context of parallel computing. After a complete verification of the new transport method on various academic test cases, we demonstrate the overall strategy's ability to solve a strongly-coupled liquid jet with fine spatial resolution and we apply it to the case of high-fidelity Large Eddy Simulation of a dense spray flow. A fuel spray is simulated after atomization at Diesel engine combustion chamber conditions. The large, parallel, strongly coupled computation proves the efficiency of the method for dense, polydisperse, reacting spray flows.« less

Three-Dimensional Navier-Stokes Method with Two-Equation Turbulence Models for Efficient Numerical Simulation of Hypersonic Flows

NASA Technical Reports Server (NTRS)

Bardina, J. E.

1994-01-01

A new computational efficient 3-D compressible Reynolds-averaged implicit Navier-Stokes method with advanced two equation turbulence models for high speed flows is presented. All convective terms are modeled using an entropy satisfying higher-order Total Variation Diminishing (TVD) scheme based on implicit upwind flux-difference split approximations and arithmetic averaging procedure of primitive variables. This method combines the best features of data management and computational efficiency of space marching procedures with the generality and stability of time dependent Navier-Stokes procedures to solve flows with mixed supersonic and subsonic zones, including streamwise separated flows. Its robust stability derives from a combination of conservative implicit upwind flux-difference splitting with Roe's property U to provide accurate shock capturing capability that non-conservative schemes do not guarantee, alternating symmetric Gauss-Seidel 'method of planes' relaxation procedure coupled with a three-dimensional two-factor diagonal-dominant approximate factorization scheme, TVD flux limiters of higher-order flux differences satisfying realizability, and well-posed characteristic-based implicit boundary-point a'pproximations consistent with the local characteristics domain of dependence. The efficiency of the method is highly increased with Newton Raphson acceleration which allows convergence in essentially one forward sweep for supersonic flows. The method is verified by comparing with experiment and other Navier-Stokes methods. Here, results of adiabatic and cooled flat plate flows, compression corner flow, and 3-D hypersonic shock-wave/turbulent boundary layer interaction flows are presented. The robust 3-D method achieves a better computational efficiency of at least one order of magnitude over the CNS Navier-Stokes code. It provides cost-effective aerodynamic predictions in agreement with experiment, and the capability of predicting complex flow structures in complex geometries with good accuracy.

A flexible and accurate digital volume correlation method applicable to high-resolution volumetric images

NASA Astrophysics Data System (ADS)

Pan, Bing; Wang, Bo

2017-10-01

Digital volume correlation (DVC) is a powerful technique for quantifying interior deformation within solid opaque materials and biological tissues. In the last two decades, great efforts have been made to improve the accuracy and efficiency of the DVC algorithm. However, there is still a lack of a flexible, robust and accurate version that can be efficiently implemented in personal computers with limited RAM. This paper proposes an advanced DVC method that can realize accurate full-field internal deformation measurement applicable to high-resolution volume images with up to billions of voxels. Specifically, a novel layer-wise reliability-guided displacement tracking strategy combined with dynamic data management is presented to guide the DVC computation from slice to slice. The displacements at specified calculation points in each layer are computed using the advanced 3D inverse-compositional Gauss-Newton algorithm with the complete initial guess of the deformation vector accurately predicted from the computed calculation points. Since only limited slices of interest in the reference and deformed volume images rather than the whole volume images are required, the DVC calculation can thus be efficiently implemented on personal computers. The flexibility, accuracy and efficiency of the presented DVC approach are demonstrated by analyzing computer-simulated and experimentally obtained high-resolution volume images.

Composite SAR imaging using sequential joint sparsity

NASA Astrophysics Data System (ADS)

Sanders, Toby; Gelb, Anne; Platte, Rodrigo B.

2017-06-01

This paper investigates accurate and efficient ℓ1 regularization methods for generating synthetic aperture radar (SAR) images. Although ℓ1 regularization algorithms are already employed in SAR imaging, practical and efficient implementation in terms of real time imaging remain a challenge. Here we demonstrate that fast numerical operators can be used to robustly implement ℓ1 regularization methods that are as or more efficient than traditional approaches such as back projection, while providing superior image quality. In particular, we develop a sequential joint sparsity model for composite SAR imaging which naturally combines the joint sparsity methodology with composite SAR. Our technique, which can be implemented using standard, fractional, or higher order total variation regularization, is able to reduce the effects of speckle and other noisy artifacts with little additional computational cost. Finally we show that generalizing total variation regularization to non-integer and higher orders provides improved flexibility and robustness for SAR imaging.

Accelerated Enveloping Distribution Sampling: Enabling Sampling of Multiple End States while Preserving Local Energy Minima.

PubMed

Perthold, Jan Walther; Oostenbrink, Chris

2018-05-17

Enveloping distribution sampling (EDS) is an efficient approach to calculate multiple free-energy differences from a single molecular dynamics (MD) simulation. However, the construction of an appropriate reference-state Hamiltonian that samples all states efficiently is not straightforward. We propose a novel approach for the construction of the EDS reference-state Hamiltonian, related to a previously described procedure to smoothen energy landscapes. In contrast to previously suggested EDS approaches, our reference-state Hamiltonian preserves local energy minima of the combined end-states. Moreover, we propose an intuitive, robust and efficient parameter optimization scheme to tune EDS Hamiltonian parameters. We demonstrate the proposed method with established and novel test systems and conclude that our approach allows for the automated calculation of multiple free-energy differences from a single simulation. Accelerated EDS promises to be a robust and user-friendly method to compute free-energy differences based on solid statistical mechanics.

Linear programming computational experience with onyx

DOE Office of Scientific and Technical Information (OSTI.GOV)

Atrek, E.

1994-12-31

ONYX is a linear programming software package based on an efficient variation of the gradient projection method. When fully configured, it is intended for application to industrial size problems. While the computational experience is limited at the time of this abstract, the technique is found to be robust and competitive with existing methodology in terms of both accuracy and speed. An overview of the approach is presented together with a description of program capabilities, followed by a discussion of up-to-date computational experience with the program. Conclusions include advantages of the approach and envisioned future developments.

Robust large-scale parallel nonlinear solvers for simulations.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bader, Brett William; Pawlowski, Roger Patrick; Kolda, Tamara Gibson

2005-11-01

This report documents research to develop robust and efficient solution techniques for solving large-scale systems of nonlinear equations. The most widely used method for solving systems of nonlinear equations is Newton's method. While much research has been devoted to augmenting Newton-based solvers (usually with globalization techniques), little has been devoted to exploring the application of different models. Our research has been directed at evaluating techniques using different models than Newton's method: a lower order model, Broyden's method, and a higher order model, the tensor method. We have developed large-scale versions of each of these models and have demonstrated their usemore » in important applications at Sandia. Broyden's method replaces the Jacobian with an approximation, allowing codes that cannot evaluate a Jacobian or have an inaccurate Jacobian to converge to a solution. Limited-memory methods, which have been successful in optimization, allow us to extend this approach to large-scale problems. We compare the robustness and efficiency of Newton's method, modified Newton's method, Jacobian-free Newton-Krylov method, and our limited-memory Broyden method. Comparisons are carried out for large-scale applications of fluid flow simulations and electronic circuit simulations. Results show that, in cases where the Jacobian was inaccurate or could not be computed, Broyden's method converged in some cases where Newton's method failed to converge. We identify conditions where Broyden's method can be more efficient than Newton's method. We also present modifications to a large-scale tensor method, originally proposed by Bouaricha, for greater efficiency, better robustness, and wider applicability. Tensor methods are an alternative to Newton-based methods and are based on computing a step based on a local quadratic model rather than a linear model. The advantage of Bouaricha's method is that it can use any existing linear solver, which makes it simple to write and easily portable. However, the method usually takes twice as long to solve as Newton-GMRES on general problems because it solves two linear systems at each iteration. In this paper, we discuss modifications to Bouaricha's method for a practical implementation, including a special globalization technique and other modifications for greater efficiency. We present numerical results showing computational advantages over Newton-GMRES on some realistic problems. We further discuss a new approach for dealing with singular (or ill-conditioned) matrices. In particular, we modify an algorithm for identifying a turning point so that an increasingly ill-conditioned Jacobian does not prevent convergence.« less

A robust and fast active contour model for image segmentation with intensity inhomogeneity

NASA Astrophysics Data System (ADS)

Ding, Keyan; Weng, Guirong

2018-04-01

In this paper, a robust and fast active contour model is proposed for image segmentation in the presence of intensity inhomogeneity. By introducing the local image intensities fitting functions before the evolution of curve, the proposed model can effectively segment images with intensity inhomogeneity. And the computation cost is low because the fitting functions do not need to be updated in each iteration. Experiments have shown that the proposed model has a higher segmentation efficiency compared to some well-known active contour models based on local region fitting energy. In addition, the proposed model is robust to initialization, which allows the initial level set function to be a small constant function.

Feedback Robust Cubature Kalman Filter for Target Tracking Using an Angle Sensor.

PubMed

Wu, Hao; Chen, Shuxin; Yang, Binfeng; Chen, Kun

2016-05-09

The direction of arrival (DOA) tracking problem based on an angle sensor is an important topic in many fields. In this paper, a nonlinear filter named the feedback M-estimation based robust cubature Kalman filter (FMR-CKF) is proposed to deal with measurement outliers from the angle sensor. The filter designs a new equivalent weight function with the Mahalanobis distance to combine the cubature Kalman filter (CKF) with the M-estimation method. Moreover, by embedding a feedback strategy which consists of a splitting and merging procedure, the proper sub-filter (the standard CKF or the robust CKF) can be chosen in each time index. Hence, the probability of the outliers' misjudgment can be reduced. Numerical experiments show that the FMR-CKF performs better than the CKF and conventional robust filters in terms of accuracy and robustness with good computational efficiency. Additionally, the filter can be extended to the nonlinear applications using other types of sensors.

Bayesian experimental design for models with intractable likelihoods.

PubMed

Drovandi, Christopher C; Pettitt, Anthony N

2013-12-01

In this paper we present a methodology for designing experiments for efficiently estimating the parameters of models with computationally intractable likelihoods. The approach combines a commonly used methodology for robust experimental design, based on Markov chain Monte Carlo sampling, with approximate Bayesian computation (ABC) to ensure that no likelihood evaluations are required. The utility function considered for precise parameter estimation is based upon the precision of the ABC posterior distribution, which we form efficiently via the ABC rejection algorithm based on pre-computed model simulations. Our focus is on stochastic models and, in particular, we investigate the methodology for Markov process models of epidemics and macroparasite population evolution. The macroparasite example involves a multivariate process and we assess the loss of information from not observing all variables. © 2013, The International Biometric Society.

Human motion planning based on recursive dynamics and optimal control techniques

NASA Technical Reports Server (NTRS)

Lo, Janzen; Huang, Gang; Metaxas, Dimitris

2002-01-01

This paper presents an efficient optimal control and recursive dynamics-based computer animation system for simulating and controlling the motion of articulated figures. A quasi-Newton nonlinear programming technique (super-linear convergence) is implemented to solve minimum torque-based human motion-planning problems. The explicit analytical gradients needed in the dynamics are derived using a matrix exponential formulation and Lie algebra. Cubic spline functions are used to make the search space for an optimal solution finite. Based on our formulations, our method is well conditioned and robust, in addition to being computationally efficient. To better illustrate the efficiency of our method, we present results of natural looking and physically correct human motions for a variety of human motion tasks involving open and closed loop kinematic chains.

Robust and efficient overset grid assembly for partitioned unstructured meshes

NASA Astrophysics Data System (ADS)

Roget, Beatrice; Sitaraman, Jayanarayanan

2014-03-01

This paper presents a method to perform efficient and automated Overset Grid Assembly (OGA) on a system of overlapping unstructured meshes in a parallel computing environment where all meshes are partitioned into multiple mesh-blocks and processed on multiple cores. The main task of the overset grid assembler is to identify, in parallel, among all points in the overlapping mesh system, at which points the flow solution should be computed (field points), interpolated (receptor points), or ignored (hole points). Point containment search or donor search, an algorithm to efficiently determine the cell that contains a given point, is the core procedure necessary for accomplishing this task. Donor search is particularly challenging for partitioned unstructured meshes because of the complex irregular boundaries that are often created during partitioning.

A Taylor Expansion-Based Adaptive Design Strategy for Global Surrogate Modeling With Applications in Groundwater Modeling

DOE PAGES

Mo, Shaoxing; Lu, Dan; Shi, Xiaoqing; ...

2017-12-27

Global sensitivity analysis (GSA) and uncertainty quantification (UQ) for groundwater modeling are challenging because of the model complexity and significant computational requirements. To reduce the massive computational cost, a cheap-to-evaluate surrogate model is usually constructed to approximate and replace the expensive groundwater models in the GSA and UQ. Constructing an accurate surrogate requires actual model simulations on a number of parameter samples. Thus, a robust experimental design strategy is desired to locate informative samples so as to reduce the computational cost in surrogate construction and consequently to improve the efficiency in the GSA and UQ. In this study, we developmore » a Taylor expansion-based adaptive design (TEAD) that aims to build an accurate global surrogate model with a small training sample size. TEAD defines a novel hybrid score function to search informative samples, and a robust stopping criterion to terminate the sample search that guarantees the resulted approximation errors satisfy the desired accuracy. The good performance of TEAD in building global surrogate models is demonstrated in seven analytical functions with different dimensionality and complexity in comparison to two widely used experimental design methods. The application of the TEAD-based surrogate method in two groundwater models shows that the TEAD design can effectively improve the computational efficiency of GSA and UQ for groundwater modeling.« less

A Taylor Expansion-Based Adaptive Design Strategy for Global Surrogate Modeling With Applications in Groundwater Modeling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mo, Shaoxing; Lu, Dan; Shi, Xiaoqing

Global sensitivity analysis (GSA) and uncertainty quantification (UQ) for groundwater modeling are challenging because of the model complexity and significant computational requirements. To reduce the massive computational cost, a cheap-to-evaluate surrogate model is usually constructed to approximate and replace the expensive groundwater models in the GSA and UQ. Constructing an accurate surrogate requires actual model simulations on a number of parameter samples. Thus, a robust experimental design strategy is desired to locate informative samples so as to reduce the computational cost in surrogate construction and consequently to improve the efficiency in the GSA and UQ. In this study, we developmore » a Taylor expansion-based adaptive design (TEAD) that aims to build an accurate global surrogate model with a small training sample size. TEAD defines a novel hybrid score function to search informative samples, and a robust stopping criterion to terminate the sample search that guarantees the resulted approximation errors satisfy the desired accuracy. The good performance of TEAD in building global surrogate models is demonstrated in seven analytical functions with different dimensionality and complexity in comparison to two widely used experimental design methods. The application of the TEAD-based surrogate method in two groundwater models shows that the TEAD design can effectively improve the computational efficiency of GSA and UQ for groundwater modeling.« less

Acceleration of FDTD mode solver by high-performance computing techniques.

PubMed

Han, Lin; Xi, Yanping; Huang, Wei-Ping

2010-06-21

A two-dimensional (2D) compact finite-difference time-domain (FDTD) mode solver is developed based on wave equation formalism in combination with the matrix pencil method (MPM). The method is validated for calculation of both real guided and complex leaky modes of typical optical waveguides against the bench-mark finite-difference (FD) eigen mode solver. By taking advantage of the inherent parallel nature of the FDTD algorithm, the mode solver is implemented on graphics processing units (GPUs) using the compute unified device architecture (CUDA). It is demonstrated that the high-performance computing technique leads to significant acceleration of the FDTD mode solver with more than 30 times improvement in computational efficiency in comparison with the conventional FDTD mode solver running on CPU of a standard desktop computer. The computational efficiency of the accelerated FDTD method is in the same order of magnitude of the standard finite-difference eigen mode solver and yet require much less memory (e.g., less than 10%). Therefore, the new method may serve as an efficient, accurate and robust tool for mode calculation of optical waveguides even when the conventional eigen value mode solvers are no longer applicable due to memory limitation.

High-Throughput Histopathological Image Analysis via Robust Cell Segmentation and Hashing

PubMed Central

Zhang, Xiaofan; Xing, Fuyong; Su, Hai; Yang, Lin; Zhang, Shaoting

2015-01-01

Computer-aided diagnosis of histopathological images usually requires to examine all cells for accurate diagnosis. Traditional computational methods may have efficiency issues when performing cell-level analysis. In this paper, we propose a robust and scalable solution to enable such analysis in a real-time fashion. Specifically, a robust segmentation method is developed to delineate cells accurately using Gaussian-based hierarchical voting and repulsive balloon model. A large-scale image retrieval approach is also designed to examine and classify each cell of a testing image by comparing it with a massive database, e.g., half-million cells extracted from the training dataset. We evaluate this proposed framework on a challenging and important clinical use case, i.e., differentiation of two types of lung cancers (the adenocarcinoma and squamous carcinoma), using thousands of lung microscopic tissue images extracted from hundreds of patients. Our method has achieved promising accuracy and running time by searching among half-million cells. PMID:26599156

An efficient adaptive sampling strategy for global surrogate modeling with applications in multiphase flow simulation

NASA Astrophysics Data System (ADS)

Mo, S.; Lu, D.; Shi, X.; Zhang, G.; Ye, M.; Wu, J.

2016-12-01

Surrogate models have shown remarkable computational efficiency in hydrological simulations involving design space exploration, sensitivity analysis, uncertainty quantification, etc. The central task of constructing a global surrogate models is to achieve a prescribed approximation accuracy with as few original model executions as possible, which requires a good design strategy to optimize the distribution of data points in the parameter domains and an effective stopping criterion to automatically terminate the design process when desired approximation accuracy is achieved. This study proposes a novel adaptive sampling strategy, which starts from a small number of initial samples and adaptively selects additional samples by balancing the collection in unexplored regions and refinement in interesting areas. We define an efficient and effective evaluation metric basing on Taylor expansion to select the most promising potential samples from candidate points, and propose a robust stopping criterion basing on the approximation accuracy at new points to guarantee the achievement of desired accuracy. The numerical results of several benchmark analytical functions indicate that the proposed approach is more computationally efficient and robust than the widely used maximin distance design and two other well-known adaptive sampling strategies. The application to two complicated multiphase flow problems further demonstrates the efficiency and effectiveness of our method in constructing global surrogate models for high-dimensional and highly nonlinear problems. Acknowledgements: This work was financially supported by the National Nature Science Foundation of China grants No. 41030746 and 41172206.

Real-time depth processing for embedded platforms

NASA Astrophysics Data System (ADS)

Rahnama, Oscar; Makarov, Aleksej; Torr, Philip

2017-05-01

Obtaining depth information of a scene is an important requirement in many computer-vision and robotics applications. For embedded platforms, passive stereo systems have many advantages over their active counterparts (i.e. LiDAR, Infrared). They are power efficient, cheap, robust to lighting conditions and inherently synchronized to the RGB images of the scene. However, stereo depth estimation is a computationally expensive task that operates over large amounts of data. For embedded applications which are often constrained by power consumption, obtaining accurate results in real-time is a challenge. We demonstrate a computationally and memory efficient implementation of a stereo block-matching algorithm in FPGA. The computational core achieves a throughput of 577 fps at standard VGA resolution whilst consuming less than 3 Watts of power. The data is processed using an in-stream approach that minimizes memory-access bottlenecks and best matches the raster scan readout of modern digital image sensors.

OpenFOAM: Open source CFD in research and industry

NASA Astrophysics Data System (ADS)

Jasak, Hrvoje

2009-12-01

The current focus of development in industrial Computational Fluid Dynamics (CFD) is integration of CFD into Computer-Aided product development, geometrical optimisation, robust design and similar. On the other hand, in CFD research aims to extend the boundaries ofpractical engineering use in "non-traditional " areas. Requirements of computational flexibility and code integration are contradictory: a change of coding paradigm, with object orientation, library components, equation mimicking is proposed as a way forward. This paper describes OpenFOAM, a C++ object oriented library for Computational Continuum Mechanics (CCM) developed by the author. Efficient and flexible implementation of complex physical models is achieved by mimicking the form ofpartial differential equation in software, with code functionality provided in library form. Open Source deployment and development model allows the user to achieve desired versatility in physical modeling without the sacrifice of complex geometry support and execution efficiency.

Robust Low-dose CT Perfusion Deconvolution via Tensor Total-Variation Regularization

PubMed Central

Zhang, Shaoting; Chen, Tsuhan; Sanelli, Pina C.

2016-01-01

Acute brain diseases such as acute strokes and transit ischemic attacks are the leading causes of mortality and morbidity worldwide, responsible for 9% of total death every year. ‘Time is brain’ is a widely accepted concept in acute cerebrovascular disease treatment. Efficient and accurate computational framework for hemodynamic parameters estimation can save critical time for thrombolytic therapy. Meanwhile the high level of accumulated radiation dosage due to continuous image acquisition in CT perfusion (CTP) raised concerns on patient safety and public health. However, low-radiation leads to increased noise and artifacts which require more sophisticated and time-consuming algorithms for robust estimation. In this paper, we focus on developing a robust and efficient framework to accurately estimate the perfusion parameters at low radiation dosage. Specifically, we present a tensor total-variation (TTV) technique which fuses the spatial correlation of the vascular structure and the temporal continuation of the blood signal flow. An efficient algorithm is proposed to find the solution with fast convergence and reduced computational complexity. Extensive evaluations are carried out in terms of sensitivity to noise levels, estimation accuracy, contrast preservation, and performed on digital perfusion phantom estimation, as well as in-vivo clinical subjects. Our framework reduces the necessary radiation dose to only 8% of the original level and outperforms the state-of-art algorithms with peak signal-to-noise ratio improved by 32%. It reduces the oscillation in the residue functions, corrects over-estimation of cerebral blood flow (CBF) and under-estimation of mean transit time (MTT), and maintains the distinction between the deficit and normal regions. PMID:25706579

Melody Alignment and Similarity Metric for Content-Based Music Retrieval

NASA Astrophysics Data System (ADS)

Zhu, Yongwei; Kankanhalli, Mohan S.

2003-01-01

Music query-by-humming has attracted much research interest recently. It is a challenging problem since the hummed query inevitably contains much variation and inaccuracy. Furthermore, the similarity computation between the query tune and the reference melody is not easy due to the difficulty in ensuring proper alignment. This is because the query tune can be rendered at an unknown speed and it is usually an arbitrary subsequence of the target reference melody. Many of the previous methods, which adopt note segmentation and string matching, suffer drastically from the errors in the note segmentation, which affects retrieval accuracy and efficiency. Some methods solve the alignment issue by controlling the speed of the articulation of queries, which is inconvenient because it forces users to hum along a metronome. Some other techniques introduce arbitrary rescaling in time but this is computationally very inefficient. In this paper, we introduce a melody alignment technique, which addresses the robustness and efficiency issues. We also present a new melody similarity metric, which is performed directly on melody contours of the query data. This approach cleanly separates the alignment and similarity measurement in the search process. We show how to robustly and efficiently align the query melody with the reference melodies and how to measure the similarity subsequently. We have carried out extensive experiments. Our melody alignment method can reduce the matching candidate to 1.7% with 95% correct alignment rate. The overall retrieval system achieved 80% recall in the top 10 rank list. The results demonstrate the robustness and effectiveness the proposed methods.

A robust and efficient stepwise regression method for building sparse polynomial chaos expansions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Abraham, Simon, E-mail: Simon.Abraham@ulb.ac.be; Raisee, Mehrdad; Ghorbaniasl, Ghader

2017-03-01

Polynomial Chaos (PC) expansions are widely used in various engineering fields for quantifying uncertainties arising from uncertain parameters. The computational cost of classical PC solution schemes is unaffordable as the number of deterministic simulations to be calculated grows dramatically with the number of stochastic dimension. This considerably restricts the practical use of PC at the industrial level. A common approach to address such problems is to make use of sparse PC expansions. This paper presents a non-intrusive regression-based method for building sparse PC expansions. The most important PC contributions are detected sequentially through an automatic search procedure. The variable selectionmore » criterion is based on efficient tools relevant to probabilistic method. Two benchmark analytical functions are used to validate the proposed algorithm. The computational efficiency of the method is then illustrated by a more realistic CFD application, consisting of the non-deterministic flow around a transonic airfoil subject to geometrical uncertainties. To assess the performance of the developed methodology, a detailed comparison is made with the well established LAR-based selection technique. The results show that the developed sparse regression technique is able to identify the most significant PC contributions describing the problem. Moreover, the most important stochastic features are captured at a reduced computational cost compared to the LAR method. The results also demonstrate the superior robustness of the method by repeating the analyses using random experimental designs.« less

TOUGH3: A new efficient version of the TOUGH suite of multiphase flow and transport simulators

NASA Astrophysics Data System (ADS)

Jung, Yoojin; Pau, George Shu Heng; Finsterle, Stefan; Pollyea, Ryan M.

2017-11-01

The TOUGH suite of nonisothermal multiphase flow and transport simulators has been updated by various developers over many years to address a vast range of challenging subsurface problems. The increasing complexity of the simulated processes as well as the growing size of model domains that need to be handled call for an improvement in the simulator's computational robustness and efficiency. Moreover, modifications have been frequently introduced independently, resulting in multiple versions of TOUGH that (1) led to inconsistencies in feature implementation and usage, (2) made code maintenance and development inefficient, and (3) caused confusion to users and developers. TOUGH3-a new base version of TOUGH-addresses these issues. It consolidates both the serial (TOUGH2 V2.1) and parallel (TOUGH2-MP V2.0) implementations, enabling simulations to be performed on desktop computers and supercomputers using a single code. New PETSc parallel linear solvers are added to the existing serial solvers of TOUGH2 and the Aztec solver used in TOUGH2-MP. The PETSc solvers generally perform better than the Aztec solvers in parallel and the internal TOUGH3 linear solver in serial. TOUGH3 also incorporates many new features, addresses bugs, and improves the flexibility of data handling. Due to the improved capabilities and usability, TOUGH3 is more robust and efficient for solving tough and computationally demanding problems in diverse scientific and practical applications related to subsurface flow modeling.

Quasi-Maximum Likelihood Estimation of Structural Equation Models with Multiple Interaction and Quadratic Effects

ERIC Educational Resources Information Center

Klein, Andreas G.; Muthen, Bengt O.

2007-01-01

In this article, a nonlinear structural equation model is introduced and a quasi-maximum likelihood method for simultaneous estimation and testing of multiple nonlinear effects is developed. The focus of the new methodology lies on efficiency, robustness, and computational practicability. Monte-Carlo studies indicate that the method is highly…

Hebbian Wiring Plasticity Generates Efficient Network Structures for Robust Inference with Synaptic Weight Plasticity

PubMed Central

Hiratani, Naoki; Fukai, Tomoki

2016-01-01

In the adult mammalian cortex, a small fraction of spines are created and eliminated every day, and the resultant synaptic connection structure is highly nonrandom, even in local circuits. However, it remains unknown whether a particular synaptic connection structure is functionally advantageous in local circuits, and why creation and elimination of synaptic connections is necessary in addition to rich synaptic weight plasticity. To answer these questions, we studied an inference task model through theoretical and numerical analyses. We demonstrate that a robustly beneficial network structure naturally emerges by combining Hebbian-type synaptic weight plasticity and wiring plasticity. Especially in a sparsely connected network, wiring plasticity achieves reliable computation by enabling efficient information transmission. Furthermore, the proposed rule reproduces experimental observed correlation between spine dynamics and task performance. PMID:27303271

Adaptive correlation filter-based video stabilization without accumulative global motion estimation

NASA Astrophysics Data System (ADS)

Koh, Eunjin; Lee, Chanyong; Jeong, Dong Gil

2014-12-01

We present a digital video stabilization approach that provides both robustness and efficiency for practical applications. In this approach, we adopt a stabilization model that maintains spatio-temporal information of past input frames efficiently and can track original stabilization position. Because of the stabilization model, the proposed method does not need accumulative global motion estimation and can recover the original position even if there is a failure in interframe motion estimation. It can also intelligently overcome the situation of damaged or interrupted video sequences. Moreover, because it is simple and suitable to parallel scheme, we implement it on a commercial field programmable gate array and a graphics processing unit board with compute unified device architecture in a breeze. Experimental results show that the proposed approach is both fast and robust.

MDTri: robust and efficient global mixed integer search of spaces of multiple ternary alloys: A DIRECT-inspired optimization algorithm for experimentally accessible computational material design

DOE PAGES

Graf, Peter A.; Billups, Stephen

2017-07-24

Computational materials design has suffered from a lack of algorithms formulated in terms of experimentally accessible variables. Here we formulate the problem of (ternary) alloy optimization at the level of choice of atoms and their composition that is normal for synthesists. Mathematically, this is a mixed integer problem where a candidate solution consists of a choice of three elements, and how much of each of them to use. This space has the natural structure of a set of equilateral triangles. We solve this problem by introducing a novel version of the DIRECT algorithm that (1) operates on equilateral triangles insteadmore » of rectangles and (2) works across multiple triangles. We demonstrate on a test case that the algorithm is both robust and efficient. Lastly, we offer an explanation of the efficacy of DIRECT -- specifically, its balance of global and local search -- by showing that 'potentially optimal rectangles' of the original algorithm are akin to the Pareto front of the 'multi-component optimization' of global and local search.« less

A simple, robust and efficient high-order accurate shock-capturing scheme for compressible flows: Towards minimalism

NASA Astrophysics Data System (ADS)

Ohwada, Taku; Shibata, Yuki; Kato, Takuma; Nakamura, Taichi

2018-06-01

Developed is a high-order accurate shock-capturing scheme for the compressible Euler/Navier-Stokes equations; the formal accuracy is 5th order in space and 4th order in time. The performance and efficiency of the scheme are validated in various numerical tests. The main ingredients of the scheme are nothing special; they are variants of the standard numerical flux, MUSCL, the usual Lagrange's polynomial and the conventional Runge-Kutta method. The scheme can compute a boundary layer accurately with a rational resolution and capture a stationary contact discontinuity sharply without inner points. And yet it is endowed with high resistance against shock anomalies (carbuncle phenomenon, post-shock oscillations, etc.). A good balance between high robustness and low dissipation is achieved by blending three types of numerical fluxes according to physical situation in an intuitively easy-to-understand way. The performance of the scheme is largely comparable to that of WENO5-Rusanov, while its computational cost is 30-40% less than of that of the advanced scheme.

MDTri: robust and efficient global mixed integer search of spaces of multiple ternary alloys: A DIRECT-inspired optimization algorithm for experimentally accessible computational material design

DOE Office of Scientific and Technical Information (OSTI.GOV)

Graf, Peter A.; Billups, Stephen

Computational materials design has suffered from a lack of algorithms formulated in terms of experimentally accessible variables. Here we formulate the problem of (ternary) alloy optimization at the level of choice of atoms and their composition that is normal for synthesists. Mathematically, this is a mixed integer problem where a candidate solution consists of a choice of three elements, and how much of each of them to use. This space has the natural structure of a set of equilateral triangles. We solve this problem by introducing a novel version of the DIRECT algorithm that (1) operates on equilateral triangles insteadmore » of rectangles and (2) works across multiple triangles. We demonstrate on a test case that the algorithm is both robust and efficient. Lastly, we offer an explanation of the efficacy of DIRECT -- specifically, its balance of global and local search -- by showing that 'potentially optimal rectangles' of the original algorithm are akin to the Pareto front of the 'multi-component optimization' of global and local search.« less

The HTM Spatial Pooler-A Neocortical Algorithm for Online Sparse Distributed Coding.

PubMed

Cui, Yuwei; Ahmad, Subutai; Hawkins, Jeff

2017-01-01

Hierarchical temporal memory (HTM) provides a theoretical framework that models several key computational principles of the neocortex. In this paper, we analyze an important component of HTM, the HTM spatial pooler (SP). The SP models how neurons learn feedforward connections and form efficient representations of the input. It converts arbitrary binary input patterns into sparse distributed representations (SDRs) using a combination of competitive Hebbian learning rules and homeostatic excitability control. We describe a number of key properties of the SP, including fast adaptation to changing input statistics, improved noise robustness through learning, efficient use of cells, and robustness to cell death. In order to quantify these properties we develop a set of metrics that can be directly computed from the SP outputs. We show how the properties are met using these metrics and targeted artificial simulations. We then demonstrate the value of the SP in a complete end-to-end real-world HTM system. We discuss the relationship with neuroscience and previous studies of sparse coding. The HTM spatial pooler represents a neurally inspired algorithm for learning sparse representations from noisy data streams in an online fashion.

Covariate selection with group lasso and doubly robust estimation of causal effects

PubMed Central

Koch, Brandon; Vock, David M.; Wolfson, Julian

2017-01-01

Summary The efficiency of doubly robust estimators of the average causal effect (ACE) of a treatment can be improved by including in the treatment and outcome models only those covariates which are related to both treatment and outcome (i.e., confounders) or related only to the outcome. However, it is often challenging to identify such covariates among the large number that may be measured in a given study. In this paper, we propose GLiDeR (Group Lasso and Doubly Robust Estimation), a novel variable selection technique for identifying confounders and predictors of outcome using an adaptive group lasso approach that simultaneously performs coefficient selection, regularization, and estimation across the treatment and outcome models. The selected variables and corresponding coefficient estimates are used in a standard doubly robust ACE estimator. We provide asymptotic results showing that, for a broad class of data generating mechanisms, GLiDeR yields a consistent estimator of the ACE when either the outcome or treatment model is correctly specified. A comprehensive simulation study shows that GLiDeR is more efficient than doubly robust methods using standard variable selection techniques and has substantial computational advantages over a recently proposed doubly robust Bayesian model averaging method. We illustrate our method by estimating the causal treatment effect of bilateral versus single-lung transplant on forced expiratory volume in one year after transplant using an observational registry. PMID:28636276

Covariate selection with group lasso and doubly robust estimation of causal effects.

PubMed

Koch, Brandon; Vock, David M; Wolfson, Julian

2018-03-01

The efficiency of doubly robust estimators of the average causal effect (ACE) of a treatment can be improved by including in the treatment and outcome models only those covariates which are related to both treatment and outcome (i.e., confounders) or related only to the outcome. However, it is often challenging to identify such covariates among the large number that may be measured in a given study. In this article, we propose GLiDeR (Group Lasso and Doubly Robust Estimation), a novel variable selection technique for identifying confounders and predictors of outcome using an adaptive group lasso approach that simultaneously performs coefficient selection, regularization, and estimation across the treatment and outcome models. The selected variables and corresponding coefficient estimates are used in a standard doubly robust ACE estimator. We provide asymptotic results showing that, for a broad class of data generating mechanisms, GLiDeR yields a consistent estimator of the ACE when either the outcome or treatment model is correctly specified. A comprehensive simulation study shows that GLiDeR is more efficient than doubly robust methods using standard variable selection techniques and has substantial computational advantages over a recently proposed doubly robust Bayesian model averaging method. We illustrate our method by estimating the causal treatment effect of bilateral versus single-lung transplant on forced expiratory volume in one year after transplant using an observational registry. © 2017, The International Biometric Society.

Decreasing-Rate Pruning Optimizes the Construction of Efficient and Robust Distributed Networks.

PubMed

Navlakha, Saket; Barth, Alison L; Bar-Joseph, Ziv

2015-07-01

Robust, efficient, and low-cost networks are advantageous in both biological and engineered systems. During neural network development in the brain, synapses are massively over-produced and then pruned-back over time. This strategy is not commonly used when designing engineered networks, since adding connections that will soon be removed is considered wasteful. Here, we show that for large distributed routing networks, network function is markedly enhanced by hyper-connectivity followed by aggressive pruning and that the global rate of pruning, a developmental parameter not previously studied by experimentalists, plays a critical role in optimizing network structure. We first used high-throughput image analysis techniques to quantify the rate of pruning in the mammalian neocortex across a broad developmental time window and found that the rate is decreasing over time. Based on these results, we analyzed a model of computational routing networks and show using both theoretical analysis and simulations that decreasing rates lead to more robust and efficient networks compared to other rates. We also present an application of this strategy to improve the distributed design of airline networks. Thus, inspiration from neural network formation suggests effective ways to design distributed networks across several domains.

Decreasing-Rate Pruning Optimizes the Construction of Efficient and Robust Distributed Networks

PubMed Central

Navlakha, Saket; Barth, Alison L.; Bar-Joseph, Ziv

2015-01-01

Robust, efficient, and low-cost networks are advantageous in both biological and engineered systems. During neural network development in the brain, synapses are massively over-produced and then pruned-back over time. This strategy is not commonly used when designing engineered networks, since adding connections that will soon be removed is considered wasteful. Here, we show that for large distributed routing networks, network function is markedly enhanced by hyper-connectivity followed by aggressive pruning and that the global rate of pruning, a developmental parameter not previously studied by experimentalists, plays a critical role in optimizing network structure. We first used high-throughput image analysis techniques to quantify the rate of pruning in the mammalian neocortex across a broad developmental time window and found that the rate is decreasing over time. Based on these results, we analyzed a model of computational routing networks and show using both theoretical analysis and simulations that decreasing rates lead to more robust and efficient networks compared to other rates. We also present an application of this strategy to improve the distributed design of airline networks. Thus, inspiration from neural network formation suggests effective ways to design distributed networks across several domains. PMID:26217933

Efficiency and robustness of different bus network designs

NASA Astrophysics Data System (ADS)

Pang, John Zhen Fu; Bin Othman, Nasri; Ng, Keng Meng; Monterola, Christopher

2015-07-01

We compare the efficiencies and robustness of four transport networks that can be possibly formed as a result of deliberate city planning. The networks are constructed based on their spatial resemblance to the cities of Manhattan (lattice), Sudan (random), Beijing (single-blob) and Greater Cairo (dual-blob). For a given type, a genetic algorithm is employed to obtain an optimized set of the bus routes. We then simulate how commuter travels using Yen's algorithms for k shortest paths on an adjacency matrix. The cost of traveling such as walking between stations is captured by varying the weighted sums of matrices. We also consider the number of transfers a posteriori by looking at the computed shortest paths. With consideration to distances via radius of gyration, redundancies of travel and number of bus transfers, our simulations indicate that random and dual-blob are more efficient than single-blob and lattice networks. Moreover, dual-blob type is least robust when node removals are targeted but is most resilient when node failures are random. The work hopes to guide and provide technical perspectives on how geospatial distribution of a city limits the optimality of transport designs.

A Robust and Effective Smart-Card-Based Remote User Authentication Mechanism Using Hash Function

PubMed Central

Odelu, Vanga; Goswami, Adrijit

2014-01-01

In a remote user authentication scheme, a remote server verifies whether a login user is genuine and trustworthy, and also for mutual authentication purpose a login user validates whether the remote server is genuine and trustworthy. Several remote user authentication schemes using the password, the biometrics, and the smart card have been proposed in the literature. However, most schemes proposed in the literature are either computationally expensive or insecure against several known attacks. In this paper, we aim to propose a new robust and effective password-based remote user authentication scheme using smart card. Our scheme is efficient, because our scheme uses only efficient one-way hash function and bitwise XOR operations. Through the rigorous informal and formal security analysis, we show that our scheme is secure against possible known attacks. We perform the simulation for the formal security analysis using the widely accepted AVISPA (Automated Validation Internet Security Protocols and Applications) tool to ensure that our scheme is secure against passive and active attacks. Furthermore, our scheme supports efficiently the password change phase always locally without contacting the remote server and correctly. In addition, our scheme performs significantly better than other existing schemes in terms of communication, computational overheads, security, and features provided by our scheme. PMID:24892078

A robust and effective smart-card-based remote user authentication mechanism using hash function.

PubMed

Das, Ashok Kumar; Odelu, Vanga; Goswami, Adrijit

2014-01-01

In a remote user authentication scheme, a remote server verifies whether a login user is genuine and trustworthy, and also for mutual authentication purpose a login user validates whether the remote server is genuine and trustworthy. Several remote user authentication schemes using the password, the biometrics, and the smart card have been proposed in the literature. However, most schemes proposed in the literature are either computationally expensive or insecure against several known attacks. In this paper, we aim to propose a new robust and effective password-based remote user authentication scheme using smart card. Our scheme is efficient, because our scheme uses only efficient one-way hash function and bitwise XOR operations. Through the rigorous informal and formal security analysis, we show that our scheme is secure against possible known attacks. We perform the simulation for the formal security analysis using the widely accepted AVISPA (Automated Validation Internet Security Protocols and Applications) tool to ensure that our scheme is secure against passive and active attacks. Furthermore, our scheme supports efficiently the password change phase always locally without contacting the remote server and correctly. In addition, our scheme performs significantly better than other existing schemes in terms of communication, computational overheads, security, and features provided by our scheme.

Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems

PubMed Central

Rodriguez-Fernandez, Maria; Egea, Jose A; Banga, Julio R

2006-01-01

Background We consider the problem of parameter estimation (model calibration) in nonlinear dynamic models of biological systems. Due to the frequent ill-conditioning and multi-modality of many of these problems, traditional local methods usually fail (unless initialized with very good guesses of the parameter vector). In order to surmount these difficulties, global optimization (GO) methods have been suggested as robust alternatives. Currently, deterministic GO methods can not solve problems of realistic size within this class in reasonable computation times. In contrast, certain types of stochastic GO methods have shown promising results, although the computational cost remains large. Rodriguez-Fernandez and coworkers have presented hybrid stochastic-deterministic GO methods which could reduce computation time by one order of magnitude while guaranteeing robustness. Our goal here was to further reduce the computational effort without loosing robustness. Results We have developed a new procedure based on the scatter search methodology for nonlinear optimization of dynamic models of arbitrary (or even unknown) structure (i.e. black-box models). In this contribution, we describe and apply this novel metaheuristic, inspired by recent developments in the field of operations research, to a set of complex identification problems and we make a critical comparison with respect to the previous (above mentioned) successful methods. Conclusion Robust and efficient methods for parameter estimation are of key importance in systems biology and related areas. The new metaheuristic presented in this paper aims to ensure the proper solution of these problems by adopting a global optimization approach, while keeping the computational effort under reasonable values. This new metaheuristic was applied to a set of three challenging parameter estimation problems of nonlinear dynamic biological systems, outperforming very significantly all the methods previously used for these benchmark problems. PMID:17081289

Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems.

PubMed

Rodriguez-Fernandez, Maria; Egea, Jose A; Banga, Julio R

2006-11-02

We consider the problem of parameter estimation (model calibration) in nonlinear dynamic models of biological systems. Due to the frequent ill-conditioning and multi-modality of many of these problems, traditional local methods usually fail (unless initialized with very good guesses of the parameter vector). In order to surmount these difficulties, global optimization (GO) methods have been suggested as robust alternatives. Currently, deterministic GO methods can not solve problems of realistic size within this class in reasonable computation times. In contrast, certain types of stochastic GO methods have shown promising results, although the computational cost remains large. Rodriguez-Fernandez and coworkers have presented hybrid stochastic-deterministic GO methods which could reduce computation time by one order of magnitude while guaranteeing robustness. Our goal here was to further reduce the computational effort without loosing robustness. We have developed a new procedure based on the scatter search methodology for nonlinear optimization of dynamic models of arbitrary (or even unknown) structure (i.e. black-box models). In this contribution, we describe and apply this novel metaheuristic, inspired by recent developments in the field of operations research, to a set of complex identification problems and we make a critical comparison with respect to the previous (above mentioned) successful methods. Robust and efficient methods for parameter estimation are of key importance in systems biology and related areas. The new metaheuristic presented in this paper aims to ensure the proper solution of these problems by adopting a global optimization approach, while keeping the computational effort under reasonable values. This new metaheuristic was applied to a set of three challenging parameter estimation problems of nonlinear dynamic biological systems, outperforming very significantly all the methods previously used for these benchmark problems.

Modified Method of Adaptive Artificial Viscosity for Solution of Gas Dynamics Problems on Parallel Computer Systems

NASA Astrophysics Data System (ADS)

Popov, Igor; Sukov, Sergey

2018-02-01

A modification of the adaptive artificial viscosity (AAV) method is considered. This modification is based on one stage time approximation and is adopted to calculation of gasdynamics problems on unstructured grids with an arbitrary type of grid elements. The proposed numerical method has simplified logic, better performance and parallel efficiency compared to the implementation of the original AAV method. Computer experiments evidence the robustness and convergence of the method to difference solution.

NAS Requirements Checklist for Job Queuing/Scheduling Software

NASA Technical Reports Server (NTRS)

Jones, James Patton

1996-01-01

The increasing reliability of parallel systems and clusters of computers has resulted in these systems becoming more attractive for true production workloads. Today, the primary obstacle to production use of clusters of computers is the lack of a functional and robust Job Management System for parallel applications. This document provides a checklist of NAS requirements for job queuing and scheduling in order to make most efficient use of parallel systems and clusters for parallel applications. Future requirements are also identified to assist software vendors with design planning.

The need for scientific software engineering in the pharmaceutical industry

NASA Astrophysics Data System (ADS)

Luty, Brock; Rose, Peter W.

2017-03-01

Scientific software engineering is a distinct discipline from both computational chemistry project support and research informatics. A scientific software engineer not only has a deep understanding of the science of drug discovery but also the desire, skills and time to apply good software engineering practices. A good team of scientific software engineers can create a software foundation that is maintainable, validated and robust. If done correctly, this foundation enable the organization to investigate new and novel computational ideas with a very high level of efficiency.

The need for scientific software engineering in the pharmaceutical industry.

PubMed

Luty, Brock; Rose, Peter W

2017-03-01

Scientific software engineering is a distinct discipline from both computational chemistry project support and research informatics. A scientific software engineer not only has a deep understanding of the science of drug discovery but also the desire, skills and time to apply good software engineering practices. A good team of scientific software engineers can create a software foundation that is maintainable, validated and robust. If done correctly, this foundation enable the organization to investigate new and novel computational ideas with a very high level of efficiency.

Advances in Computational Capabilities for Hypersonic Flows

NASA Technical Reports Server (NTRS)

Kumar, Ajay; Gnoffo, Peter A.; Moss, James N.; Drummond, J. Philip

1997-01-01

The paper reviews the growth and advances in computational capabilities for hypersonic applications over the period from the mid-1980's to the present day. The current status of the code development issues such as surface and field grid generation, algorithms, physical and chemical modeling, and validation is provided. A brief description of some of the major codes being used at NASA Langley Research Center for hypersonic continuum and rarefied flows is provided, along with their capabilities and deficiencies. A number of application examples are presented, and future areas of research to enhance accuracy, reliability, efficiency, and robustness of computational codes are discussed.

A Generalized Method for the Comparable and Rigorous Calculation of the Polytropic Efficiencies of Turbocompressors

NASA Astrophysics Data System (ADS)

Dimitrakopoulos, Panagiotis

2018-03-01

The calculation of polytropic efficiencies is a very important task, especially during the development of new compression units, like compressor impellers, stages and stage groups. Such calculations are also crucial for the determination of the performance of a whole compressor. As processors and computational capacities have substantially been improved in the last years, the need for a new, rigorous, robust, accurate and at the same time standardized method merged, regarding the computation of the polytropic efficiencies, especially based on thermodynamics of real gases. The proposed method is based on the rigorous definition of the polytropic efficiency. The input consists of pressure and temperature values at the end points of the compression path (suction and discharge), for a given working fluid. The average relative error for the studied cases was 0.536 %. Thus, this high-accuracy method is proposed for efficiency calculations related with turbocompressors and their compression units, especially when they are operating at high power levels, for example in jet engines and high-power plants.

Accelerating global optimization of aerodynamic shapes using a new surrogate-assisted parallel genetic algorithm

NASA Astrophysics Data System (ADS)

Ebrahimi, Mehdi; Jahangirian, Alireza

2017-12-01

An efficient strategy is presented for global shape optimization of wing sections with a parallel genetic algorithm. Several computational techniques are applied to increase the convergence rate and the efficiency of the method. A variable fidelity computational evaluation method is applied in which the expensive Navier-Stokes flow solver is complemented by an inexpensive multi-layer perceptron neural network for the objective function evaluations. A population dispersion method that consists of two phases, of exploration and refinement, is developed to improve the convergence rate and the robustness of the genetic algorithm. Owing to the nature of the optimization problem, a parallel framework based on the master/slave approach is used. The outcomes indicate that the method is able to find the global optimum with significantly lower computational time in comparison to the conventional genetic algorithm.

FANSe2: a robust and cost-efficient alignment tool for quantitative next-generation sequencing applications.

PubMed

Xiao, Chuan-Le; Mai, Zhi-Biao; Lian, Xin-Lei; Zhong, Jia-Yong; Jin, Jing-Jie; He, Qing-Yu; Zhang, Gong

2014-01-01

Correct and bias-free interpretation of the deep sequencing data is inevitably dependent on the complete mapping of all mappable reads to the reference sequence, especially for quantitative RNA-seq applications. Seed-based algorithms are generally slow but robust, while Burrows-Wheeler Transform (BWT) based algorithms are fast but less robust. To have both advantages, we developed an algorithm FANSe2 with iterative mapping strategy based on the statistics of real-world sequencing error distribution to substantially accelerate the mapping without compromising the accuracy. Its sensitivity and accuracy are higher than the BWT-based algorithms in the tests using both prokaryotic and eukaryotic sequencing datasets. The gene identification results of FANSe2 is experimentally validated, while the previous algorithms have false positives and false negatives. FANSe2 showed remarkably better consistency to the microarray than most other algorithms in terms of gene expression quantifications. We implemented a scalable and almost maintenance-free parallelization method that can utilize the computational power of multiple office computers, a novel feature not present in any other mainstream algorithm. With three normal office computers, we demonstrated that FANSe2 mapped an RNA-seq dataset generated from an entire Illunima HiSeq 2000 flowcell (8 lanes, 608 M reads) to masked human genome within 4.1 hours with higher sensitivity than Bowtie/Bowtie2. FANSe2 thus provides robust accuracy, full indel sensitivity, fast speed, versatile compatibility and economical computational utilization, making it a useful and practical tool for deep sequencing applications. FANSe2 is freely available at http://bioinformatics.jnu.edu.cn/software/fanse2/.

On Improving Efficiency of Differential Evolution for Aerodynamic Shape Optimization Applications

NASA Technical Reports Server (NTRS)

Madavan, Nateri K.

2004-01-01

Differential Evolution (DE) is a simple and robust evolutionary strategy that has been proven effective in determining the global optimum for several difficult optimization problems. Although DE offers several advantages over traditional optimization approaches, its use in applications such as aerodynamic shape optimization where the objective function evaluations are computationally expensive is limited by the large number of function evaluations often required. In this paper various approaches for improving the efficiency of DE are reviewed and discussed. These approaches are implemented in a DE-based aerodynamic shape optimization method that uses a Navier-Stokes solver for the objective function evaluations. Parallelization techniques on distributed computers are used to reduce turnaround times. Results are presented for the inverse design of a turbine airfoil. The efficiency improvements achieved by the different approaches are evaluated and compared.

An efficient and robust method for predicting helicopter rotor high-speed impulsive noise

NASA Technical Reports Server (NTRS)

Brentner, Kenneth S.

1996-01-01

A new formulation for the Ffowcs Williams-Hawkings quadrupole source, which is valid for a far-field in-plane observer, is presented. The far-field approximation is new and unique in that no further approximation of the quadrupole source strength is made and integrands with r(exp -2) and r(exp -3) dependence are retained. This paper focuses on the development of a retarded-time formulation in which time derivatives are analytically taken inside the integrals to avoid unnecessary computational work when the observer moves with the rotor. The new quadrupole formulation is similar to Farassat's thickness and loading formulation 1A. Quadrupole noise prediction is carried out in two parts: a preprocessing stage in which the previously computed flow field is integrated in the direction normal to the rotor disk, and a noise computation stage in which quadrupole surface integrals are evaluated for a particular observer position. Preliminary predictions for hover and forward flight agree well with experimental data. The method is robust and requires computer resources comparable to thickness and loading noise prediction.

Constraint treatment techniques and parallel algorithms for multibody dynamic analysis. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Chiou, Jin-Chern

1990-01-01

Computational procedures for kinematic and dynamic analysis of three-dimensional multibody dynamic (MBD) systems are developed from the differential-algebraic equations (DAE's) viewpoint. Constraint violations during the time integration process are minimized and penalty constraint stabilization techniques and partitioning schemes are developed. The governing equations of motion, a two-stage staggered explicit-implicit numerical algorithm, are treated which takes advantage of a partitioned solution procedure. A robust and parallelizable integration algorithm is developed. This algorithm uses a two-stage staggered central difference algorithm to integrate the translational coordinates and the angular velocities. The angular orientations of bodies in MBD systems are then obtained by using an implicit algorithm via the kinematic relationship between Euler parameters and angular velocities. It is shown that the combination of the present solution procedures yields a computationally more accurate solution. To speed up the computational procedures, parallel implementation of the present constraint treatment techniques, the two-stage staggered explicit-implicit numerical algorithm was efficiently carried out. The DAE's and the constraint treatment techniques were transformed into arrowhead matrices to which Schur complement form was derived. By fully exploiting the sparse matrix structural analysis techniques, a parallel preconditioned conjugate gradient numerical algorithm is used to solve the systems equations written in Schur complement form. A software testbed was designed and implemented in both sequential and parallel computers. This testbed was used to demonstrate the robustness and efficiency of the constraint treatment techniques, the accuracy of the two-stage staggered explicit-implicit numerical algorithm, and the speed up of the Schur-complement-based parallel preconditioned conjugate gradient algorithm on a parallel computer.

Application of the MacCormack scheme to overland flow routing for high-spatial resolution distributed hydrological model

NASA Astrophysics Data System (ADS)

Zhang, Ling; Nan, Zhuotong; Liang, Xu; Xu, Yi; Hernández, Felipe; Li, Lianxia

2018-03-01

Although process-based distributed hydrological models (PDHMs) are evolving rapidly over the last few decades, their extensive applications are still challenged by the computational expenses. This study attempted, for the first time, to apply the numerically efficient MacCormack algorithm to overland flow routing in a representative high-spatial resolution PDHM, i.e., the distributed hydrology-soil-vegetation model (DHSVM), in order to improve its computational efficiency. The analytical verification indicates that both the semi and full versions of the MacCormack schemes exhibit robust numerical stability and are more computationally efficient than the conventional explicit linear scheme. The full-version outperforms the semi-version in terms of simulation accuracy when a same time step is adopted. The semi-MacCormack scheme was implemented into DHSVM (version 3.1.2) to solve the kinematic wave equations for overland flow routing. The performance and practicality of the enhanced DHSVM-MacCormack model was assessed by performing two groups of modeling experiments in the Mercer Creek watershed, a small urban catchment near Bellevue, Washington. The experiments show that DHSVM-MacCormack can considerably improve the computational efficiency without compromising the simulation accuracy of the original DHSVM model. More specifically, with the same computational environment and model settings, the computational time required by DHSVM-MacCormack can be reduced to several dozen minutes for a simulation period of three months (in contrast with one day and a half by the original DHSVM model) without noticeable sacrifice of the accuracy. The MacCormack scheme proves to be applicable to overland flow routing in DHSVM, which implies that it can be coupled into other PHDMs for watershed routing to either significantly improve their computational efficiency or to make the kinematic wave routing for high resolution modeling computational feasible.

Uncertainty quantification-based robust aerodynamic optimization of laminar flow nacelle

NASA Astrophysics Data System (ADS)

Xiong, Neng; Tao, Yang; Liu, Zhiyong; Lin, Jun

2018-05-01

The aerodynamic performance of laminar flow nacelle is highly sensitive to uncertain working conditions, especially the surface roughness. An efficient robust aerodynamic optimization method on the basis of non-deterministic computational fluid dynamic (CFD) simulation and Efficient Global Optimization (EGO)algorithm was employed. A non-intrusive polynomial chaos method is used in conjunction with an existing well-verified CFD module to quantify the uncertainty propagation in the flow field. This paper investigates the roughness modeling behavior with the γ-Ret shear stress transport model including modeling flow transition and surface roughness effects. The roughness effects are modeled to simulate sand grain roughness. A Class-Shape Transformation-based parametrical description of the nacelle contour as part of an automatic design evaluation process is presented. A Design-of-Experiments (DoE) was performed and surrogate model by Kriging method was built. The new design nacelle process demonstrates that significant improvements of both mean and variance of the efficiency are achieved and the proposed method can be applied to laminar flow nacelle design successfully.

Aerodynamic design applying automatic differentiation and using robust variable fidelity optimization

NASA Astrophysics Data System (ADS)

Takemiya, Tetsushi

In modern aerospace engineering, the physics-based computational design method is becoming more important, as it is more efficient than experiments and because it is more suitable in designing new types of aircraft (e.g., unmanned aerial vehicles or supersonic business jets) than the conventional design method, which heavily relies on historical data. To enhance the reliability of the physics-based computational design method, researchers have made tremendous efforts to improve the fidelity of models. However, high-fidelity models require longer computational time, so the advantage of efficiency is partially lost. This problem has been overcome with the development of variable fidelity optimization (VFO). In VFO, different fidelity models are simultaneously employed in order to improve the speed and the accuracy of convergence in an optimization process. Among the various types of VFO methods, one of the most promising methods is the approximation management framework (AMF). In the AMF, objective and constraint functions of a low-fidelity model are scaled at a design point so that the scaled functions, which are referred to as "surrogate functions," match those of a high-fidelity model. Since scaling functions and the low-fidelity model constitutes surrogate functions, evaluating the surrogate functions is faster than evaluating the high-fidelity model. Therefore, in the optimization process, in which gradient-based optimization is implemented and thus many function calls are required, the surrogate functions are used instead of the high-fidelity model to obtain a new design point. The best feature of the AMF is that it may converge to a local optimum of the high-fidelity model in much less computational time than the high-fidelity model. However, through literature surveys and implementations of the AMF, the author xx found that (1) the AMF is very vulnerable when the computational analysis models have numerical noise, which is very common in high-fidelity models, and that (2) the AMF terminates optimization erroneously when the optimization problems have constraints. The first problem is due to inaccuracy in computing derivatives in the AMF, and the second problem is due to erroneous treatment of the trust region ratio, which sets the size of the domain for an optimization in the AMF. In order to solve the first problem of the AMF, automatic differentiation (AD) technique, which reads the codes of analysis models and automatically generates new derivative codes based on some mathematical rules, is applied. If derivatives are computed with the generated derivative code, they are analytical, and the required computational time is independent of the number of design variables, which is very advantageous for realistic aerospace engineering problems. However, if analysis models implement iterative computations such as computational fluid dynamics (CFD), which solves system partial differential equations iteratively, computing derivatives through the AD requires a massive memory size. The author solved this deficiency by modifying the AD approach and developing a more efficient implementation with CFD, and successfully applied the AD to general CFD software. In order to solve the second problem of the AMF, the governing equation of the trust region ratio, which is very strict against the violation of constraints, is modified so that it can accept the violation of constraints within some tolerance. By accepting violations of constraints during the optimization process, the AMF can continue optimization without terminating immaturely and eventually find the true optimum design point. With these modifications, the AMF is referred to as "Robust AMF," and it is applied to airfoil and wing aerodynamic design problems using Euler CFD software. The former problem has 21 design variables, and the latter 64. In both problems, derivatives computed with the proposed AD method are first compared with those computed with the finite differentiation (FD) method, and then, the Robust AMF is implemented along with the sequential quadratic programming (SQP) optimization method with only high-fidelity models. The proposed AD method computes derivatives more accurately and faster than the FD method, and the Robust AMF successfully optimizes shapes of the airfoil and the wing in a much shorter time than SQP with only high-fidelity models. These results clearly show the effectiveness of the Robust AMF. Finally, the feasibility of reducing computational time for calculating derivatives and the necessity of AMF with an optimum design point always in the feasible region are discussed as future work.

Structure Computation of Quiet Spike[Trademark] Flight-Test Data During Envelope Expansion

NASA Technical Reports Server (NTRS)

Kukreja, Sunil L.

2008-01-01

System identification or mathematical modeling is used in the aerospace community for development of simulation models for robust control law design. These models are often described as linear time-invariant processes. Nevertheless, it is well known that the underlying process is often nonlinear. The reason for using a linear approach has been due to the lack of a proper set of tools for the identification of nonlinear systems. Over the past several decades, the controls and biomedical communities have made great advances in developing tools for the identification of nonlinear systems. These approaches are robust and readily applicable to aerospace systems. In this paper, we show the application of one such nonlinear system identification technique, structure detection, for the analysis of F-15B Quiet Spike(TradeMark) aeroservoelastic flight-test data. Structure detection is concerned with the selection of a subset of candidate terms that best describe the observed output. This is a necessary procedure to compute an efficient system description that may afford greater insight into the functionality of the system or a simpler controller design. Structure computation as a tool for black-box modeling may be of critical importance for the development of robust parsimonious models for the flight-test community. Moreover, this approach may lead to efficient strategies for rapid envelope expansion, which may save significant development time and costs. The objectives of this study are to demonstrate via analysis of F-15B Quiet Spike aeroservoelastic flight-test data for several flight conditions that 1) linear models are inefficient for modeling aeroservoelastic data, 2) nonlinear identification provides a parsimonious model description while providing a high percent fit for cross-validated data, and 3) the model structure and parameters vary as the flight condition is altered.

A robust multilevel simultaneous eigenvalue solver

NASA Technical Reports Server (NTRS)

Costiner, Sorin; Taasan, Shlomo

1993-01-01

Multilevel (ML) algorithms for eigenvalue problems are often faced with several types of difficulties such as: the mixing of approximated eigenvectors by the solution process, the approximation of incomplete clusters of eigenvectors, the poor representation of solution on coarse levels, and the existence of close or equal eigenvalues. Algorithms that do not treat appropriately these difficulties usually fail, or their performance degrades when facing them. These issues motivated the development of a robust adaptive ML algorithm which treats these difficulties, for the calculation of a few eigenvectors and their corresponding eigenvalues. The main techniques used in the new algorithm include: the adaptive completion and separation of the relevant clusters on different levels, the simultaneous treatment of solutions within each cluster, and the robustness tests which monitor the algorithm's efficiency and convergence. The eigenvectors' separation efficiency is based on a new ML projection technique generalizing the Rayleigh Ritz projection, combined with a technique, the backrotations. These separation techniques, when combined with an FMG formulation, in many cases lead to algorithms of O(qN) complexity, for q eigenvectors of size N on the finest level. Previously developed ML algorithms are less focused on the mentioned difficulties. Moreover, algorithms which employ fine level separation techniques are of O(q(sub 2)N) complexity and usually do not overcome all these difficulties. Computational examples are presented where Schrodinger type eigenvalue problems in 2-D and 3-D, having equal and closely clustered eigenvalues, are solved with the efficiency of the Poisson multigrid solver. A second order approximation is obtained in O(qN) work, where the total computational work is equivalent to only a few fine level relaxations per eigenvector.

Towards developing robust algorithms for solving partial differential equations on MIMD machines

NASA Technical Reports Server (NTRS)

Saltz, Joel H.; Naik, Vijay K.

1988-01-01

Methods for efficient computation of numerical algorithms on a wide variety of MIMD machines are proposed. These techniques reorganize the data dependency patterns to improve the processor utilization. The model problem finds the time-accurate solution to a parabolic partial differential equation discretized in space and implicitly marched forward in time. The algorithms are extensions of Jacobi and SOR. The extensions consist of iterating over a window of several timesteps, allowing efficient overlap of computation with communication. The methods increase the degree to which work can be performed while data are communicated between processors. The effect of the window size and of domain partitioning on the system performance is examined both by implementing the algorithm on a simulated multiprocessor system.

Towards developing robust algorithms for solving partial differential equations on MIMD machines

NASA Technical Reports Server (NTRS)

Saltz, J. H.; Naik, V. K.

1985-01-01

Methods for efficient computation of numerical algorithms on a wide variety of MIMD machines are proposed. These techniques reorganize the data dependency patterns to improve the processor utilization. The model problem finds the time-accurate solution to a parabolic partial differential equation discretized in space and implicitly marched forward in time. The algorithms are extensions of Jacobi and SOR. The extensions consist of iterating over a window of several timesteps, allowing efficient overlap of computation with communication. The methods increase the degree to which work can be performed while data are communicated between processors. The effect of the window size and of domain partitioning on the system performance is examined both by implementing the algorithm on a simulated multiprocessor system.

Approach for Uncertainty Propagation and Robust Design in CFD Using Sensitivity Derivatives

NASA Technical Reports Server (NTRS)

Putko, Michele M.; Newman, Perry A.; Taylor, Arthur C., III; Green, Lawrence L.

2001-01-01

This paper presents an implementation of the approximate statistical moment method for uncertainty propagation and robust optimization for a quasi 1-D Euler CFD (computational fluid dynamics) code. Given uncertainties in statistically independent, random, normally distributed input variables, a first- and second-order statistical moment matching procedure is performed to approximate the uncertainty in the CFD output. Efficient calculation of both first- and second-order sensitivity derivatives is required. In order to assess the validity of the approximations, the moments are compared with statistical moments generated through Monte Carlo simulations. The uncertainties in the CFD input variables are also incorporated into a robust optimization procedure. For this optimization, statistical moments involving first-order sensitivity derivatives appear in the objective function and system constraints. Second-order sensitivity derivatives are used in a gradient-based search to successfully execute a robust optimization. The approximate methods used throughout the analyses are found to be valid when considering robustness about input parameter mean values.

Robust Vision-Based Pose Estimation Algorithm for AN Uav with Known Gravity Vector

NASA Astrophysics Data System (ADS)

Kniaz, V. V.

2016-06-01

Accurate estimation of camera external orientation with respect to a known object is one of the central problems in photogrammetry and computer vision. In recent years this problem is gaining an increasing attention in the field of UAV autonomous flight. Such application requires a real-time performance and robustness of the external orientation estimation algorithm. The accuracy of the solution is strongly dependent on the number of reference points visible on the given image. The problem only has an analytical solution if 3 or more reference points are visible. However, in limited visibility conditions it is often needed to perform external orientation with only 2 visible reference points. In such case the solution could be found if the gravity vector direction in the camera coordinate system is known. A number of algorithms for external orientation estimation for the case of 2 known reference points and a gravity vector were developed to date. Most of these algorithms provide analytical solution in the form of polynomial equation that is subject to large errors in the case of complex reference points configurations. This paper is focused on the development of a new computationally effective and robust algorithm for external orientation based on positions of 2 known reference points and a gravity vector. The algorithm implementation for guidance of a Parrot AR.Drone 2.0 micro-UAV is discussed. The experimental evaluation of the algorithm proved its computational efficiency and robustness against errors in reference points positions and complex configurations.

Combining the AFLOW GIBBS and elastic libraries to efficiently and robustly screen thermomechanical properties of solids

NASA Astrophysics Data System (ADS)

Toher, Cormac; Oses, Corey; Plata, Jose J.; Hicks, David; Rose, Frisco; Levy, Ohad; de Jong, Maarten; Asta, Mark; Fornari, Marco; Buongiorno Nardelli, Marco; Curtarolo, Stefano

2017-06-01

Thorough characterization of the thermomechanical properties of materials requires difficult and time-consuming experiments. This severely limits the availability of data and is one of the main obstacles for the development of effective accelerated materials design strategies. The rapid screening of new potential materials requires highly integrated, sophisticated, and robust computational approaches. We tackled the challenge by developing an automated, integrated workflow with robust error-correction within the AFLOW framework which combines the newly developed "Automatic Elasticity Library" with the previously implemented GIBBS method. The first extracts the mechanical properties from automatic self-consistent stress-strain calculations, while the latter employs those mechanical properties to evaluate the thermodynamics within the Debye model. This new thermoelastic workflow is benchmarked against a set of 74 experimentally characterized systems to pinpoint a robust computational methodology for the evaluation of bulk and shear moduli, Poisson ratios, Debye temperatures, Grüneisen parameters, and thermal conductivities of a wide variety of materials. The effect of different choices of equations of state and exchange-correlation functionals is examined and the optimum combination of properties for the Leibfried-Schlömann prediction of thermal conductivity is identified, leading to improved agreement with experimental results than the GIBBS-only approach. The framework has been applied to the AFLOW.org data repositories to compute the thermoelastic properties of over 3500 unique materials. The results are now available online by using an expanded version of the REST-API described in the Appendix.

Lattice Boltzmann Method for 3-D Flows with Curved Boundary

NASA Technical Reports Server (NTRS)

Mei, Renwei; Shyy, Wei; Yu, Dazhi; Luo, Li-Shi

2002-01-01

In this work, we investigate two issues that are important to computational efficiency and reliability in fluid dynamics applications of the lattice, Boltzmann equation (LBE): (1) Computational stability and accuracy of different lattice Boltzmann models and (2) the treatment of the boundary conditions on curved solid boundaries and their 3-D implementations. Three athermal 3-D LBE models (D3QI5, D3Ql9, and D3Q27) are studied and compared in terms of efficiency, accuracy, and robustness. The boundary treatment recently developed by Filippova and Hanel and Met et al. in 2-D is extended to and implemented for 3-D. The convergence, stability, and computational efficiency of the 3-D LBE models with the boundary treatment for curved boundaries were tested in simulations of four 3-D flows: (1) Fully developed flows in a square duct, (2) flow in a 3-D lid-driven cavity, (3) fully developed flows in a circular pipe, and (4) a uniform flow over a sphere. We found that while the fifteen-velocity 3-D (D3Ql5) model is more prone to numerical instability and the D3Q27 is more computationally intensive, the 63Q19 model provides a balance between computational reliability and efficiency. Through numerical simulations, we demonstrated that the boundary treatment for 3-D arbitrary curved geometry has second-order accuracy and possesses satisfactory stability characteristics.

A closed-form solution to tensor voting: theory and applications.

PubMed

Wu, Tai-Pang; Yeung, Sai-Kit; Jia, Jiaya; Tang, Chi-Keung; Medioni, Gérard

2012-08-01

We prove a closed-form solution to tensor voting (CFTV): Given a point set in any dimensions, our closed-form solution provides an exact, continuous, and efficient algorithm for computing a structure-aware tensor that simultaneously achieves salient structure detection and outlier attenuation. Using CFTV, we prove the convergence of tensor voting on a Markov random field (MRF), thus termed as MRFTV, where the structure-aware tensor at each input site reaches a stationary state upon convergence in structure propagation. We then embed structure-aware tensor into expectation maximization (EM) for optimizing a single linear structure to achieve efficient and robust parameter estimation. Specifically, our EMTV algorithm optimizes both the tensor and fitting parameters and does not require random sampling consensus typically used in existing robust statistical techniques. We performed quantitative evaluation on its accuracy and robustness, showing that EMTV performs better than the original TV and other state-of-the-art techniques in fundamental matrix estimation for multiview stereo matching. The extensions of CFTV and EMTV for extracting multiple and nonlinear structures are underway.

High-efficiency multiphoton boson sampling

NASA Astrophysics Data System (ADS)

Wang, Hui; He, Yu; Li, Yu-Huai; Su, Zu-En; Li, Bo; Huang, He-Liang; Ding, Xing; Chen, Ming-Cheng; Liu, Chang; Qin, Jian; Li, Jin-Peng; He, Yu-Ming; Schneider, Christian; Kamp, Martin; Peng, Cheng-Zhi; Höfling, Sven; Lu, Chao-Yang; Pan, Jian-Wei

2017-06-01

Boson sampling is considered as a strong candidate to demonstrate 'quantum computational supremacy' over classical computers. However, previous proof-of-principle experiments suffered from small photon number and low sampling rates owing to the inefficiencies of the single-photon sources and multiport optical interferometers. Here, we develop two central components for high-performance boson sampling: robust multiphoton interferometers with 99% transmission rate and actively demultiplexed single-photon sources based on a quantum dot-micropillar with simultaneously high efficiency, purity and indistinguishability. We implement and validate three-, four- and five-photon boson sampling, and achieve sampling rates of 4.96 kHz, 151 Hz and 4 Hz, respectively, which are over 24,000 times faster than previous experiments. Our architecture can be scaled up for a larger number of photons and with higher sampling rates to compete with classical computers, and might provide experimental evidence against the extended Church-Turing thesis.

Design and Development of ChemInfoCloud: An Integrated Cloud Enabled Platform for Virtual Screening.

PubMed

Karthikeyan, Muthukumarasamy; Pandit, Deepak; Bhavasar, Arvind; Vyas, Renu

2015-01-01

The power of cloud computing and distributed computing has been harnessed to handle vast and heterogeneous data required to be processed in any virtual screening protocol. A cloud computing platorm ChemInfoCloud was built and integrated with several chemoinformatics and bioinformatics tools. The robust engine performs the core chemoinformatics tasks of lead generation, lead optimisation and property prediction in a fast and efficient manner. It has also been provided with some of the bioinformatics functionalities including sequence alignment, active site pose prediction and protein ligand docking. Text mining, NMR chemical shift (1H, 13C) prediction and reaction fingerprint generation modules for efficient lead discovery are also implemented in this platform. We have developed an integrated problem solving cloud environment for virtual screening studies that also provides workflow management, better usability and interaction with end users using container based virtualization, OpenVz.

Seismic data restoration with a fast L1 norm trust region method

NASA Astrophysics Data System (ADS)

Cao, Jingjie; Wang, Yanfei

2014-08-01

Seismic data restoration is a major strategy to provide reliable wavefield when field data dissatisfy the Shannon sampling theorem. Recovery by sparsity-promoting inversion often get sparse solutions of seismic data in a transformed domains, however, most methods for sparsity-promoting inversion are line-searching methods which are efficient but are inclined to obtain local solutions. Using trust region method which can provide globally convergent solutions is a good choice to overcome this shortcoming. A trust region method for sparse inversion has been proposed, however, the efficiency should be improved to suitable for large-scale computation. In this paper, a new L1 norm trust region model is proposed for seismic data restoration and a robust gradient projection method for solving the sub-problem is utilized. Numerical results of synthetic and field data demonstrate that the proposed trust region method can get excellent computation speed and is a viable alternative for large-scale computation.

An online hybrid brain-computer interface combining multiple physiological signals for webpage browse.

PubMed

Long Chen; Zhongpeng Wang; Feng He; Jiajia Yang; Hongzhi Qi; Peng Zhou; Baikun Wan; Dong Ming

2015-08-01

The hybrid brain computer interface (hBCI) could provide higher information transfer rate than did the classical BCIs. It included more than one brain-computer or human-machine interact paradigms, such as the combination of the P300 and SSVEP paradigms. Research firstly constructed independent subsystems of three different paradigms and tested each of them with online experiments. Then we constructed a serial hybrid BCI system which combined these paradigms to achieve the functions of typing letters, moving and clicking cursor, and switching among them for the purpose of browsing webpages. Five subjects were involved in this study. They all successfully realized these functions in the online tests. The subjects could achieve an accuracy above 90% after training, which met the requirement in operating the system efficiently. The results demonstrated that it was an efficient system capable of robustness, which provided an approach for the clinic application.

Overcoming Geometry-Induced Stiffness with IMplicit-Explicit (IMEX) Runge-Kutta Algorithms on Unstructured Grids with Applications to CEM, CFD, and CAA

NASA Technical Reports Server (NTRS)

Kanevsky, Alex

2004-01-01

My goal is to develop and implement efficient, accurate, and robust Implicit-Explicit Runge-Kutta (IMEX RK) methods [9] for overcoming geometry-induced stiffness with applications to computational electromagnetics (CEM), computational fluid dynamics (CFD) and computational aeroacoustics (CAA). IMEX algorithms solve the non-stiff portions of the domain using explicit methods, and isolate and solve the more expensive stiff portions using implicit methods. Current algorithms in CEM can only simulate purely harmonic (up to lOGHz plane wave) EM scattering by fighter aircraft, which are assumed to be pure metallic shells, and cannot handle the inclusion of coatings, penetration into and radiation out of the aircraft. Efficient MEX RK methods could potentially increase current CEM capabilities by 1-2 orders of magnitude, allowing scientists and engineers to attack more challenging and realistic problems.

On Improving Efficiency of Differential Evolution for Aerodynamic Shape Optimization Applications

NASA Technical Reports Server (NTRS)

Madavan, Nateri K.

2004-01-01

Differential Evolution (DE) is a simple and robust evolutionary strategy that has been provEn effective in determining the global optimum for several difficult optimization problems. Although DE offers several advantages over traditional optimization approaches, its use in applications such as aerodynamic shape optimization where the objective function evaluations are computationally expensive is limited by the large number of function evaluations often required. In this paper various approaches for improving the efficiency of DE are reviewed and discussed. Several approaches that have proven effective for other evolutionary algorithms are modified and implemented in a DE-based aerodynamic shape optimization method that uses a Navier-Stokes solver for the objective function evaluations. Parallelization techniques on distributed computers are used to reduce turnaround times. Results are presented for standard test optimization problems and for the inverse design of a turbine airfoil. The efficiency improvements achieved by the different approaches are evaluated and compared.

A generic efficient adaptive grid scheme for rocket propulsion modeling

NASA Technical Reports Server (NTRS)

Mo, J. D.; Chow, Alan S.

1993-01-01

The objective of this research is to develop an efficient, time-accurate numerical algorithm to discretize the Navier-Stokes equations for the predictions of internal one-, two-dimensional and axisymmetric flows. A generic, efficient, elliptic adaptive grid generator is implicitly coupled with the Lower-Upper factorization scheme in the development of ALUNS computer code. The calculations of one-dimensional shock tube wave propagation and two-dimensional shock wave capture, wave-wave interactions, shock wave-boundary interactions show that the developed scheme is stable, accurate and extremely robust. The adaptive grid generator produced a very favorable grid network by a grid speed technique. This generic adaptive grid generator is also applied in the PARC and FDNS codes and the computational results for solid rocket nozzle flowfield and crystal growth modeling by those codes will be presented in the conference, too. This research work is being supported by NASA/MSFC.

Meshfree and efficient modeling of swimming cells

NASA Astrophysics Data System (ADS)

Gallagher, Meurig T.; Smith, David J.

2018-05-01

Locomotion in Stokes flow is an intensively studied problem because it describes important biological phenomena such as the motility of many species' sperm, bacteria, algae, and protozoa. Numerical computations can be challenging, particularly in three dimensions, due to the presence of moving boundaries and complex geometries; methods which combine ease of implementation and computational efficiency are therefore needed. A recently proposed method to discretize the regularized Stokeslet boundary integral equation without the need for a connected mesh is applied to the inertialess locomotion problem in Stokes flow. The mathematical formulation and key aspects of the computational implementation in matlab® or GNU Octave are described, followed by numerical experiments with biflagellate algae and multiple uniflagellate sperm swimming between no-slip surfaces, for which both swimming trajectories and flow fields are calculated. These computational experiments required minutes of time on modest hardware; an extensible implementation is provided in a GitHub repository. The nearest-neighbor discretization dramatically improves convergence and robustness, a key challenge in extending the regularized Stokeslet method to complicated three-dimensional biological fluid problems.

Designing Robust and Resilient Tactical MANETs

DTIC Science & Technology

2014-09-25

Bounds on the Throughput Efficiency of Greedy Maximal Scheduling in Wireless Networks , IEEE/ACM Transactions on Networking , (06 2011): 0. doi: N... Wireless Sensor Networks and Effects of Long Range Dependant Data, Special IWSM Issue of Sequential Analysis, (11 2012): 0. doi: A. D. Dominguez...Bushnell, R. Poovendran. A Convex Optimization Approach for Clone Detection in Wireless Sensor Networks , Pervasive and Mobile Computing, (01 2012

On-line Bayesian model updating for structural health monitoring

NASA Astrophysics Data System (ADS)

Rocchetta, Roberto; Broggi, Matteo; Huchet, Quentin; Patelli, Edoardo

2018-03-01

Fatigue induced cracks is a dangerous failure mechanism which affects mechanical components subject to alternating load cycles. System health monitoring should be adopted to identify cracks which can jeopardise the structure. Real-time damage detection may fail in the identification of the cracks due to different sources of uncertainty which have been poorly assessed or even fully neglected. In this paper, a novel efficient and robust procedure is used for the detection of cracks locations and lengths in mechanical components. A Bayesian model updating framework is employed, which allows accounting for relevant sources of uncertainty. The idea underpinning the approach is to identify the most probable crack consistent with the experimental measurements. To tackle the computational cost of the Bayesian approach an emulator is adopted for replacing the computationally costly Finite Element model. To improve the overall robustness of the procedure, different numerical likelihoods, measurement noises and imprecision in the value of model parameters are analysed and their effects quantified. The accuracy of the stochastic updating and the efficiency of the numerical procedure are discussed. An experimental aluminium frame and on a numerical model of a typical car suspension arm are used to demonstrate the applicability of the approach.

Background recovery via motion-based robust principal component analysis with matrix factorization

NASA Astrophysics Data System (ADS)

Pan, Peng; Wang, Yongli; Zhou, Mingyuan; Sun, Zhipeng; He, Guoping

2018-03-01

Background recovery is a key technique in video analysis, but it still suffers from many challenges, such as camouflage, lighting changes, and diverse types of image noise. Robust principal component analysis (RPCA), which aims to recover a low-rank matrix and a sparse matrix, is a general framework for background recovery. The nuclear norm is widely used as a convex surrogate for the rank function in RPCA, which requires computing the singular value decomposition (SVD), a task that is increasingly costly as matrix sizes and ranks increase. However, matrix factorization greatly reduces the dimension of the matrix for which the SVD must be computed. Motion information has been shown to improve low-rank matrix recovery in RPCA, but this method still finds it difficult to handle original video data sets because of its batch-mode formulation and implementation. Hence, in this paper, we propose a motion-assisted RPCA model with matrix factorization (FM-RPCA) for background recovery. Moreover, an efficient linear alternating direction method of multipliers with a matrix factorization (FL-ADM) algorithm is designed for solving the proposed FM-RPCA model. Experimental results illustrate that the method provides stable results and is more efficient than the current state-of-the-art algorithms.

Application of augmented-Lagrangian methods in meteorology: Comparison of different conjugate-gradient codes for large-scale minimization

NASA Technical Reports Server (NTRS)

Navon, I. M.

1984-01-01

A Lagrange multiplier method using techniques developed by Bertsekas (1982) was applied to solving the problem of enforcing simultaneous conservation of the nonlinear integral invariants of the shallow water equations on a limited area domain. This application of nonlinear constrained optimization is of the large dimensional type and the conjugate gradient method was found to be the only computationally viable method for the unconstrained minimization. Several conjugate-gradient codes were tested and compared for increasing accuracy requirements. Robustness and computational efficiency were the principal criteria.

ROBNCA: robust network component analysis for recovering transcription factor activities.

PubMed

Noor, Amina; Ahmad, Aitzaz; Serpedin, Erchin; Nounou, Mohamed; Nounou, Hazem

2013-10-01

Network component analysis (NCA) is an efficient method of reconstructing the transcription factor activity (TFA), which makes use of the gene expression data and prior information available about transcription factor (TF)-gene regulations. Most of the contemporary algorithms either exhibit the drawback of inconsistency and poor reliability, or suffer from prohibitive computational complexity. In addition, the existing algorithms do not possess the ability to counteract the presence of outliers in the microarray data. Hence, robust and computationally efficient algorithms are needed to enable practical applications. We propose ROBust Network Component Analysis (ROBNCA), a novel iterative algorithm that explicitly models the possible outliers in the microarray data. An attractive feature of the ROBNCA algorithm is the derivation of a closed form solution for estimating the connectivity matrix, which was not available in prior contributions. The ROBNCA algorithm is compared with FastNCA and the non-iterative NCA (NI-NCA). ROBNCA estimates the TF activity profiles as well as the TF-gene control strength matrix with a much higher degree of accuracy than FastNCA and NI-NCA, irrespective of varying noise, correlation and/or amount of outliers in case of synthetic data. The ROBNCA algorithm is also tested on Saccharomyces cerevisiae data and Escherichia coli data, and it is observed to outperform the existing algorithms. The run time of the ROBNCA algorithm is comparable with that of FastNCA, and is hundreds of times faster than NI-NCA. The ROBNCA software is available at http://people.tamu.edu/∼amina/ROBNCA

Efficient super-resolution image reconstruction applied to surveillance video captured by small unmanned aircraft systems

NASA Astrophysics Data System (ADS)

He, Qiang; Schultz, Richard R.; Chu, Chee-Hung Henry

2008-04-01

The concept surrounding super-resolution image reconstruction is to recover a highly-resolved image from a series of low-resolution images via between-frame subpixel image registration. In this paper, we propose a novel and efficient super-resolution algorithm, and then apply it to the reconstruction of real video data captured by a small Unmanned Aircraft System (UAS). Small UAS aircraft generally have a wingspan of less than four meters, so that these vehicles and their payloads can be buffeted by even light winds, resulting in potentially unstable video. This algorithm is based on a coarse-to-fine strategy, in which a coarsely super-resolved image sequence is first built from the original video data by image registration and bi-cubic interpolation between a fixed reference frame and every additional frame. It is well known that the median filter is robust to outliers. If we calculate pixel-wise medians in the coarsely super-resolved image sequence, we can restore a refined super-resolved image. The primary advantage is that this is a noniterative algorithm, unlike traditional approaches based on highly-computational iterative algorithms. Experimental results show that our coarse-to-fine super-resolution algorithm is not only robust, but also very efficient. In comparison with five well-known super-resolution algorithms, namely the robust super-resolution algorithm, bi-cubic interpolation, projection onto convex sets (POCS), the Papoulis-Gerchberg algorithm, and the iterated back projection algorithm, our proposed algorithm gives both strong efficiency and robustness, as well as good visual performance. This is particularly useful for the application of super-resolution to UAS surveillance video, where real-time processing is highly desired.

A novel composite adaptive flap controller design by a high-efficient modified differential evolution identification approach.

PubMed

Li, Nailu; Mu, Anle; Yang, Xiyun; Magar, Kaman T; Liu, Chao

2018-05-01

The optimal tuning of adaptive flap controller can improve adaptive flap control performance on uncertain operating environments, but the optimization process is usually time-consuming and it is difficult to design proper optimal tuning strategy for the flap control system (FCS). To solve this problem, a novel adaptive flap controller is designed based on a high-efficient differential evolution (DE) identification technique and composite adaptive internal model control (CAIMC) strategy. The optimal tuning can be easily obtained by DE identified inverse of the FCS via CAIMC structure. To achieve fast tuning, a high-efficient modified adaptive DE algorithm is proposed with new mutant operator and varying range adaptive mechanism for the FCS identification. A tradeoff between optimized adaptive flap control and low computation cost is successfully achieved by proposed controller. Simulation results show the robustness of proposed method and its superiority to conventional adaptive IMC (AIMC) flap controller and the CAIMC flap controllers using other DE algorithms on various uncertain operating conditions. The high computation efficiency of proposed controller is also verified based on the computation time on those operating cases. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

An efficient numerical model for multicomponent compressible flow in fractured porous media

NASA Astrophysics Data System (ADS)

Zidane, Ali; Firoozabadi, Abbas

2014-12-01

An efficient and accurate numerical model for multicomponent compressible single-phase flow in fractured media is presented. The discrete-fracture approach is used to model the fractures where the fracture entities are described explicitly in the computational domain. We use the concept of cross flow equilibrium in the fractures. This will allow large matrix elements in the neighborhood of the fractures and considerable speed up of the algorithm. We use an implicit finite volume (FV) scheme to solve the species mass balance equation in the fractures. This step avoids the use of Courant-Freidricks-Levy (CFL) condition and contributes to significant speed up of the code. The hybrid mixed finite element method (MFE) is used to solve for the velocity in both the matrix and the fractures coupled with the discontinuous Galerkin (DG) method to solve the species transport equations in the matrix. Four numerical examples are presented to demonstrate the robustness and efficiency of the proposed model. We show that the combination of the fracture cross-flow equilibrium and the implicit composition calculation in the fractures increase the computational speed 20-130 times in 2D. In 3D, one may expect even a higher computational efficiency.

Multiple feature fusion via covariance matrix for visual tracking

NASA Astrophysics Data System (ADS)

Jin, Zefenfen; Hou, Zhiqiang; Yu, Wangsheng; Wang, Xin; Sun, Hui

2018-04-01

Aiming at the problem of complicated dynamic scenes in visual target tracking, a multi-feature fusion tracking algorithm based on covariance matrix is proposed to improve the robustness of the tracking algorithm. In the frame-work of quantum genetic algorithm, this paper uses the region covariance descriptor to fuse the color, edge and texture features. It also uses a fast covariance intersection algorithm to update the model. The low dimension of region covariance descriptor, the fast convergence speed and strong global optimization ability of quantum genetic algorithm, and the fast computation of fast covariance intersection algorithm are used to improve the computational efficiency of fusion, matching, and updating process, so that the algorithm achieves a fast and effective multi-feature fusion tracking. The experiments prove that the proposed algorithm can not only achieve fast and robust tracking but also effectively handle interference of occlusion, rotation, deformation, motion blur and so on.

Robust Operation of Soft Open Points in Active Distribution Networks with High Penetration of Photovoltaic Integration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ding, Fei; Ji, Haoran; Wang, Chengshan

Distributed generators (DGs) including photovoltaic panels (PVs) have been integrated dramatically in active distribution networks (ADNs). Due to the strong volatility and uncertainty, the high penetration of PV generation immensely exacerbates the conditions of voltage violation in ADNs. However, the emerging flexible interconnection technology based on soft open points (SOPs) provides increased controllability and flexibility to the system operation. For fully exploiting the regulation ability of SOPs to address the problems caused by PV, this paper proposes a robust optimization method to achieve the robust optimal operation of SOPs in ADNs. A two-stage adjustable robust optimization model is built tomore » tackle the uncertainties of PV outputs, in which robust operation strategies of SOPs are generated to eliminate the voltage violations and reduce the power losses of ADNs. A column-and-constraint generation (C&CG) algorithm is developed to solve the proposed robust optimization model, which are formulated as second-order cone program (SOCP) to facilitate the accuracy and computation efficiency. Case studies on the modified IEEE 33-node system and comparisons with the deterministic optimization approach are conducted to verify the effectiveness and robustness of the proposed method.« less

Aeroelastic Model Structure Computation for Envelope Expansion

NASA Technical Reports Server (NTRS)

Kukreja, Sunil L.

2007-01-01

Structure detection is a procedure for selecting a subset of candidate terms, from a full model description, that best describes the observed output. This is a necessary procedure to compute an efficient system description which may afford greater insight into the functionality of the system or a simpler controller design. Structure computation as a tool for black-box modelling may be of critical importance in the development of robust, parsimonious models for the flight-test community. Moreover, this approach may lead to efficient strategies for rapid envelope expansion which may save significant development time and costs. In this study, a least absolute shrinkage and selection operator (LASSO) technique is investigated for computing efficient model descriptions of nonlinear aeroelastic systems. The LASSO minimises the residual sum of squares by the addition of an l(sub 1) penalty term on the parameter vector of the traditional 2 minimisation problem. Its use for structure detection is a natural extension of this constrained minimisation approach to pseudolinear regression problems which produces some model parameters that are exactly zero and, therefore, yields a parsimonious system description. Applicability of this technique for model structure computation for the F/A-18 Active Aeroelastic Wing using flight test data is shown for several flight conditions (Mach numbers) by identifying a parsimonious system description with a high percent fit for cross-validated data.

Efficient Computation of Closed-loop Frequency Response for Large Order Flexible Systems

NASA Technical Reports Server (NTRS)

Maghami, Peiman G.; Giesy, Daniel P.

1997-01-01

An efficient and robust computational scheme is given for the calculation of the frequency response function of a large order, flexible system implemented with a linear, time invariant control system. Advantage is taken of the highly structured sparsity of the system matrix of the plant based on a model of the structure using normal mode coordinates. The computational time per frequency point of the new computational scheme is a linear function of system size, a significant improvement over traditional, full-matrix techniques whose computational times per frequency point range from quadratic to cubic functions of system size. This permits the practical frequency domain analysis of systems of much larger order than by traditional, full-matrix techniques. Formulations are given for both open and closed loop loop systems. Numerical examples are presented showing the advantages of the present formulation over traditional approaches, both in speed and in accuracy. Using a model with 703 structural modes, a speed-up of almost two orders of magnitude was observed while accuracy improved by up to 5 decimal places.

Exploratory High-Fidelity Aerostructural Optimization Using an Efficient Monolithic Solution Method

NASA Astrophysics Data System (ADS)

Zhang, Jenmy Zimi

This thesis is motivated by the desire to discover fuel efficient aircraft concepts through exploratory design. An optimization methodology based on tightly integrated high-fidelity aerostructural analysis is proposed, which has the flexibility, robustness, and efficiency to contribute to this goal. The present aerostructural optimization methodology uses an integrated geometry parameterization and mesh movement strategy, which was initially proposed for aerodynamic shape optimization. This integrated approach provides the optimizer with a large amount of geometric freedom for conducting exploratory design, while allowing for efficient and robust mesh movement in the presence of substantial shape changes. In extending this approach to aerostructural optimization, this thesis has addressed a number of important challenges. A structural mesh deformation strategy has been introduced to translate consistently the shape changes described by the geometry parameterization to the structural model. A three-field formulation of the discrete steady aerostructural residual couples the mesh movement equations with the three-dimensional Euler equations and a linear structural analysis. Gradients needed for optimization are computed with a three-field coupled adjoint approach. A number of investigations have been conducted to demonstrate the suitability and accuracy of the present methodology for use in aerostructural optimization involving substantial shape changes. Robustness and efficiency in the coupled solution algorithms is crucial to the success of an exploratory optimization. This thesis therefore also focuses on the design of an effective monolithic solution algorithm for the proposed methodology. This involves using a Newton-Krylov method for the aerostructural analysis and a preconditioned Krylov subspace method for the coupled adjoint solution. Several aspects of the monolithic solution method have been investigated. These include appropriate strategies for scaling and matrix-vector product evaluation, as well as block preconditioning techniques that preserve the modularity between subproblems. The monolithic solution method is applied to problems with varying degrees of fluid-structural coupling, as well as a wing span optimization study. The monolithic solution algorithm typically requires 20%-70% less computing time than its partitioned counterpart. This advantage increases with increasing wing flexibility. The performance of the monolithic solution method is also much less sensitive to the choice of the solution parameter.

High-order computational fluid dynamics tools for aircraft design

PubMed Central

Wang, Z. J.

2014-01-01

Most forecasts predict an annual airline traffic growth rate between 4.5 and 5% in the foreseeable future. To sustain that growth, the environmental impact of aircraft cannot be ignored. Future aircraft must have much better fuel economy, dramatically less greenhouse gas emissions and noise, in addition to better performance. Many technical breakthroughs must take place to achieve the aggressive environmental goals set up by governments in North America and Europe. One of these breakthroughs will be physics-based, highly accurate and efficient computational fluid dynamics and aeroacoustics tools capable of predicting complex flows over the entire flight envelope and through an aircraft engine, and computing aircraft noise. Some of these flows are dominated by unsteady vortices of disparate scales, often highly turbulent, and they call for higher-order methods. As these tools will be integral components of a multi-disciplinary optimization environment, they must be efficient to impact design. Ultimately, the accuracy, efficiency, robustness, scalability and geometric flexibility will determine which methods will be adopted in the design process. This article explores these aspects and identifies pacing items. PMID:25024419

Discrete-time neural network for fast solving large linear L1 estimation problems and its application to image restoration.

PubMed

Xia, Youshen; Sun, Changyin; Zheng, Wei Xing

2012-05-01

There is growing interest in solving linear L1 estimation problems for sparsity of the solution and robustness against non-Gaussian noise. This paper proposes a discrete-time neural network which can calculate large linear L1 estimation problems fast. The proposed neural network has a fixed computational step length and is proved to be globally convergent to an optimal solution. Then, the proposed neural network is efficiently applied to image restoration. Numerical results show that the proposed neural network is not only efficient in solving degenerate problems resulting from the nonunique solutions of the linear L1 estimation problems but also needs much less computational time than the related algorithms in solving both linear L1 estimation and image restoration problems.

Dynamic optimization of distributed biological systems using robust and efficient numerical techniques.

PubMed

Vilas, Carlos; Balsa-Canto, Eva; García, Maria-Sonia G; Banga, Julio R; Alonso, Antonio A

2012-07-02

Systems biology allows the analysis of biological systems behavior under different conditions through in silico experimentation. The possibility of perturbing biological systems in different manners calls for the design of perturbations to achieve particular goals. Examples would include, the design of a chemical stimulation to maximize the amplitude of a given cellular signal or to achieve a desired pattern in pattern formation systems, etc. Such design problems can be mathematically formulated as dynamic optimization problems which are particularly challenging when the system is described by partial differential equations.This work addresses the numerical solution of such dynamic optimization problems for spatially distributed biological systems. The usual nonlinear and large scale nature of the mathematical models related to this class of systems and the presence of constraints on the optimization problems, impose a number of difficulties, such as the presence of suboptimal solutions, which call for robust and efficient numerical techniques. Here, the use of a control vector parameterization approach combined with efficient and robust hybrid global optimization methods and a reduced order model methodology is proposed. The capabilities of this strategy are illustrated considering the solution of a two challenging problems: bacterial chemotaxis and the FitzHugh-Nagumo model. In the process of chemotaxis the objective was to efficiently compute the time-varying optimal concentration of chemotractant in one of the spatial boundaries in order to achieve predefined cell distribution profiles. Results are in agreement with those previously published in the literature. The FitzHugh-Nagumo problem is also efficiently solved and it illustrates very well how dynamic optimization may be used to force a system to evolve from an undesired to a desired pattern with a reduced number of actuators. The presented methodology can be used for the efficient dynamic optimization of generic distributed biological systems.

Some Approaches Towards Constructing Optimally Efficient Multigrid Solvers for the Inviscid Flow Equations

NASA Technical Reports Server (NTRS)

Sidilkover, David

1997-01-01

Some important advances took place during the last several years in the development of genuinely multidimensional upwind schemes for the compressible Euler equations. In particular, a robust, high-resolution genuinely multidimensional scheme which can be used for any of the flow regimes computations was constructed. This paper summarizes briefly these developments and outlines the fundamental advantages of this approach.

Experimenting with galaxies

NASA Technical Reports Server (NTRS)

Miller, Richard H.

1992-01-01

A study to demonstrate how the dynamics of galaxies may be investigated through the creation of galaxies within a computer model is presented. The numerical technique for simulating galaxies is shown to be both highly efficient and highly robust. Consideration is given to the anatomy of a galaxy, the gravitational N-body problem, numerical approaches to the N-body problem, use of the Poisson equation, and the symplectic integrator.

Crux: Rapid Open Source Protein Tandem Mass Spectrometry Analysis

PubMed Central

2015-01-01

Efficiently and accurately analyzing big protein tandem mass spectrometry data sets requires robust software that incorporates state-of-the-art computational, machine learning, and statistical methods. The Crux mass spectrometry analysis software toolkit (http://cruxtoolkit.sourceforge.net) is an open source project that aims to provide users with a cross-platform suite of analysis tools for interpreting protein mass spectrometry data. PMID:25182276

A Simplified Guidance for Target Missiles Used in Ballistic Missile Defence Evaluation

NASA Astrophysics Data System (ADS)

Prabhakar, N.; Kumar, I. D.; Tata, S. K.; Vaithiyanathan, V.

2013-01-01

A simplified guidance scheme for the target missiles used in Ballistic Missile Defence is presented in this paper. The proposed method has two major components, a Ground Guidance Computation (GGC) and an In-Flight Guidance Computation. The GGC which runs on the ground uses a missile model to generate attitude history in pitch plane and computes launch azimuth of the missile to compensate for the effect of earth rotation. The vehicle follows the pre launch computed attitude (theta) history in pitch plane and also applies the course correction in azimuth plane based on its deviation from the pre launch computed azimuth plane. This scheme requires less computations and counters In-flight disturbances such as wind, gust etc. quite efficiently. The simulation results show that the proposed method provides the satisfactory performance and robustness.

Reliability based design optimization: Formulations and methodologies

NASA Astrophysics Data System (ADS)

Agarwal, Harish

Modern products ranging from simple components to complex systems should be designed to be optimal and reliable. The challenge of modern engineering is to ensure that manufacturing costs are reduced and design cycle times are minimized while achieving requirements for performance and reliability. If the market for the product is competitive, improved quality and reliability can generate very strong competitive advantages. Simulation based design plays an important role in designing almost any kind of automotive, aerospace, and consumer products under these competitive conditions. Single discipline simulations used for analysis are being coupled together to create complex coupled simulation tools. This investigation focuses on the development of efficient and robust methodologies for reliability based design optimization in a simulation based design environment. Original contributions of this research are the development of a novel efficient and robust unilevel methodology for reliability based design optimization, the development of an innovative decoupled reliability based design optimization methodology, the application of homotopy techniques in unilevel reliability based design optimization methodology, and the development of a new framework for reliability based design optimization under epistemic uncertainty. The unilevel methodology for reliability based design optimization is shown to be mathematically equivalent to the traditional nested formulation. Numerical test problems show that the unilevel methodology can reduce computational cost by at least 50% as compared to the nested approach. The decoupled reliability based design optimization methodology is an approximate technique to obtain consistent reliable designs at lesser computational expense. Test problems show that the methodology is computationally efficient compared to the nested approach. A framework for performing reliability based design optimization under epistemic uncertainty is also developed. A trust region managed sequential approximate optimization methodology is employed for this purpose. Results from numerical test studies indicate that the methodology can be used for performing design optimization under severe uncertainty.

A Nanotechnology-Ready Computing Scheme based on a Weakly Coupled Oscillator Network

NASA Astrophysics Data System (ADS)

Vodenicarevic, Damir; Locatelli, Nicolas; Abreu Araujo, Flavio; Grollier, Julie; Querlioz, Damien

2017-03-01

With conventional transistor technologies reaching their limits, alternative computing schemes based on novel technologies are currently gaining considerable interest. Notably, promising computing approaches have proposed to leverage the complex dynamics emerging in networks of coupled oscillators based on nanotechnologies. The physical implementation of such architectures remains a true challenge, however, as most proposed ideas are not robust to nanotechnology devices’ non-idealities. In this work, we propose and investigate the implementation of an oscillator-based architecture, which can be used to carry out pattern recognition tasks, and which is tailored to the specificities of nanotechnologies. This scheme relies on a weak coupling between oscillators, and does not require a fine tuning of the coupling values. After evaluating its reliability under the severe constraints associated to nanotechnologies, we explore the scalability of such an architecture, suggesting its potential to realize pattern recognition tasks using limited resources. We show that it is robust to issues like noise, variability and oscillator non-linearity. Defining network optimization design rules, we show that nano-oscillator networks could be used for efficient cognitive processing.

Plane Smoothers for Multiblock Grids: Computational Aspects

NASA Technical Reports Server (NTRS)

Llorente, Ignacio M.; Diskin, Boris; Melson, N. Duane

1999-01-01

Standard multigrid methods are not well suited for problems with anisotropic discrete operators, which can occur, for example, on grids that are stretched in order to resolve a boundary layer. One of the most efficient approaches to yield robust methods is the combination of standard coarsening with alternating-direction plane relaxation in the three dimensions. However, this approach may be difficult to implement in codes with multiblock structured grids because there may be no natural definition of global lines or planes. This inherent obstacle limits the range of an implicit smoother to only the portion of the computational domain in the current block. This report studies in detail, both numerically and analytically, the behavior of blockwise plane smoothers in order to provide guidance to engineers who use block-structured grids. The results obtained so far show alternating-direction plane smoothers to be very robust, even on multiblock grids. In common computational fluid dynamics multiblock simulations, where the number of subdomains crossed by the line of a strong anisotropy is low (up to four), textbook multigrid convergence rates can be obtained with a small overlap of cells between neighboring blocks.

A Nanotechnology-Ready Computing Scheme based on a Weakly Coupled Oscillator Network.

PubMed

Vodenicarevic, Damir; Locatelli, Nicolas; Abreu Araujo, Flavio; Grollier, Julie; Querlioz, Damien

2017-03-21

With conventional transistor technologies reaching their limits, alternative computing schemes based on novel technologies are currently gaining considerable interest. Notably, promising computing approaches have proposed to leverage the complex dynamics emerging in networks of coupled oscillators based on nanotechnologies. The physical implementation of such architectures remains a true challenge, however, as most proposed ideas are not robust to nanotechnology devices' non-idealities. In this work, we propose and investigate the implementation of an oscillator-based architecture, which can be used to carry out pattern recognition tasks, and which is tailored to the specificities of nanotechnologies. This scheme relies on a weak coupling between oscillators, and does not require a fine tuning of the coupling values. After evaluating its reliability under the severe constraints associated to nanotechnologies, we explore the scalability of such an architecture, suggesting its potential to realize pattern recognition tasks using limited resources. We show that it is robust to issues like noise, variability and oscillator non-linearity. Defining network optimization design rules, we show that nano-oscillator networks could be used for efficient cognitive processing.

n-D shape/texture optimal synthetic description and modeling by GEOGINE

NASA Astrophysics Data System (ADS)

Fiorini, Rodolfo A.; Dacquino, Gianfranco F.

2004-12-01

GEOGINE(GEOmetrical enGINE), a state-of-the-art OMG (Ontological Model Generator) based on n-D Tensor Invariants for multidimensional shape/texture optimal synthetic description and learning, is presented. Usually elementary geometric shape robust characterization, subjected to geometric transformation, on a rigorous mathematical level is a key problem in many computer applications in different interest areas. The past four decades have seen solutions almost based on the use of n-Dimensional Moment and Fourier descriptor invariants. The present paper introduces a new approach for automatic model generation based on n -Dimensional Tensor Invariants as formal dictionary. An ontological model is the kernel used for specifying ontologies so that how close an ontology can be from the real world depends on the possibilities offered by the ontological model. By this approach even chromatic information content can be easily and reliably decoupled from target geometric information and computed into robus colour shape parameter attributes. Main GEOGINEoperational advantages over previous approaches are: 1) Automated Model Generation, 2) Invariant Minimal Complete Set for computational efficiency, 3) Arbitrary Model Precision for robust object description.

A Nanotechnology-Ready Computing Scheme based on a Weakly Coupled Oscillator Network

PubMed Central

Vodenicarevic, Damir; Locatelli, Nicolas; Abreu Araujo, Flavio; Grollier, Julie; Querlioz, Damien

2017-01-01

With conventional transistor technologies reaching their limits, alternative computing schemes based on novel technologies are currently gaining considerable interest. Notably, promising computing approaches have proposed to leverage the complex dynamics emerging in networks of coupled oscillators based on nanotechnologies. The physical implementation of such architectures remains a true challenge, however, as most proposed ideas are not robust to nanotechnology devices’ non-idealities. In this work, we propose and investigate the implementation of an oscillator-based architecture, which can be used to carry out pattern recognition tasks, and which is tailored to the specificities of nanotechnologies. This scheme relies on a weak coupling between oscillators, and does not require a fine tuning of the coupling values. After evaluating its reliability under the severe constraints associated to nanotechnologies, we explore the scalability of such an architecture, suggesting its potential to realize pattern recognition tasks using limited resources. We show that it is robust to issues like noise, variability and oscillator non-linearity. Defining network optimization design rules, we show that nano-oscillator networks could be used for efficient cognitive processing. PMID:28322262

An efficient dynamic load balancing algorithm

NASA Astrophysics Data System (ADS)

Lagaros, Nikos D.

2014-01-01

In engineering problems, randomness and uncertainties are inherent. Robust design procedures, formulated in the framework of multi-objective optimization, have been proposed in order to take into account sources of randomness and uncertainty. These design procedures require orders of magnitude more computational effort than conventional analysis or optimum design processes since a very large number of finite element analyses is required to be dealt. It is therefore an imperative need to exploit the capabilities of computing resources in order to deal with this kind of problems. In particular, parallel computing can be implemented at the level of metaheuristic optimization, by exploiting the physical parallelization feature of the nondominated sorting evolution strategies method, as well as at the level of repeated structural analyses required for assessing the behavioural constraints and for calculating the objective functions. In this study an efficient dynamic load balancing algorithm for optimum exploitation of available computing resources is proposed and, without loss of generality, is applied for computing the desired Pareto front. In such problems the computation of the complete Pareto front with feasible designs only, constitutes a very challenging task. The proposed algorithm achieves linear speedup factors and almost 100% speedup factor values with reference to the sequential procedure.

A General Formulation for Robust and Efficient Integration of Finite Differences and Phase Unwrapping on Sparse Multidimensional Domains

NASA Astrophysics Data System (ADS)

Costantini, Mario; Malvarosa, Fabio; Minati, Federico

2010-03-01

Phase unwrapping and integration of finite differences are key problems in several technical fields. In SAR interferometry and differential and persistent scatterers interferometry digital elevation models and displacement measurements can be obtained after unambiguously determining the phase values and reconstructing the mean velocities and elevations of the observed targets, which can be performed by integrating differential estimates of these quantities (finite differences between neighboring points).In this paper we propose a general formulation for robust and efficient integration of finite differences and phase unwrapping, which includes standard techniques methods as sub-cases. The proposed approach allows obtaining more reliable and accurate solutions by exploiting redundant differential estimates (not only between nearest neighboring points) and multi-dimensional information (e.g. multi-temporal, multi-frequency, multi-baseline observations), or external data (e.g. GPS measurements). The proposed approach requires the solution of linear or quadratic programming problems, for which computationally efficient algorithms exist.The validation tests obtained on real SAR data confirm the validity of the method, which was integrated in our production chain and successfully used also in massive productions.

Approximate, computationally efficient online learning in Bayesian spiking neurons.

PubMed

Kuhlmann, Levin; Hauser-Raspe, Michael; Manton, Jonathan H; Grayden, David B; Tapson, Jonathan; van Schaik, André

2014-03-01

Bayesian spiking neurons (BSNs) provide a probabilistic interpretation of how neurons perform inference and learning. Online learning in BSNs typically involves parameter estimation based on maximum-likelihood expectation-maximization (ML-EM) which is computationally slow and limits the potential of studying networks of BSNs. An online learning algorithm, fast learning (FL), is presented that is more computationally efficient than the benchmark ML-EM for a fixed number of time steps as the number of inputs to a BSN increases (e.g., 16.5 times faster run times for 20 inputs). Although ML-EM appears to converge 2.0 to 3.6 times faster than FL, the computational cost of ML-EM means that ML-EM takes longer to simulate to convergence than FL. FL also provides reasonable convergence performance that is robust to initialization of parameter estimates that are far from the true parameter values. However, parameter estimation depends on the range of true parameter values. Nevertheless, for a physiologically meaningful range of parameter values, FL gives very good average estimation accuracy, despite its approximate nature. The FL algorithm therefore provides an efficient tool, complementary to ML-EM, for exploring BSN networks in more detail in order to better understand their biological relevance. Moreover, the simplicity of the FL algorithm means it can be easily implemented in neuromorphic VLSI such that one can take advantage of the energy-efficient spike coding of BSNs.

Efficient and robust pupil size and blink estimation from near-field video sequences for human-machine interaction.

PubMed

Chen, Siyuan; Epps, Julien

2014-12-01

Monitoring pupil and blink dynamics has applications in cognitive load measurement during human-machine interaction. However, accurate, efficient, and robust pupil size and blink estimation pose significant challenges to the efficacy of real-time applications due to the variability of eye images, hence to date, require manual intervention for fine tuning of parameters. In this paper, a novel self-tuning threshold method, which is applicable to any infrared-illuminated eye images without a tuning parameter, is proposed for segmenting the pupil from the background images recorded by a low cost webcam placed near the eye. A convex hull and a dual-ellipse fitting method are also proposed to select pupil boundary points and to detect the eyelid occlusion state. Experimental results on a realistic video dataset show that the measurement accuracy using the proposed methods is higher than that of widely used manually tuned parameter methods or fixed parameter methods. Importantly, it demonstrates convenience and robustness for an accurate and fast estimate of eye activity in the presence of variations due to different users, task types, load, and environments. Cognitive load measurement in human-machine interaction can benefit from this computationally efficient implementation without requiring a threshold calibration beforehand. Thus, one can envisage a mini IR camera embedded in a lightweight glasses frame, like Google Glass, for convenient applications of real-time adaptive aiding and task management in the future.

Aerial video mosaicking using binary feature tracking

NASA Astrophysics Data System (ADS)

Minnehan, Breton; Savakis, Andreas

2015-05-01

Unmanned Aerial Vehicles are becoming an increasingly attractive platform for many applications, as their cost decreases and their capabilities increase. Creating detailed maps from aerial data requires fast and accurate video mosaicking methods. Traditional mosaicking techniques rely on inter-frame homography estimations that are cascaded through the video sequence. Computationally expensive keypoint matching algorithms are often used to determine the correspondence of keypoints between frames. This paper presents a video mosaicking method that uses an object tracking approach for matching keypoints between frames to improve both efficiency and robustness. The proposed tracking method matches local binary descriptors between frames and leverages the spatial locality of the keypoints to simplify the matching process. Our method is robust to cascaded errors by determining the homography between each frame and the ground plane rather than the prior frame. The frame-to-ground homography is calculated based on the relationship of each point's image coordinates and its estimated location on the ground plane. Robustness to moving objects is integrated into the homography estimation step through detecting anomalies in the motion of keypoints and eliminating the influence of outliers. The resulting mosaics are of high accuracy and can be computed in real time.

Efficient and optimized identification of generalized Maxwell viscoelastic relaxation spectra

PubMed Central

Babaei, Behzad; Davarian, Ali; Pryse, Kenneth M.; Elson, Elliot L.; Genin, Guy M.

2017-01-01

Viscoelastic relaxation spectra are essential for predicting and interpreting the mechanical responses of materials and structures. For biological tissues, these spectra must usually be estimated from viscoelastic relaxation tests. Interpreting viscoelastic relaxation tests is challenging because the inverse problem is expensive computationally. We present here an efficient algorithm that enables rapid identification of viscoelastic relaxation spectra. The algorithm was tested against trial data to characterize its robustness and identify its limitations and strengths. The algorithm was then applied to identify the viscoelastic response of reconstituted collagen, revealing an extensive distribution of viscoelastic time constants. PMID:26523785

Two-dimensional photonic crystal slab nanocavities on bulk single-crystal diamond

NASA Astrophysics Data System (ADS)

Wan, Noel H.; Mouradian, Sara; Englund, Dirk

2018-04-01

Color centers in diamond are promising spin qubits for quantum computing and quantum networking. In photon-mediated entanglement distribution schemes, the efficiency of the optical interface ultimately determines the scalability of such systems. Nano-scale optical cavities coupled to emitters constitute a robust spin-photon interface that can increase spontaneous emission rates and photon extraction efficiencies. In this work, we introduce the fabrication of 2D photonic crystal slab nanocavities with high quality factors and cubic wavelength mode volumes—directly in bulk diamond. This planar platform offers scalability and considerably expands the toolkit for classical and quantum nanophotonics in diamond.

Self-paced model learning for robust visual tracking

NASA Astrophysics Data System (ADS)

Huang, Wenhui; Gu, Jason; Ma, Xin; Li, Yibin

2017-01-01

In visual tracking, learning a robust and efficient appearance model is a challenging task. Model learning determines both the strategy and the frequency of model updating, which contains many details that could affect the tracking results. Self-paced learning (SPL) has recently been attracting considerable interest in the fields of machine learning and computer vision. SPL is inspired by the learning principle underlying the cognitive process of humans, whose learning process is generally from easier samples to more complex aspects of a task. We propose a tracking method that integrates the learning paradigm of SPL into visual tracking, so reliable samples can be automatically selected for model learning. In contrast to many existing model learning strategies in visual tracking, we discover the missing link between sample selection and model learning, which are combined into a single objective function in our approach. Sample weights and model parameters can be learned by minimizing this single objective function. Additionally, to solve the real-valued learning weight of samples, an error-tolerant self-paced function that considers the characteristics of visual tracking is proposed. We demonstrate the robustness and efficiency of our tracker on a recent tracking benchmark data set with 50 video sequences.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mehrez, Loujaine; Ghanem, Roger; Aitharaju, Venkat

Design of non-crimp fabric (NCF) composites entails major challenges pertaining to (1) the complex fine-scale morphology of the constituents, (2) the manufacturing-produced inconsistency of this morphology spatially, and thus (3) the ability to build reliable, robust, and efficient computational surrogate models to account for this complex nature. Traditional approaches to construct computational surrogate models have been to average over the fluctuations of the material properties at different scale lengths. This fails to account for the fine-scale features and fluctuations in morphology, material properties of the constituents, as well as fine-scale phenomena such as damage and cracks. In addition, it failsmore » to accurately predict the scatter in macroscopic properties, which is vital to the design process and behavior prediction. In this work, funded in part by the Department of Energy, we present an approach for addressing these challenges by relying on polynomial chaos representations of both input parameters and material properties at different scales. Moreover, we emphasize the efficiency and robustness of integrating the polynomial chaos expansion with multiscale tools to perform multiscale assimilation, characterization, propagation, and prediction, all of which are necessary to construct the data-driven surrogate models required to design under the uncertainty of composites. These data-driven constructions provide an accurate map from parameters (and their uncertainties) at all scales and the system-level behavior relevant for design. While this perspective is quite general and applicable to all multiscale systems, NCF composites present a particular hierarchy of scales that permits the efficient implementation of these concepts.« less

Iris recognition based on robust principal component analysis

NASA Astrophysics Data System (ADS)

Karn, Pradeep; He, Xiao Hai; Yang, Shuai; Wu, Xiao Hong

2014-11-01

Iris images acquired under different conditions often suffer from blur, occlusion due to eyelids and eyelashes, specular reflection, and other artifacts. Existing iris recognition systems do not perform well on these types of images. To overcome these problems, we propose an iris recognition method based on robust principal component analysis. The proposed method decomposes all training images into a low-rank matrix and a sparse error matrix, where the low-rank matrix is used for feature extraction. The sparsity concentration index approach is then applied to validate the recognition result. Experimental results using CASIA V4 and IIT Delhi V1iris image databases showed that the proposed method achieved competitive performances in both recognition accuracy and computational efficiency.

Efficient parallel implicit methods for rotary-wing aerodynamics calculations

NASA Astrophysics Data System (ADS)

Wissink, Andrew M.

Euler/Navier-Stokes Computational Fluid Dynamics (CFD) methods are commonly used for prediction of the aerodynamics and aeroacoustics of modern rotary-wing aircraft. However, their widespread application to large complex problems is limited lack of adequate computing power. Parallel processing offers the potential for dramatic increases in computing power, but most conventional implicit solution methods are inefficient in parallel and new techniques must be adopted to realize its potential. This work proposes alternative implicit schemes for Euler/Navier-Stokes rotary-wing calculations which are robust and efficient in parallel. The first part of this work proposes an efficient parallelizable modification of the Lower Upper-Symmetric Gauss Seidel (LU-SGS) implicit operator used in the well-known Transonic Unsteady Rotor Navier Stokes (TURNS) code. The new hybrid LU-SGS scheme couples a point-relaxation approach of the Data Parallel-Lower Upper Relaxation (DP-LUR) algorithm for inter-processor communication with the Symmetric Gauss Seidel algorithm of LU-SGS for on-processor computations. With the modified operator, TURNS is implemented in parallel using Message Passing Interface (MPI) for communication. Numerical performance and parallel efficiency are evaluated on the IBM SP2 and Thinking Machines CM-5 multi-processors for a variety of steady-state and unsteady test cases. The hybrid LU-SGS scheme maintains the numerical performance of the original LU-SGS algorithm in all cases and shows a good degree of parallel efficiency. It experiences a higher degree of robustness than DP-LUR for third-order upwind solutions. The second part of this work examines use of Krylov subspace iterative solvers for the nonlinear CFD solutions. The hybrid LU-SGS scheme is used as a parallelizable preconditioner. Two iterative methods are tested, Generalized Minimum Residual (GMRES) and Orthogonal s-Step Generalized Conjugate Residual (OSGCR). The Newton method demonstrates good parallel performance on the IBM SP2, with OS-GCR giving slightly better performance than GMRES on large numbers of processors. For steady and quasi-steady calculations, the convergence rate is accelerated but the overall solution time remains about the same as the standard hybrid LU-SGS scheme. For unsteady calculations, however, the Newton method maintains a higher degree of time-accuracy which allows tbe use of larger timesteps and results in CPU savings of 20-35%.

Resolution-Enhanced Harmonic and Interharmonic Measurement for Power Quality Analysis in Cyber-Physical Energy System.

PubMed

Liu, Yanchi; Wang, Xue; Liu, Youda; Cui, Sujin

2016-06-27

Power quality analysis issues, especially the measurement of harmonic and interharmonic in cyber-physical energy systems, are addressed in this paper. As new situations are introduced to the power system, the impact of electric vehicles, distributed generation and renewable energy has introduced extra demands to distributed sensors, waveform-level information and power quality data analytics. Harmonics and interharmonics, as the most significant disturbances, require carefully designed detection methods for an accurate measurement of electric loads whose information is crucial to subsequent analyzing and control. This paper gives a detailed description of the power quality analysis framework in networked environment and presents a fast and resolution-enhanced method for harmonic and interharmonic measurement. The proposed method first extracts harmonic and interharmonic components efficiently using the single-channel version of Robust Independent Component Analysis (RobustICA), then estimates the high-resolution frequency from three discrete Fourier transform (DFT) samples with little additional computation, and finally computes the amplitudes and phases with the adaptive linear neuron network. The experiments show that the proposed method is time-efficient and leads to a better accuracy of the simulated and experimental signals in the presence of noise and fundamental frequency deviation, thus providing a deeper insight into the (inter)harmonic sources or even the whole system.

Resolution-Enhanced Harmonic and Interharmonic Measurement for Power Quality Analysis in Cyber-Physical Energy System

PubMed Central

Liu, Yanchi; Wang, Xue; Liu, Youda; Cui, Sujin

2016-01-01

Power quality analysis issues, especially the measurement of harmonic and interharmonic in cyber-physical energy systems, are addressed in this paper. As new situations are introduced to the power system, the impact of electric vehicles, distributed generation and renewable energy has introduced extra demands to distributed sensors, waveform-level information and power quality data analytics. Harmonics and interharmonics, as the most significant disturbances, require carefully designed detection methods for an accurate measurement of electric loads whose information is crucial to subsequent analyzing and control. This paper gives a detailed description of the power quality analysis framework in networked environment and presents a fast and resolution-enhanced method for harmonic and interharmonic measurement. The proposed method first extracts harmonic and interharmonic components efficiently using the single-channel version of Robust Independent Component Analysis (RobustICA), then estimates the high-resolution frequency from three discrete Fourier transform (DFT) samples with little additional computation, and finally computes the amplitudes and phases with the adaptive linear neuron network. The experiments show that the proposed method is time-efficient and leads to a better accuracy of the simulated and experimental signals in the presence of noise and fundamental frequency deviation, thus providing a deeper insight into the (inter)harmonic sources or even the whole system. PMID:27355946

π-π stacking tackled with density functional theory

PubMed Central

Swart, Marcel; van der Wijst, Tushar; Fonseca Guerra, Célia

2007-01-01

Through comparison with ab initio reference data, we have evaluated the performance of various density functionals for describing π-π interactions as a function of the geometry between two stacked benzenes or benzene analogs, between two stacked DNA bases, and between two stacked Watson–Crick pairs. Our main purpose is to find a robust and computationally efficient density functional to be used specifically and only for describing π-π stacking interactions in DNA and other biological molecules in the framework of our recently developed QM/QM approach "QUILD". In line with previous studies, most standard density functionals recover, at best, only part of the favorable stacking interactions. An exception is the new KT1 functional, which correctly yields bound π-stacked structures. Surprisingly, a similarly good performance is achieved with the computationally very robust and efficient local density approximation (LDA). Furthermore, we show that classical electrostatic interactions determine the shape and depth of the π-π stacking potential energy surface. Figure Additivity approximation for the π-π interaction between two stacked Watson–Crick base pairs in terms of pairwise interactions between individual bases Electronic supplementary material The online version of this article (doi:10.1007/s00894-007-0239-y) contains supplementary material, which is available to authorized users. PMID:17874150

A heterogeneous computing accelerated SCE-UA global optimization method using OpenMP, OpenCL, CUDA, and OpenACC.

PubMed

Kan, Guangyuan; He, Xiaoyan; Ding, Liuqian; Li, Jiren; Liang, Ke; Hong, Yang

2017-10-01

The shuffled complex evolution optimization developed at the University of Arizona (SCE-UA) has been successfully applied in various kinds of scientific and engineering optimization applications, such as hydrological model parameter calibration, for many years. The algorithm possesses good global optimality, convergence stability and robustness. However, benchmark and real-world applications reveal the poor computational efficiency of the SCE-UA. This research aims at the parallelization and acceleration of the SCE-UA method based on powerful heterogeneous computing technology. The parallel SCE-UA is implemented on Intel Xeon multi-core CPU (by using OpenMP and OpenCL) and NVIDIA Tesla many-core GPU (by using OpenCL, CUDA, and OpenACC). The serial and parallel SCE-UA were tested based on the Griewank benchmark function. Comparison results indicate the parallel SCE-UA significantly improves computational efficiency compared to the original serial version. The OpenCL implementation obtains the best overall acceleration results however, with the most complex source code. The parallel SCE-UA has bright prospects to be applied in real-world applications.

Efficient Variational Quantum Simulator Incorporating Active Error Minimization

NASA Astrophysics Data System (ADS)

Li, Ying; Benjamin, Simon C.

2017-04-01

One of the key applications for quantum computers will be the simulation of other quantum systems that arise in chemistry, materials science, etc., in order to accelerate the process of discovery. It is important to ask the following question: Can this simulation be achieved using near-future quantum processors, of modest size and under imperfect control, or must it await the more distant era of large-scale fault-tolerant quantum computing? Here, we propose a variational method involving closely integrated classical and quantum coprocessors. We presume that all operations in the quantum coprocessor are prone to error. The impact of such errors is minimized by boosting them artificially and then extrapolating to the zero-error case. In comparison to a more conventional optimized Trotterization technique, we find that our protocol is efficient and appears to be fundamentally more robust against error accumulation.

A novel combined SLAM based on RBPF-SLAM and EIF-SLAM for mobile system sensing in a large scale environment.

PubMed

He, Bo; Zhang, Shujing; Yan, Tianhong; Zhang, Tao; Liang, Yan; Zhang, Hongjin

2011-01-01

Mobile autonomous systems are very important for marine scientific investigation and military applications. Many algorithms have been studied to deal with the computational efficiency problem required for large scale simultaneous localization and mapping (SLAM) and its related accuracy and consistency. Among these methods, submap-based SLAM is a more effective one. By combining the strength of two popular mapping algorithms, the Rao-Blackwellised particle filter (RBPF) and extended information filter (EIF), this paper presents a combined SLAM-an efficient submap-based solution to the SLAM problem in a large scale environment. RBPF-SLAM is used to produce local maps, which are periodically fused into an EIF-SLAM algorithm. RBPF-SLAM can avoid linearization of the robot model during operating and provide a robust data association, while EIF-SLAM can improve the whole computational speed, and avoid the tendency of RBPF-SLAM to be over-confident. In order to further improve the computational speed in a real time environment, a binary-tree-based decision-making strategy is introduced. Simulation experiments show that the proposed combined SLAM algorithm significantly outperforms currently existing algorithms in terms of accuracy and consistency, as well as the computing efficiency. Finally, the combined SLAM algorithm is experimentally validated in a real environment by using the Victoria Park dataset.

Combined multi-spectrum and orthogonal Laplacianfaces for fast CB-XLCT imaging with single-view data

NASA Astrophysics Data System (ADS)

Zhang, Haibo; Geng, Guohua; Chen, Yanrong; Qu, Xuan; Zhao, Fengjun; Hou, Yuqing; Yi, Huangjian; He, Xiaowei

2017-12-01

Cone-beam X-ray luminescence computed tomography (CB-XLCT) is an attractive hybrid imaging modality, which has the potential of monitoring the metabolic processes of nanophosphors-based drugs in vivo. Single-view data reconstruction as a key issue of CB-XLCT imaging promotes the effective study of dynamic XLCT imaging. However, it suffers from serious ill-posedness in the inverse problem. In this paper, a multi-spectrum strategy is adopted to relieve the ill-posedness of reconstruction. The strategy is based on the third-order simplified spherical harmonic approximation model. Then, an orthogonal Laplacianfaces-based method is proposed to reduce the large computational burden without degrading the imaging quality. Both simulated data and in vivo experimental data were used to evaluate the efficiency and robustness of the proposed method. The results are satisfactory in terms of both location and quantitative recovering with computational efficiency, indicating that the proposed method is practical and promising for single-view CB-XLCT imaging.

Quadratic Programming for Allocating Control Effort

NASA Technical Reports Server (NTRS)

Singh, Gurkirpal

2005-01-01

A computer program calculates an optimal allocation of control effort in a system that includes redundant control actuators. The program implements an iterative (but otherwise single-stage) algorithm of the quadratic-programming type. In general, in the quadratic-programming problem, one seeks the values of a set of variables that minimize a quadratic cost function, subject to a set of linear equality and inequality constraints. In this program, the cost function combines control effort (typically quantified in terms of energy or fuel consumed) and control residuals (differences between commanded and sensed values of variables to be controlled). In comparison with prior control-allocation software, this program offers approximately equal accuracy but much greater computational efficiency. In addition, this program offers flexibility, robustness to actuation failures, and a capability for selective enforcement of control requirements. The computational efficiency of this program makes it suitable for such complex, real-time applications as controlling redundant aircraft actuators or redundant spacecraft thrusters. The program is written in the C language for execution in a UNIX operating system.

Aeroelastic Model Structure Computation for Envelope Expansion

NASA Technical Reports Server (NTRS)

Kukreja, Sunil L.

2007-01-01

Structure detection is a procedure for selecting a subset of candidate terms, from a full model description, that best describes the observed output. This is a necessary procedure to compute an efficient system description which may afford greater insight into the functionality of the system or a simpler controller design. Structure computation as a tool for black-box modeling may be of critical importance in the development of robust, parsimonious models for the flight-test community. Moreover, this approach may lead to efficient strategies for rapid envelope expansion that may save significant development time and costs. In this study, a least absolute shrinkage and selection operator (LASSO) technique is investigated for computing efficient model descriptions of non-linear aeroelastic systems. The LASSO minimises the residual sum of squares with the addition of an l(Sub 1) penalty term on the parameter vector of the traditional l(sub 2) minimisation problem. Its use for structure detection is a natural extension of this constrained minimisation approach to pseudo-linear regression problems which produces some model parameters that are exactly zero and, therefore, yields a parsimonious system description. Applicability of this technique for model structure computation for the F/A-18 (McDonnell Douglas, now The Boeing Company, Chicago, Illinois) Active Aeroelastic Wing project using flight test data is shown for several flight conditions (Mach numbers) by identifying a parsimonious system description with a high percent fit for cross-validated data.

Multi-Party Privacy-Preserving Set Intersection with Quasi-Linear Complexity

NASA Astrophysics Data System (ADS)

Cheon, Jung Hee; Jarecki, Stanislaw; Seo, Jae Hong

Secure computation of the set intersection functionality allows n parties to find the intersection between their datasets without revealing anything else about them. An efficient protocol for such a task could have multiple potential applications in commerce, health care, and security. However, all currently known secure set intersection protocols for n>2 parties have computational costs that are quadratic in the (maximum) number of entries in the dataset contributed by each party, making secure computation of the set intersection only practical for small datasets. In this paper, we describe the first multi-party protocol for securely computing the set intersection functionality with both the communication and the computation costs that are quasi-linear in the size of the datasets. For a fixed security parameter, our protocols require O(n2k) bits of communication and Õ(n2k) group multiplications per player in the malicious adversary setting, where k is the size of each dataset. Our protocol follows the basic idea of the protocol proposed by Kissner and Song, but we gain efficiency by using different representations of the polynomials associated with users' datasets and careful employment of algorithms that interpolate or evaluate polynomials on multiple points more efficiently. Moreover, the proposed protocol is robust. This means that the protocol outputs the desired result even if some corrupted players leave during the execution of the protocol.

Recent Developments in the Application of Biologically Inspired Computation to Chemical Sensing

NASA Astrophysics Data System (ADS)

Marco, S.; Gutierrez-Gálvez, A.

2009-05-01

Biological olfaction outperforms chemical instrumentation in specificity, response time, detection limit, coding capacity, time stability, robustness, size, power consumption, and portability. This biological function provides outstanding performance due, to a large extent, to the unique architecture of the olfactory pathway, which combines a high degree of redundancy, an efficient combinatorial coding along with unmatched chemical information processing mechanisms. The last decade has witnessed important advances in the understanding of the computational primitives underlying the functioning of the olfactory system. In this work, the state of the art concerning biologically inspired computation for chemical sensing will be reviewed. Instead of reviewing the whole body of computational neuroscience of olfaction, we restrict this review to the application of models to the processing of real chemical sensor data.

Trajectory Segmentation Map-Matching Approach for Large-Scale, High-Resolution GPS Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhu, Lei; Holden, Jacob R.; Gonder, Jeffrey D.

With the development of smartphones and portable GPS devices, large-scale, high-resolution GPS data can be collected. Map matching is a critical step in studying vehicle driving activity and recognizing network traffic conditions from the data. A new trajectory segmentation map-matching algorithm is proposed to deal accurately and efficiently with large-scale, high-resolution GPS trajectory data. The new algorithm separated the GPS trajectory into segments. It found the shortest path for each segment in a scientific manner and ultimately generated a best-matched path for the entire trajectory. The similarity of a trajectory segment and its matched path is described by a similaritymore » score system based on the longest common subsequence. The numerical experiment indicated that the proposed map-matching algorithm was very promising in relation to accuracy and computational efficiency. Large-scale data set applications verified that the proposed method is robust and capable of dealing with real-world, large-scale GPS data in a computationally efficient and accurate manner.« less

Vehicle trajectory linearisation to enable efficient optimisation of the constant speed racing line

NASA Astrophysics Data System (ADS)

Timings, Julian P.; Cole, David J.

2012-06-01

A driver model is presented capable of optimising the trajectory of a simple dynamic nonlinear vehicle, at constant forward speed, so that progression along a predefined track is maximised as a function of time. In doing so, the model is able to continually operate a vehicle at its lateral-handling limit, maximising vehicle performance. The technique used forms a part of the solution to the motor racing objective of minimising lap time. A new approach of formulating the minimum lap time problem is motivated by the need for a more computationally efficient and robust tool-set for understanding on-the-limit driving behaviour. This has been achieved through set point-dependent linearisation of the vehicle model and coupling the vehicle-track system using an intrinsic coordinate description. Through this, the geometric vehicle trajectory had been linearised relative to the track reference, leading to new path optimisation algorithm which can be formed as a computationally efficient convex quadratic programming problem.

Trajectory Segmentation Map-Matching Approach for Large-Scale, High-Resolution GPS Data

DOE PAGES

Zhu, Lei; Holden, Jacob R.; Gonder, Jeffrey D.

2017-01-01

With the development of smartphones and portable GPS devices, large-scale, high-resolution GPS data can be collected. Map matching is a critical step in studying vehicle driving activity and recognizing network traffic conditions from the data. A new trajectory segmentation map-matching algorithm is proposed to deal accurately and efficiently with large-scale, high-resolution GPS trajectory data. The new algorithm separated the GPS trajectory into segments. It found the shortest path for each segment in a scientific manner and ultimately generated a best-matched path for the entire trajectory. The similarity of a trajectory segment and its matched path is described by a similaritymore » score system based on the longest common subsequence. The numerical experiment indicated that the proposed map-matching algorithm was very promising in relation to accuracy and computational efficiency. Large-scale data set applications verified that the proposed method is robust and capable of dealing with real-world, large-scale GPS data in a computationally efficient and accurate manner.« less

Implementation of the diagonalization-free algorithm in the self-consistent field procedure within the four-component relativistic scheme.

PubMed

Hrdá, Marcela; Kulich, Tomáš; Repiský, Michal; Noga, Jozef; Malkina, Olga L; Malkin, Vladimir G

2014-09-05

A recently developed Thouless-expansion-based diagonalization-free approach for improving the efficiency of self-consistent field (SCF) methods (Noga and Šimunek, J. Chem. Theory Comput. 2010, 6, 2706) has been adapted to the four-component relativistic scheme and implemented within the program package ReSpect. In addition to the implementation, the method has been thoroughly analyzed, particularly with respect to cases for which it is difficult or computationally expensive to find a good initial guess. Based on this analysis, several modifications of the original algorithm, refining its stability and efficiency, are proposed. To demonstrate the robustness and efficiency of the improved algorithm, we present the results of four-component diagonalization-free SCF calculations on several heavy-metal complexes, the largest of which contains more than 80 atoms (about 6000 4-spinor basis functions). The diagonalization-free procedure is about twice as fast as the corresponding diagonalization. Copyright © 2014 Wiley Periodicals, Inc.

Integration of nanoscale memristor synapses in neuromorphic computing architectures

NASA Astrophysics Data System (ADS)

Indiveri, Giacomo; Linares-Barranco, Bernabé; Legenstein, Robert; Deligeorgis, George; Prodromakis, Themistoklis

2013-09-01

Conventional neuro-computing architectures and artificial neural networks have often been developed with no or loose connections to neuroscience. As a consequence, they have largely ignored key features of biological neural processing systems, such as their extremely low-power consumption features or their ability to carry out robust and efficient computation using massively parallel arrays of limited precision, highly variable, and unreliable components. Recent developments in nano-technologies are making available extremely compact and low power, but also variable and unreliable solid-state devices that can potentially extend the offerings of availing CMOS technologies. In particular, memristors are regarded as a promising solution for modeling key features of biological synapses due to their nanoscale dimensions, their capacity to store multiple bits of information per element and the low energy required to write distinct states. In this paper, we first review the neuro- and neuromorphic computing approaches that can best exploit the properties of memristor and scale devices, and then propose a novel hybrid memristor-CMOS neuromorphic circuit which represents a radical departure from conventional neuro-computing approaches, as it uses memristors to directly emulate the biophysics and temporal dynamics of real synapses. We point out the differences between the use of memristors in conventional neuro-computing architectures and the hybrid memristor-CMOS circuit proposed, and argue how this circuit represents an ideal building block for implementing brain-inspired probabilistic computing paradigms that are robust to variability and fault tolerant by design.

A μ analysis-based, controller-synthesis framework for robust bioinspired visual navigation in less-structured environments.

PubMed

Keshavan, J; Gremillion, G; Escobar-Alvarez, H; Humbert, J S

2014-06-01

Safe, autonomous navigation by aerial microsystems in less-structured environments is a difficult challenge to overcome with current technology. This paper presents a novel visual-navigation approach that combines bioinspired wide-field processing of optic flow information with control-theoretic tools for synthesis of closed loop systems, resulting in robustness and performance guarantees. Structured singular value analysis is used to synthesize a dynamic controller that provides good tracking performance in uncertain environments without resorting to explicit pose estimation or extraction of a detailed environmental depth map. Experimental results with a quadrotor demonstrate the vehicle's robust obstacle-avoidance behaviour in a straight line corridor, an S-shaped corridor and a corridor with obstacles distributed in the vehicle's path. The computational efficiency and simplicity of the current approach offers a promising alternative to satisfying the payload, power and bandwidth constraints imposed by aerial microsystems.

Audio fingerprint extraction for content identification

NASA Astrophysics Data System (ADS)

Shiu, Yu; Yeh, Chia-Hung; Kuo, C. C. J.

2003-11-01

In this work, we present an audio content identification system that identifies some unknown audio material by comparing its fingerprint with those extracted off-line and saved in the music database. We will describe in detail the procedure to extract audio fingerprints and demonstrate that they are robust to noise and content-preserving manipulations. The main feature in the proposed system is the zero-crossing rate extracted with the octave-band filter bank. The zero-crossing rate can be used to describe the dominant frequency in each subband with a very low computational cost. The size of audio fingerprint is small and can be efficiently stored along with the compressed files in the database. It is also robust to many modifications such as tempo change and time-alignment distortion. Besides, the octave-band filter bank is used to enhance the robustness to distortion, especially those localized on some frequency regions.

Benchmarking the efficiency of the Chilean water and sewerage companies: a double-bootstrap approach.

PubMed

Molinos-Senante, María; Donoso, Guillermo; Sala-Garrido, Ramon; Villegas, Andrés

2018-03-01

Benchmarking the efficiency of water companies is essential to set water tariffs and to promote their sustainability. In doing so, most of the previous studies have applied conventional data envelopment analysis (DEA) models. However, it is a deterministic method that does not allow to identify environmental factors influencing efficiency scores. To overcome this limitation, this paper evaluates the efficiency of a sample of Chilean water and sewerage companies applying a double-bootstrap DEA model. Results evidenced that the ranking of water and sewerage companies changes notably whether efficiency scores are computed applying conventional or double-bootstrap DEA models. Moreover, it was found that the percentage of non-revenue water and customer density are factors influencing the efficiency of Chilean water and sewerage companies. This paper illustrates the importance of using a robust and reliable method to increase the relevance of benchmarking tools.

A-VCI: A flexible method to efficiently compute vibrational spectra

NASA Astrophysics Data System (ADS)

Odunlami, Marc; Le Bris, Vincent; Bégué, Didier; Baraille, Isabelle; Coulaud, Olivier

2017-06-01

The adaptive vibrational configuration interaction algorithm has been introduced as a new method to efficiently reduce the dimension of the set of basis functions used in a vibrational configuration interaction process. It is based on the construction of nested bases for the discretization of the Hamiltonian operator according to a theoretical criterion that ensures the convergence of the method. In the present work, the Hamiltonian is written as a sum of products of operators. The purpose of this paper is to study the properties and outline the performance details of the main steps of the algorithm. New parameters have been incorporated to increase flexibility, and their influence has been thoroughly investigated. The robustness and reliability of the method are demonstrated for the computation of the vibrational spectrum up to 3000 cm-1 of a widely studied 6-atom molecule (acetonitrile). Our results are compared to the most accurate up to date computation; we also give a new reference calculation for future work on this system. The algorithm has also been applied to a more challenging 7-atom molecule (ethylene oxide). The computed spectrum up to 3200 cm-1 is the most accurate computation that exists today on such systems.

A-VCI: A flexible method to efficiently compute vibrational spectra.

PubMed

Odunlami, Marc; Le Bris, Vincent; Bégué, Didier; Baraille, Isabelle; Coulaud, Olivier

2017-06-07

The adaptive vibrational configuration interaction algorithm has been introduced as a new method to efficiently reduce the dimension of the set of basis functions used in a vibrational configuration interaction process. It is based on the construction of nested bases for the discretization of the Hamiltonian operator according to a theoretical criterion that ensures the convergence of the method. In the present work, the Hamiltonian is written as a sum of products of operators. The purpose of this paper is to study the properties and outline the performance details of the main steps of the algorithm. New parameters have been incorporated to increase flexibility, and their influence has been thoroughly investigated. The robustness and reliability of the method are demonstrated for the computation of the vibrational spectrum up to 3000 cm -1 of a widely studied 6-atom molecule (acetonitrile). Our results are compared to the most accurate up to date computation; we also give a new reference calculation for future work on this system. The algorithm has also been applied to a more challenging 7-atom molecule (ethylene oxide). The computed spectrum up to 3200 cm -1 is the most accurate computation that exists today on such systems.

An assessment of the potential of PFEM-2 for solving long real-time industrial applications

NASA Astrophysics Data System (ADS)

Gimenez, Juan M.; Ramajo, Damián E.; Márquez Damián, Santiago; Nigro, Norberto M.; Idelsohn, Sergio R.

2017-07-01

The latest generation of the particle finite element method (PFEM-2) is a numerical method based on the Lagrangian formulation of the equations, which presents advantages in terms of robustness and efficiency over classical Eulerian methodologies when certain kind of flows are simulated, especially those where convection plays an important role. These situations are often encountered in real engineering problems, where very complex geometries and operating conditions require very large and long computations. The advantages that the parallelism introduced in the computational fluid dynamics making affordable computations with very fine spatial discretizations are well known. However, it is not possible to have the time parallelized, despite the effort that is being dedicated to use space-time formulations. In this sense, PFEM-2 adds a valuable feature in that its strong stability with little loss of accuracy provides an interesting way of satisfying the real-life computation needs. After having already demonstrated in previous publications its ability to achieve academic-based solutions with a good compromise between accuracy and efficiency, in this work, the method is revisited and employed to solve several nonacademic problems of technological interest, which fall into that category. Simulations concerning oil-water separation, waste-water treatment, metallurgical foundries, and safety assessment are presented. These cases are selected due to their particular requirements of long simulation times and or intensive interface treatment. Thus, large time-steps may be employed with PFEM-2 without compromising the accuracy and robustness of the simulation, as occurs with Eulerian alternatives, showing the potentiality of the methodology for solving not only academic tests but also real engineering problems.

Experimental and Computational Sonic Boom Assessment of Lockheed-Martin N+2 Low Boom Models

NASA Technical Reports Server (NTRS)

Cliff, Susan E.; Durston, Donald A.; Elmiligui, Alaa A.; Walker, Eric L.; Carter, Melissa B.

2015-01-01

Flight at speeds greater than the speed of sound is not permitted over land, primarily because of the noise and structural damage caused by sonic boom pressure waves of supersonic aircraft. Mitigation of sonic boom is a key focus area of the High Speed Project under NASA's Fundamental Aeronautics Program. The project is focusing on technologies to enable future civilian aircraft to fly efficiently with reduced sonic boom, engine and aircraft noise, and emissions. A major objective of the project is to improve both computational and experimental capabilities for design of low-boom, high-efficiency aircraft. NASA and industry partners are developing improved wind tunnel testing techniques and new pressure instrumentation to measure the weak sonic boom pressure signatures of modern vehicle concepts. In parallel, computational methods are being developed to provide rapid design and analysis of supersonic aircraft with improved meshing techniques that provide efficient, robust, and accurate on- and off-body pressures at several body lengths from vehicles with very low sonic boom overpressures. The maturity of these critical parallel efforts is necessary before low-boom flight can be demonstrated and commercial supersonic flight can be realized.

Stabilization Approaches for Linear and Nonlinear Reduced Order Models

NASA Astrophysics Data System (ADS)

Rezaian, Elnaz; Wei, Mingjun

2017-11-01

It has been a major concern to establish reduced order models (ROMs) as reliable representatives of the dynamics inherent in high fidelity simulations, while fast computation is achieved. In practice it comes to stability and accuracy of ROMs. Given the inviscid nature of Euler equations it becomes more challenging to achieve stability, especially where moving discontinuities exist. Originally unstable linear and nonlinear ROMs are stabilized here by two approaches. First, a hybrid method is developed by integrating two different stabilization algorithms. At the same time, symmetry inner product is introduced in the generation of ROMs for its known robust behavior for compressible flows. Results have shown a notable improvement in computational efficiency and robustness compared to similar approaches. Second, a new stabilization algorithm is developed specifically for nonlinear ROMs. This method adopts Particle Swarm Optimization to enforce a bounded ROM response for minimum discrepancy between the high fidelity simulation and the ROM outputs. Promising results are obtained in its application on the nonlinear ROM of an inviscid fluid flow with discontinuities. Supported by ARL.

Generation of intervention strategy for a genetic regulatory network represented by a family of Markov Chains.

PubMed

Berlow, Noah; Pal, Ranadip

2011-01-01

Genetic Regulatory Networks (GRNs) are frequently modeled as Markov Chains providing the transition probabilities of moving from one state of the network to another. The inverse problem of inference of the Markov Chain from noisy and limited experimental data is an ill posed problem and often generates multiple model possibilities instead of a unique one. In this article, we address the issue of intervention in a genetic regulatory network represented by a family of Markov Chains. The purpose of intervention is to alter the steady state probability distribution of the GRN as the steady states are considered to be representative of the phenotypes. We consider robust stationary control policies with best expected behavior. The extreme computational complexity involved in search of robust stationary control policies is mitigated by using a sequential approach to control policy generation and utilizing computationally efficient techniques for updating the stationary probability distribution of a Markov chain following a rank one perturbation.

Robust design optimization method for centrifugal impellers under surface roughness uncertainties due to blade fouling

NASA Astrophysics Data System (ADS)

Ju, Yaping; Zhang, Chuhua

2016-03-01

Blade fouling has been proved to be a great threat to compressor performance in operating stage. The current researches on fouling-induced performance degradations of centrifugal compressors are based mainly on simplified roughness models without taking into account the realistic factors such as spatial non-uniformity and randomness of the fouling-induced surface roughness. Moreover, little attention has been paid to the robust design optimization of centrifugal compressor impellers with considerations of blade fouling. In this paper, a multi-objective robust design optimization method is developed for centrifugal impellers under surface roughness uncertainties due to blade fouling. A three-dimensional surface roughness map is proposed to describe the nonuniformity and randomness of realistic fouling accumulations on blades. To lower computational cost in robust design optimization, the support vector regression (SVR) metamodel is combined with the Monte Carlo simulation (MCS) method to conduct the uncertainty analysis of fouled impeller performance. The analyzed results show that the critical fouled region associated with impeller performance degradations lies at the leading edge of blade tip. The SVR metamodel has been proved to be an efficient and accurate means in the detection of impeller performance variations caused by roughness uncertainties. After design optimization, the robust optimal design is found to be more efficient and less sensitive to fouling uncertainties while maintaining good impeller performance in the clean condition. This research proposes a systematic design optimization method for centrifugal compressors with considerations of blade fouling, providing a practical guidance to the design of advanced centrifugal compressors.

Structural Analysis Methods for Structural Health Management of Future Aerospace Vehicles

NASA Technical Reports Server (NTRS)

Tessler, Alexander

2007-01-01

Two finite element based computational methods, Smoothing Element Analysis (SEA) and the inverse Finite Element Method (iFEM), are reviewed, and examples of their use for structural health monitoring are discussed. Due to their versatility, robustness, and computational efficiency, the methods are well suited for real-time structural health monitoring of future space vehicles, large space structures, and habitats. The methods may be effectively employed to enable real-time processing of sensing information, specifically for identifying three-dimensional deformed structural shapes as well as the internal loads. In addition, they may be used in conjunction with evolutionary algorithms to design optimally distributed sensors. These computational tools have demonstrated substantial promise for utilization in future Structural Health Management (SHM) systems.

Efficient Phase Unwrapping Architecture for Digital Holographic Microscopy

PubMed Central

Hwang, Wen-Jyi; Cheng, Shih-Chang; Cheng, Chau-Jern

2011-01-01

This paper presents a novel phase unwrapping architecture for accelerating the computational speed of digital holographic microscopy (DHM). A fast Fourier transform (FFT) based phase unwrapping algorithm providing a minimum squared error solution is adopted for hardware implementation because of its simplicity and robustness to noise. The proposed architecture is realized in a pipeline fashion to maximize throughput of the computation. Moreover, the number of hardware multipliers and dividers are minimized to reduce the hardware costs. The proposed architecture is used as a custom user logic in a system on programmable chip (SOPC) for physical performance measurement. Experimental results reveal that the proposed architecture is effective for expediting the computational speed while consuming low hardware resources for designing an embedded DHM system. PMID:22163688

Colour based fire detection method with temporal intensity variation filtration

NASA Astrophysics Data System (ADS)

Trambitckii, K.; Anding, K.; Musalimov, V.; Linß, G.

2015-02-01

Development of video, computing technologies and computer vision gives a possibility of automatic fire detection on video information. Under that project different algorithms was implemented to find more efficient way of fire detection. In that article colour based fire detection algorithm is described. But it is not enough to use only colour information to detect fire properly. The main reason of this is that in the shooting conditions may be a lot of things having colour similar to fire. A temporary intensity variation of pixels is used to separate them from the fire. These variations are averaged over the series of several frames. This algorithm shows robust work and was realised as a computer program by using of the OpenCV library.

Geometrically Flexible and Efficient Flow Analysis of High Speed Vehicles Via Domain Decomposition, Part 1: Unstructured-Grid Solver for High Speed Flows

NASA Technical Reports Server (NTRS)

White, Jeffery A.; Baurle, Robert A.; Passe, Bradley J.; Spiegel, Seth C.; Nishikawa, Hiroaki

2017-01-01

The ability to solve the equations governing the hypersonic turbulent flow of a real gas on unstructured grids using a spatially-elliptic, 2nd-order accurate, cell-centered, finite-volume method has been recently implemented in the VULCAN-CFD code. This paper describes the key numerical methods and techniques that were found to be required to robustly obtain accurate solutions to hypersonic flows on non-hex-dominant unstructured grids. The methods and techniques described include: an augmented stencil, weighted linear least squares, cell-average gradient method, a robust multidimensional cell-average gradient-limiter process that is consistent with the augmented stencil of the cell-average gradient method and a cell-face gradient method that contains a cell skewness sensitive damping term derived using hyperbolic diffusion based concepts. A data-parallel matrix-based symmetric Gauss-Seidel point-implicit scheme, used to solve the governing equations, is described and shown to be more robust and efficient than a matrix-free alternative. In addition, a y+ adaptive turbulent wall boundary condition methodology is presented. This boundary condition methodology is deigned to automatically switch between a solve-to-the-wall and a wall-matching-function boundary condition based on the local y+ of the 1st cell center off the wall. The aforementioned methods and techniques are then applied to a series of hypersonic and supersonic turbulent flat plate unit tests to examine the efficiency, robustness and convergence behavior of the implicit scheme and to determine the ability of the solve-to-the-wall and y+ adaptive turbulent wall boundary conditions to reproduce the turbulent law-of-the-wall. Finally, the thermally perfect, chemically frozen, Mach 7.8 turbulent flow of air through a scramjet flow-path is computed and compared with experimental data to demonstrate the robustness, accuracy and convergence behavior of the unstructured-grid solver for a realistic 3-D geometry on a non-hex-dominant grid.

Analysis of entropy extraction efficiencies in random number generation systems

NASA Astrophysics Data System (ADS)

Wang, Chao; Wang, Shuang; Chen, Wei; Yin, Zhen-Qiang; Han, Zheng-Fu

2016-05-01

Random numbers (RNs) have applications in many areas: lottery games, gambling, computer simulation, and, most importantly, cryptography [N. Gisin et al., Rev. Mod. Phys. 74 (2002) 145]. In cryptography theory, the theoretical security of the system calls for high quality RNs. Therefore, developing methods for producing unpredictable RNs with adequate speed is an attractive topic. Early on, despite the lack of theoretical support, pseudo RNs generated by algorithmic methods performed well and satisfied reasonable statistical requirements. However, as implemented, those pseudorandom sequences were completely determined by mathematical formulas and initial seeds, which cannot introduce extra entropy or information. In these cases, “random” bits are generated that are not at all random. Physical random number generators (RNGs), which, in contrast to algorithmic methods, are based on unpredictable physical random phenomena, have attracted considerable research interest. However, the way that we extract random bits from those physical entropy sources has a large influence on the efficiency and performance of the system. In this manuscript, we will review and discuss several randomness extraction schemes that are based on radiation or photon arrival times. We analyze the robustness, post-processing requirements and, in particular, the extraction efficiency of those methods to aid in the construction of efficient, compact and robust physical RNG systems.

A Parallel Nonrigid Registration Algorithm Based on B-Spline for Medical Images.

PubMed

Du, Xiaogang; Dang, Jianwu; Wang, Yangping; Wang, Song; Lei, Tao

2016-01-01

The nonrigid registration algorithm based on B-spline Free-Form Deformation (FFD) plays a key role and is widely applied in medical image processing due to the good flexibility and robustness. However, it requires a tremendous amount of computing time to obtain more accurate registration results especially for a large amount of medical image data. To address the issue, a parallel nonrigid registration algorithm based on B-spline is proposed in this paper. First, the Logarithm Squared Difference (LSD) is considered as the similarity metric in the B-spline registration algorithm to improve registration precision. After that, we create a parallel computing strategy and lookup tables (LUTs) to reduce the complexity of the B-spline registration algorithm. As a result, the computing time of three time-consuming steps including B-splines interpolation, LSD computation, and the analytic gradient computation of LSD, is efficiently reduced, for the B-spline registration algorithm employs the Nonlinear Conjugate Gradient (NCG) optimization method. Experimental results of registration quality and execution efficiency on the large amount of medical images show that our algorithm achieves a better registration accuracy in terms of the differences between the best deformation fields and ground truth and a speedup of 17 times over the single-threaded CPU implementation due to the powerful parallel computing ability of Graphics Processing Unit (GPU).

Data mining in soft computing framework: a survey.

PubMed

Mitra, S; Pal, S K; Mitra, P

2002-01-01

The present article provides a survey of the available literature on data mining using soft computing. A categorization has been provided based on the different soft computing tools and their hybridizations used, the data mining function implemented, and the preference criterion selected by the model. The utility of the different soft computing methodologies is highlighted. Generally fuzzy sets are suitable for handling the issues related to understandability of patterns, incomplete/noisy data, mixed media information and human interaction, and can provide approximate solutions faster. Neural networks are nonparametric, robust, and exhibit good learning and generalization capabilities in data-rich environments. Genetic algorithms provide efficient search algorithms to select a model, from mixed media data, based on some preference criterion/objective function. Rough sets are suitable for handling different types of uncertainty in data. Some challenges to data mining and the application of soft computing methodologies are indicated. An extensive bibliography is also included.

Snore related signals processing in a private cloud computing system.

PubMed

Qian, Kun; Guo, Jian; Xu, Huijie; Zhu, Zhaomeng; Zhang, Gongxuan

2014-09-01

Snore related signals (SRS) have been demonstrated to carry important information about the obstruction site and degree in the upper airway of Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) patients in recent years. To make this acoustic signal analysis method more accurate and robust, big SRS data processing is inevitable. As an emerging concept and technology, cloud computing has motivated numerous researchers and engineers to exploit applications both in academic and industry field, which could have an ability to implement a huge blue print in biomedical engineering. Considering the security and transferring requirement of biomedical data, we designed a system based on private cloud computing to process SRS. Then we set the comparable experiments of processing a 5-hour audio recording of an OSAHS patient by a personal computer, a server and a private cloud computing system to demonstrate the efficiency of the infrastructure we proposed.

Machine learning based Intelligent cognitive network using fog computing

NASA Astrophysics Data System (ADS)

Lu, Jingyang; Li, Lun; Chen, Genshe; Shen, Dan; Pham, Khanh; Blasch, Erik

2017-05-01

In this paper, a Cognitive Radio Network (CRN) based on artificial intelligence is proposed to distribute the limited radio spectrum resources more efficiently. The CRN framework can analyze the time-sensitive signal data close to the signal source using fog computing with different types of machine learning techniques. Depending on the computational capabilities of the fog nodes, different features and machine learning techniques are chosen to optimize spectrum allocation. Also, the computing nodes send the periodic signal summary which is much smaller than the original signal to the cloud so that the overall system spectrum source allocation strategies are dynamically updated. Applying fog computing, the system is more adaptive to the local environment and robust to spectrum changes. As most of the signal data is processed at the fog level, it further strengthens the system security by reducing the communication burden of the communications network.

Two-Level Weld-Material Homogenization for Efficient Computational Analysis of Welded Structure Blast-Survivability

NASA Astrophysics Data System (ADS)

Grujicic, M.; Arakere, G.; Hariharan, A.; Pandurangan, B.

2012-06-01

The introduction of newer joining technologies like the so-called friction-stir welding (FSW) into automotive engineering entails the knowledge of the joint-material microstructure and properties. Since, the development of vehicles (including military vehicles capable of surviving blast and ballistic impacts) nowadays involves extensive use of the computational engineering analyses (CEA), robust high-fidelity material models are needed for the FSW joints. A two-level material-homogenization procedure is proposed and utilized in this study to help manage computational cost and computer storage requirements for such CEAs. The method utilizes experimental (microstructure, microhardness, tensile testing, and x-ray diffraction) data to construct: (a) the material model for each weld zone and (b) the material model for the entire weld. The procedure is validated by comparing its predictions with the predictions of more detailed but more costly computational analyses.

An open-source computational and data resource to analyze digital maps of immunopeptidomes

DOE PAGES

Caron, Etienne; Espona, Lucia; Kowalewski, Daniel J.; ...

2015-07-08

We present a novel mass spectrometry-based high-throughput workflow and an open-source computational and data resource to reproducibly identify and quantify HLA-associated peptides. Collectively, the resources support the generation of HLA allele-specific peptide assay libraries consisting of consensus fragment ion spectra, and the analysis of quantitative digital maps of HLA peptidomes generated from a range of biological sources by SWATH mass spectrometry (MS). This study represents the first community-based effort to develop a robust platform for the reproducible and quantitative measurement of the entire repertoire of peptides presented by HLA molecules, an essential step towards the design of efficient immunotherapies.

Random phase encoding for optical security

NASA Astrophysics Data System (ADS)

Wang, RuiKang K.; Watson, Ian A.; Chatwin, Christopher R.

1996-09-01

A new optical encoding method for security applications is proposed. The encoded image (encrypted into the security products) is merely a random phase image statistically and randomly generated by a random number generator using a computer, which contains no information from the reference pattern (stored for verification) or the frequency plane filter (a phase-only function for decoding). The phase function in the frequency plane is obtained using a modified phase retrieval algorithm. The proposed method uses two phase-only functions (images) at both the input and frequency planes of the optical processor leading to maximum optical efficiency. Computer simulation shows that the proposed method is robust for optical security applications.

Universal non-adiabatic geometric manipulation of pseudo-spin charge qubits

NASA Astrophysics Data System (ADS)

Azimi Mousolou, Vahid

2017-01-01

Reliable quantum information processing requires high-fidelity universal manipulation of quantum systems within the characteristic coherence times. Non-adiabatic holonomic quantum computation offers a promising approach to implement fast, universal, and robust quantum logic gates particularly useful in nano-fabricated solid-state architectures, which typically have short coherence times. Here, we propose an experimentally feasible scheme to realize high-speed universal geometric quantum gates in nano-engineered pseudo-spin charge qubits. We use a system of three coupled quantum dots containing a single electron, where two computational states of a double quantum dot charge qubit interact through an intermediate quantum dot. The additional degree of freedom introduced into the qubit makes it possible to create a geometric model system, which allows robust and efficient single-qubit rotations through careful control of the inter-dot tunneling parameters. We demonstrate that a capacitive coupling between two charge qubits permits a family of non-adiabatic holonomic controlled two-qubit entangling gates, and thus provides a promising procedure to maintain entanglement in charge qubits and a pathway toward fault-tolerant universal quantum computation. We estimate the feasibility of the proposed structure by analyzing the gate fidelities to some extent.

Investigation on imperfection sensitivity of composite cylindrical shells using the nonlinearity reduction technique and the polynomial chaos method

NASA Astrophysics Data System (ADS)

Liang, Ke; Sun, Qin; Liu, Xiaoran

2018-05-01

The theoretical buckling load of a perfect cylinder must be reduced by a knock-down factor to account for structural imperfections. The EU project DESICOS proposed a new robust design for imperfection-sensitive composite cylindrical shells using the combination of deterministic and stochastic simulations, however the high computational complexity seriously affects its wider application in aerospace structures design. In this paper, the nonlinearity reduction technique and the polynomial chaos method are implemented into the robust design process, to significantly lower computational costs. The modified Newton-type Koiter-Newton approach which largely reduces the number of degrees of freedom in the nonlinear finite element model, serves as the nonlinear buckling solver to trace the equilibrium paths of geometrically nonlinear structures efficiently. The non-intrusive polynomial chaos method provides the buckling load with an approximate chaos response surface with respect to imperfections and uses buckling solver codes as black boxes. A fast large-sample study can be applied using the approximate chaos response surface to achieve probability characteristics of buckling loads. The performance of the method in terms of reliability, accuracy and computational effort is demonstrated with an unstiffened CFRP cylinder.

Lane Detection on the iPhone

NASA Astrophysics Data System (ADS)

Ren, Feixiang; Huang, Jinsheng; Terauchi, Mutsuhiro; Jiang, Ruyi; Klette, Reinhard

A robust and efficient lane detection system is an essential component of Lane Departure Warning Systems, which are commonly used in many vision-based Driver Assistance Systems (DAS) in intelligent transportation. Various computation platforms have been proposed in the past few years for the implementation of driver assistance systems (e.g., PC, laptop, integrated chips, PlayStation, and so on). In this paper, we propose a new platform for the implementation of lane detection, which is based on a mobile phone (the iPhone). Due to physical limitations of the iPhone w.r.t. memory and computing power, a simple and efficient lane detection algorithm using a Hough transform is developed and implemented on the iPhone, as existing algorithms developed based on the PC platform are not suitable for mobile phone devices (currently). Experiments of the lane detection algorithm are made both on PC and on iPhone.

Analysis on trust influencing factors and trust model from multiple perspectives of online Auction

NASA Astrophysics Data System (ADS)

Yu, Wang

2017-10-01

Current reputation models lack the research on online auction trading completely so they cannot entirely reflect the reputation status of users and may cause problems on operability. To evaluate the user trust in online auction correctly, a trust computing model based on multiple influencing factors is established. It aims at overcoming the efficiency of current trust computing methods and the limitations of traditional theoretical trust models. The improved model comprehensively considers the trust degree evaluation factors of three types of participants according to different participation modes of online auctioneers, to improve the accuracy, effectiveness and robustness of the trust degree. The experiments test the efficiency and the performance of our model under different scale of malicious user, under environment like eBay and Sporas model. The experimental results analysis show the model proposed in this paper makes up the deficiency of existing model and it also has better feasibility.

Robust object tacking based on self-adaptive search area

NASA Astrophysics Data System (ADS)

Dong, Taihang; Zhong, Sheng

2018-02-01

Discriminative correlation filter (DCF) based trackers have recently achieved excellent performance with great computational efficiency. However, DCF based trackers suffer boundary effects, which result in the unstable performance in challenging situations exhibiting fast motion. In this paper, we propose a novel method to mitigate this side-effect in DCF based trackers. We change the search area according to the prediction of target motion. When the object moves fast, broad search area could alleviate boundary effects and reserve the probability of locating object. When the object moves slowly, narrow search area could prevent effect of useless background information and improve computational efficiency to attain real-time performance. This strategy can impressively soothe boundary effects in situations exhibiting fast motion and motion blur, and it can be used in almost all DCF based trackers. The experiments on OTB benchmark show that the proposed framework improves the performance compared with the baseline trackers.

Contour integral method for obtaining the self-energy matrices of electrodes in electron transport calculations

NASA Astrophysics Data System (ADS)

Iwase, Shigeru; Futamura, Yasunori; Imakura, Akira; Sakurai, Tetsuya; Tsukamoto, Shigeru; Ono, Tomoya

2018-05-01

We propose an efficient computational method for evaluating the self-energy matrices of electrodes to study ballistic electron transport properties in nanoscale systems. To reduce the high computational cost incurred in large systems, a contour integral eigensolver based on the Sakurai-Sugiura method combined with the shifted biconjugate gradient method is developed to solve an exponential-type eigenvalue problem for complex wave vectors. A remarkable feature of the proposed algorithm is that the numerical procedure is very similar to that of conventional band structure calculations. We implement the developed method in the framework of the real-space higher-order finite-difference scheme with nonlocal pseudopotentials. Numerical tests for a wide variety of materials validate the robustness, accuracy, and efficiency of the proposed method. As an illustration of the method, we present the electron transport property of the freestanding silicene with the line defect originating from the reversed buckled phases.

A Fast Gradient Method for Nonnegative Sparse Regression With Self-Dictionary

NASA Astrophysics Data System (ADS)

Gillis, Nicolas; Luce, Robert

2018-01-01

A nonnegative matrix factorization (NMF) can be computed efficiently under the separability assumption, which asserts that all the columns of the given input data matrix belong to the cone generated by a (small) subset of them. The provably most robust methods to identify these conic basis columns are based on nonnegative sparse regression and self dictionaries, and require the solution of large-scale convex optimization problems. In this paper we study a particular nonnegative sparse regression model with self dictionary. As opposed to previously proposed models, this model yields a smooth optimization problem where the sparsity is enforced through linear constraints. We show that the Euclidean projection on the polyhedron defined by these constraints can be computed efficiently, and propose a fast gradient method to solve our model. We compare our algorithm with several state-of-the-art methods on synthetic data sets and real-world hyperspectral images.

Adaptive radial basis function mesh deformation using data reduction

NASA Astrophysics Data System (ADS)

Gillebaart, T.; Blom, D. S.; van Zuijlen, A. H.; Bijl, H.

2016-09-01

Radial Basis Function (RBF) mesh deformation is one of the most robust mesh deformation methods available. Using the greedy (data reduction) method in combination with an explicit boundary correction, results in an efficient method as shown in literature. However, to ensure the method remains robust, two issues are addressed: 1) how to ensure that the set of control points remains an accurate representation of the geometry in time and 2) how to use/automate the explicit boundary correction, while ensuring a high mesh quality. In this paper, we propose an adaptive RBF mesh deformation method, which ensures the set of control points always represents the geometry/displacement up to a certain (user-specified) criteria, by keeping track of the boundary error throughout the simulation and re-selecting when needed. Opposed to the unit displacement and prescribed displacement selection methods, the adaptive method is more robust, user-independent and efficient, for the cases considered. Secondly, the analysis of a single high aspect ratio cell is used to formulate an equation for the correction radius needed, depending on the characteristics of the correction function used, maximum aspect ratio, minimum first cell height and boundary error. Based on the analysis two new radial basis correction functions are derived and proposed. This proposed automated procedure is verified while varying the correction function, Reynolds number (and thus first cell height and aspect ratio) and boundary error. Finally, the parallel efficiency is studied for the two adaptive methods, unit displacement and prescribed displacement for both the CPU as well as the memory formulation with a 2D oscillating and translating airfoil with oscillating flap, a 3D flexible locally deforming tube and deforming wind turbine blade. Generally, the memory formulation requires less work (due to the large amount of work required for evaluating RBF's), but the parallel efficiency reduces due to the limited bandwidth available between CPU and memory. In terms of parallel efficiency/scaling the different studied methods perform similarly, with the greedy algorithm being the bottleneck. In terms of absolute computational work the adaptive methods are better for the cases studied due to their more efficient selection of the control points. By automating most of the RBF mesh deformation, a robust, efficient and almost user-independent mesh deformation method is presented.

Efficient and optimized identification of generalized Maxwell viscoelastic relaxation spectra.

PubMed

Babaei, Behzad; Davarian, Ali; Pryse, Kenneth M; Elson, Elliot L; Genin, Guy M

2015-03-01

Viscoelastic relaxation spectra are essential for predicting and interpreting the mechanical responses of materials and structures. For biological tissues, these spectra must usually be estimated from viscoelastic relaxation tests. Interpreting viscoelastic relaxation tests is challenging because the inverse problem is expensive computationally. We present here an efficient algorithm that enables rapid identification of viscoelastic relaxation spectra. The algorithm was tested against trial data to characterize its robustness and identify its limitations and strengths. The algorithm was then applied to identify the viscoelastic response of reconstituted collagen, revealing an extensive distribution of viscoelastic time constants. Copyright © 2015 Elsevier Ltd. All rights reserved.

On the efficient and reliable numerical solution of rate-and-state friction problems

NASA Astrophysics Data System (ADS)

Pipping, Elias; Kornhuber, Ralf; Rosenau, Matthias; Oncken, Onno

2016-03-01

We present a mathematically consistent numerical algorithm for the simulation of earthquake rupture with rate-and-state friction. Its main features are adaptive time stepping, a novel algebraic solution algorithm involving nonlinear multigrid and a fixed point iteration for the rate-and-state decoupling. The algorithm is applied to a laboratory scale subduction zone which allows us to compare our simulations with experimental results. Using physical parameters from the experiment, we find a good fit of recurrence time of slip events as well as their rupture width and peak slip. Computations in 3-D confirm efficiency and robustness of our algorithm.

Real-Time Stability Margin Measurements for X-38 Robustness Analysis

NASA Technical Reports Server (NTRS)

Bosworth, John T.; Stachowiak, Susan J.

2005-01-01

A method has been developed for real-time stability margin measurement calculations. The method relies on a tailored-forced excitation targeted to a specific frequency range. Computation of the frequency response is matched to the specific frequencies contained in the excitation. A recursive Fourier transformation is used to make the method compatible with real-time calculation. The method was incorporated into the X-38 nonlinear simulation and applied to an X-38 robustness test. X-38 stability margins were calculated for different variations in aerodynamic and mass properties over the vehicle flight trajectory. The new method showed results comparable to more traditional stability analysis techniques, and at the same time, this new method provided coverage that is more complete and increased efficiency.

Robust Quantum Computing using Molecules with Switchable Dipole

DTIC Science & Technology

2010-06-15

REPORT Robust quantum computing using molecules with switchable dipole 14. ABSTRACT 16. SECURITY CLASSIFICATION OF: Of the many systems studied to...Research Office P.O. Box 12211 Research Triangle Park, NC 27709-2211 15. SUBJECT TERMS Ultracold polar molecules, quantum computing , phase gates...From - To) 30-Aug-2006 Standard Form 298 (Rev 8/98) Prescribed by ANSI Std. Z39.18 - 31-Aug-2009 Robust quantum computing using molecules with

A scalable parallel black oil simulator on distributed memory parallel computers

NASA Astrophysics Data System (ADS)

Wang, Kun; Liu, Hui; Chen, Zhangxin

2015-11-01

This paper presents our work on developing a parallel black oil simulator for distributed memory computers based on our in-house parallel platform. The parallel simulator is designed to overcome the performance issues of common simulators that are implemented for personal computers and workstations. The finite difference method is applied to discretize the black oil model. In addition, some advanced techniques are employed to strengthen the robustness and parallel scalability of the simulator, including an inexact Newton method, matrix decoupling methods, and algebraic multigrid methods. A new multi-stage preconditioner is proposed to accelerate the solution of linear systems from the Newton methods. Numerical experiments show that our simulator is scalable and efficient, and is capable of simulating extremely large-scale black oil problems with tens of millions of grid blocks using thousands of MPI processes on parallel computers.

Robust and Effective Component-based Banknote Recognition for the Blind

PubMed Central

Hasanuzzaman, Faiz M.; Yang, Xiaodong; Tian, YingLi

2012-01-01

We develop a novel camera-based computer vision technology to automatically recognize banknotes for assisting visually impaired people. Our banknote recognition system is robust and effective with the following features: 1) high accuracy: high true recognition rate and low false recognition rate, 2) robustness: handles a variety of currency designs and bills in various conditions, 3) high efficiency: recognizes banknotes quickly, and 4) ease of use: helps blind users to aim the target for image capture. To make the system robust to a variety of conditions including occlusion, rotation, scaling, cluttered background, illumination change, viewpoint variation, and worn or wrinkled bills, we propose a component-based framework by using Speeded Up Robust Features (SURF). Furthermore, we employ the spatial relationship of matched SURF features to detect if there is a bill in the camera view. This process largely alleviates false recognition and can guide the user to correctly aim at the bill to be recognized. The robustness and generalizability of the proposed system is evaluated on a dataset including both positive images (with U.S. banknotes) and negative images (no U.S. banknotes) collected under a variety of conditions. The proposed algorithm, achieves 100% true recognition rate and 0% false recognition rate. Our banknote recognition system is also tested by blind users. PMID:22661884

Iterative Most-Likely Point Registration (IMLP): A Robust Algorithm for Computing Optimal Shape Alignment

PubMed Central

Billings, Seth D.; Boctor, Emad M.; Taylor, Russell H.

2015-01-01

We present a probabilistic registration algorithm that robustly solves the problem of rigid-body alignment between two shapes with high accuracy, by aptly modeling measurement noise in each shape, whether isotropic or anisotropic. For point-cloud shapes, the probabilistic framework additionally enables modeling locally-linear surface regions in the vicinity of each point to further improve registration accuracy. The proposed Iterative Most-Likely Point (IMLP) algorithm is formed as a variant of the popular Iterative Closest Point (ICP) algorithm, which iterates between point-correspondence and point-registration steps. IMLP’s probabilistic framework is used to incorporate a generalized noise model into both the correspondence and the registration phases of the algorithm, hence its name as a most-likely point method rather than a closest-point method. To efficiently compute the most-likely correspondences, we devise a novel search strategy based on a principal direction (PD)-tree search. We also propose a new approach to solve the generalized total-least-squares (GTLS) sub-problem of the registration phase, wherein the point correspondences are registered under a generalized noise model. Our GTLS approach has improved accuracy, efficiency, and stability compared to prior methods presented for this problem and offers a straightforward implementation using standard least squares. We evaluate the performance of IMLP relative to a large number of prior algorithms including ICP, a robust variant on ICP, Generalized ICP (GICP), and Coherent Point Drift (CPD), as well as drawing close comparison with the prior anisotropic registration methods of GTLS-ICP and A-ICP. The performance of IMLP is shown to be superior with respect to these algorithms over a wide range of noise conditions, outliers, and misalignments using both mesh and point-cloud representations of various shapes. PMID:25748700

A memory efficient user interface for CLIPS micro-computer applications

NASA Technical Reports Server (NTRS)

Sterle, Mark E.; Mayer, Richard J.; Jordan, Janice A.; Brodale, Howard N.; Lin, Min-Jin

1990-01-01

The goal of the Integrated Southern Pine Beetle Expert System (ISPBEX) is to provide expert level knowledge concerning treatment advice that is convenient and easy to use for Forest Service personnel. ISPBEX was developed in CLIPS and delivered on an IBM PC AT class micro-computer, operating with an MS/DOS operating system. This restricted the size of the run time system to 640K. In order to provide a robust expert system, with on-line explanation, help, and alternative actions menus, as well as features that allow the user to back up or execute 'what if' scenarios, a memory efficient menuing system was developed to interface with the CLIPS programs. By robust, we mean an expert system that (1) is user friendly, (2) provides reasonable solutions for a wide variety of domain specific problems, (3) explains why some solutions were suggested but others were not, and (4) provides technical information relating to the problem solution. Several advantages were gained by using this type of user interface (UI). First, by storing the menus on the hard disk (instead of main memory) during program execution, a more robust system could be implemented. Second, since the menus were built rapidly, development time was reduced. Third, the user may try a new scenario by backing up to any of the input screens and revising segments of the original input without having to retype all the information. And fourth, asserting facts from the menus provided for a dynamic and flexible fact base. This UI technology has been applied successfully in expert systems applications in forest management, agriculture, and manufacturing. This paper discusses the architecture of the UI system, human factors considerations, and the menu syntax design.

An improved 3D MoF method based on analytical partial derivatives

NASA Astrophysics Data System (ADS)

Chen, Xiang; Zhang, Xiong

2016-12-01

MoF (Moment of Fluid) method is one of the most accurate approaches among various surface reconstruction algorithms. As other second order methods, MoF method needs to solve an implicit optimization problem to obtain the optimal approximate surface. Therefore, the partial derivatives of the objective function have to be involved during the iteration for efficiency and accuracy. However, to the best of our knowledge, the derivatives are currently estimated numerically by finite difference approximation because it is very difficult to obtain the analytical derivatives of the object function for an implicit optimization problem. Employing numerical derivatives in an iteration not only increase the computational cost, but also deteriorate the convergence rate and robustness of the iteration due to their numerical error. In this paper, the analytical first order partial derivatives of the objective function are deduced for 3D problems. The analytical derivatives can be calculated accurately, so they are incorporated into the MoF method to improve its accuracy, efficiency and robustness. Numerical studies show that by using the analytical derivatives the iterations are converged in all mixed cells with the efficiency improvement of 3 to 4 times.

An innovative multimodal virtual platform for communication with devices in a natural way

NASA Astrophysics Data System (ADS)

Kinkar, Chhayarani R.; Golash, Richa; Upadhyay, Akhilesh R.

2012-03-01

As technology grows people are diverted and are more interested in communicating with machine or computer naturally. This will make machine more compact and portable by avoiding remote, keyboard etc. also it will help them to live in an environment free from electromagnetic waves. This thought has made 'recognition of natural modality in human computer interaction' a most appealing and promising research field. Simultaneously it has been observed that using single mode of interaction limit the complete utilization of commands as well as data flow. In this paper a multimodal platform, where out of many natural modalities like eye gaze, speech, voice, face etc. human gestures are combined with human voice is proposed which will minimize the mean square error. This will loosen the strict environment needed for accurate and robust interaction while using single mode. Gesture complement Speech, gestures are ideal for direct object manipulation and natural language is used for descriptive tasks. Human computer interaction basically requires two broad sections recognition and interpretation. Recognition and interpretation of natural modality in complex binary instruction is a tough task as it integrate real world to virtual environment. The main idea of the paper is to develop a efficient model for data fusion coming from heterogeneous sensors, camera and microphone. Through this paper we have analyzed that the efficiency is increased if heterogeneous data (image & voice) is combined at feature level using artificial intelligence. The long term goal of this paper is to design a robust system for physically not able or having less technical knowledge.

Fast Approximations of the Rotational Diffusion Tensor and their Application to Structural Assembly of Molecular Complexes

PubMed Central

Berlin, Konstantin; O’Leary, Dianne P.; Fushman, David

2011-01-01

We present and evaluate a rigid-body, deterministic, molecular docking method, called ELMDOCK, that relies solely on the three-dimensional structure of the individual components and the overall rotational diffusion tensor of the complex, obtained from nuclear spin-relaxation measurements. We also introduce a docking method, called ELMPATIDOCK, derived from ELMDOCK and based on the new concept of combining the shape-related restraints from rotational diffusion with those from residual dipolar couplings, along with ambiguous contact/interface-related restraints obtained from chemical shift perturbations. ELMDOCK and ELMPATIDOCK use two novel approximations of the molecular rotational diffusion tensor that allow computationally efficient docking. We show that these approximations are accurate enough to properly dock the two components of a complex without the need to recompute the diffusion tensor at each iteration step. We analyze the accuracy, robustness, and efficiency of these methods using synthetic relaxation data for a large variety of protein-protein complexes. We also test our method on three protein systems for which the structure of the complex and experimental relaxation data are available, and analyze the effect of flexible unstructured tails on the outcome of docking. Additionally, we describe a method for integrating the new approximation methods into the existing docking approaches that use the rotational diffusion tensor as a restraint. The results show that the proposed docking method is robust against experimental errors in the relaxation data or structural rearrangements upon complex formation and is computationally more efficient than current methods. The developed approximations are accurate enough to be used in structure refinement protocols. PMID:21604302

Fast approximations of the rotational diffusion tensor and their application to structural assembly of molecular complexes.

PubMed

Berlin, Konstantin; O'Leary, Dianne P; Fushman, David

2011-07-01

We present and evaluate a rigid-body, deterministic, molecular docking method, called ELMDOCK, that relies solely on the three-dimensional structure of the individual components and the overall rotational diffusion tensor of the complex, obtained from nuclear spin-relaxation measurements. We also introduce a docking method, called ELMPATIDOCK, derived from ELMDOCK and based on the new concept of combining the shape-related restraints from rotational diffusion with those from residual dipolar couplings, along with ambiguous contact/interface-related restraints obtained from chemical shift perturbations. ELMDOCK and ELMPATIDOCK use two novel approximations of the molecular rotational diffusion tensor that allow computationally efficient docking. We show that these approximations are accurate enough to properly dock the two components of a complex without the need to recompute the diffusion tensor at each iteration step. We analyze the accuracy, robustness, and efficiency of these methods using synthetic relaxation data for a large variety of protein-protein complexes. We also test our method on three protein systems for which the structure of the complex and experimental relaxation data are available, and analyze the effect of flexible unstructured tails on the outcome of docking. Additionally, we describe a method for integrating the new approximation methods into the existing docking approaches that use the rotational diffusion tensor as a restraint. The results show that the proposed docking method is robust against experimental errors in the relaxation data or structural rearrangements upon complex formation and is computationally more efficient than current methods. The developed approximations are accurate enough to be used in structure refinement protocols. Copyright © 2011 Wiley-Liss, Inc.

Research on improving image recognition robustness by combining multiple features with associative memory

NASA Astrophysics Data System (ADS)

Guo, Dongwei; Wang, Zhe

2018-05-01

Convolutional neural networks (CNN) achieve great success in computer vision, it can learn hierarchical representation from raw pixels and has outstanding performance in various image recognition tasks [1]. However, CNN is easy to be fraudulent in terms of it is possible to produce images totally unrecognizable to human eyes that CNNs believe with near certainty are familiar objects. [2]. In this paper, an associative memory model based on multiple features is proposed. Within this model, feature extraction and classification are carried out by CNN, T-SNE and exponential bidirectional associative memory neural network (EBAM). The geometric features extracted from CNN and the digital features extracted from T-SNE are associated by EBAM. Thus we ensure the recognition of robustness by a comprehensive assessment of the two features. In our model, we can get only 8% error rate with fraudulent data. In systems that require a high safety factor or some key areas, strong robustness is extremely important, if we can ensure the image recognition robustness, network security will be greatly improved and the social production efficiency will be extremely enhanced.

Robustness-Based Design Optimization Under Data Uncertainty

NASA Technical Reports Server (NTRS)

Zaman, Kais; McDonald, Mark; Mahadevan, Sankaran; Green, Lawrence

2010-01-01

This paper proposes formulations and algorithms for design optimization under both aleatory (i.e., natural or physical variability) and epistemic uncertainty (i.e., imprecise probabilistic information), from the perspective of system robustness. The proposed formulations deal with epistemic uncertainty arising from both sparse and interval data without any assumption about the probability distributions of the random variables. A decoupled approach is proposed in this paper to un-nest the robustness-based design from the analysis of non-design epistemic variables to achieve computational efficiency. The proposed methods are illustrated for the upper stage design problem of a two-stage-to-orbit (TSTO) vehicle, where the information on the random design inputs are only available as sparse point and/or interval data. As collecting more data reduces uncertainty but increases cost, the effect of sample size on the optimality and robustness of the solution is also studied. A method is developed to determine the optimal sample size for sparse point data that leads to the solutions of the design problem that are least sensitive to variations in the input random variables.

Supervised linear dimensionality reduction with robust margins for object recognition

NASA Astrophysics Data System (ADS)

Dornaika, F.; Assoum, A.

2013-01-01

Linear Dimensionality Reduction (LDR) techniques have been increasingly important in computer vision and pattern recognition since they permit a relatively simple mapping of data onto a lower dimensional subspace, leading to simple and computationally efficient classification strategies. Recently, many linear discriminant methods have been developed in order to reduce the dimensionality of visual data and to enhance the discrimination between different groups or classes. Many existing linear embedding techniques relied on the use of local margins in order to get a good discrimination performance. However, dealing with outliers and within-class diversity has not been addressed by margin-based embedding method. In this paper, we explored the use of different margin-based linear embedding methods. More precisely, we propose to use the concepts of Median miss and Median hit for building robust margin-based criteria. Based on such margins, we seek the projection directions (linear embedding) such that the sum of local margins is maximized. Our proposed approach has been applied to the problem of appearance-based face recognition. Experiments performed on four public face databases show that the proposed approach can give better generalization performance than the classic Average Neighborhood Margin Maximization (ANMM). Moreover, thanks to the use of robust margins, the proposed method down-grades gracefully when label outliers contaminate the training data set. In particular, we show that the concept of Median hit was crucial in order to get robust performance in the presence of outliers.

Robust Segmentation of Planar and Linear Features of Terrestrial Laser Scanner Point Clouds Acquired from Construction Sites.

PubMed

Maalek, Reza; Lichti, Derek D; Ruwanpura, Janaka Y

2018-03-08

Automated segmentation of planar and linear features of point clouds acquired from construction sites is essential for the automatic extraction of building construction elements such as columns, beams and slabs. However, many planar and linear segmentation methods use scene-dependent similarity thresholds that may not provide generalizable solutions for all environments. In addition, outliers exist in construction site point clouds due to data artefacts caused by moving objects, occlusions and dust. To address these concerns, a novel method for robust classification and segmentation of planar and linear features is proposed. First, coplanar and collinear points are classified through a robust principal components analysis procedure. The classified points are then grouped using a new robust clustering method, the robust complete linkage method. A robust method is also proposed to extract the points of flat-slab floors and/or ceilings independent of the aforementioned stages to improve computational efficiency. The applicability of the proposed method is evaluated in eight datasets acquired from a complex laboratory environment and two construction sites at the University of Calgary. The precision, recall, and accuracy of the segmentation at both construction sites were 96.8%, 97.7% and 95%, respectively. These results demonstrate the suitability of the proposed method for robust segmentation of planar and linear features of contaminated datasets, such as those collected from construction sites.

Robust Segmentation of Planar and Linear Features of Terrestrial Laser Scanner Point Clouds Acquired from Construction Sites

PubMed Central

Maalek, Reza; Lichti, Derek D; Ruwanpura, Janaka Y

2018-01-01

Automated segmentation of planar and linear features of point clouds acquired from construction sites is essential for the automatic extraction of building construction elements such as columns, beams and slabs. However, many planar and linear segmentation methods use scene-dependent similarity thresholds that may not provide generalizable solutions for all environments. In addition, outliers exist in construction site point clouds due to data artefacts caused by moving objects, occlusions and dust. To address these concerns, a novel method for robust classification and segmentation of planar and linear features is proposed. First, coplanar and collinear points are classified through a robust principal components analysis procedure. The classified points are then grouped using a new robust clustering method, the robust complete linkage method. A robust method is also proposed to extract the points of flat-slab floors and/or ceilings independent of the aforementioned stages to improve computational efficiency. The applicability of the proposed method is evaluated in eight datasets acquired from a complex laboratory environment and two construction sites at the University of Calgary. The precision, recall, and accuracy of the segmentation at both construction sites were 96.8%, 97.7% and 95%, respectively. These results demonstrate the suitability of the proposed method for robust segmentation of planar and linear features of contaminated datasets, such as those collected from construction sites. PMID:29518062

Toward an optimal solver for time-spectral fluid-dynamic and aeroelastic solutions on unstructured meshes

NASA Astrophysics Data System (ADS)

Mundis, Nathan L.; Mavriplis, Dimitri J.

2017-09-01

The time-spectral method applied to the Euler and coupled aeroelastic equations theoretically offers significant computational savings for purely periodic problems when compared to standard time-implicit methods. However, attaining superior efficiency with time-spectral methods over traditional time-implicit methods hinges on the ability rapidly to solve the large non-linear system resulting from time-spectral discretizations which become larger and stiffer as more time instances are employed or the period of the flow becomes especially short (i.e. the maximum resolvable wave-number increases). In order to increase the efficiency of these solvers, and to improve robustness, particularly for large numbers of time instances, the Generalized Minimal Residual Method (GMRES) is used to solve the implicit linear system over all coupled time instances. The use of GMRES as the linear solver makes time-spectral methods more robust, allows them to be applied to a far greater subset of time-accurate problems, including those with a broad range of harmonic content, and vastly improves the efficiency of time-spectral methods. In previous work, a wave-number independent preconditioner that mitigates the increased stiffness of the time-spectral method when applied to problems with large resolvable wave numbers has been developed. This preconditioner, however, directly inverts a large matrix whose size increases in proportion to the number of time instances. As a result, the computational time of this method scales as the cube of the number of time instances. In the present work, this preconditioner has been reworked to take advantage of an approximate-factorization approach that effectively decouples the spatial and temporal systems. Once decoupled, the time-spectral matrix can be inverted in frequency space, where it has entries only on the main diagonal and therefore can be inverted quite efficiently. This new GMRES/preconditioner combination is shown to be over an order of magnitude more efficient than the previous wave-number independent preconditioner for problems with large numbers of time instances and/or large reduced frequencies.

Robustness analysis of bogie suspension components Pareto optimised values

NASA Astrophysics Data System (ADS)

Mousavi Bideleh, Seyed Milad

2017-08-01

Bogie suspension system of high speed trains can significantly affect vehicle performance. Multiobjective optimisation problems are often formulated and solved to find the Pareto optimised values of the suspension components and improve cost efficiency in railway operations from different perspectives. Uncertainties in the design parameters of suspension system can negatively influence the dynamics behaviour of railway vehicles. In this regard, robustness analysis of a bogie dynamics response with respect to uncertainties in the suspension design parameters is considered. A one-car railway vehicle model with 50 degrees of freedom and wear/comfort Pareto optimised values of bogie suspension components is chosen for the analysis. Longitudinal and lateral primary stiffnesses, longitudinal and vertical secondary stiffnesses, as well as yaw damping are considered as five design parameters. The effects of parameter uncertainties on wear, ride comfort, track shift force, stability, and risk of derailment are studied by varying the design parameters around their respective Pareto optimised values according to a lognormal distribution with different coefficient of variations (COVs). The robustness analysis is carried out based on the maximum entropy concept. The multiplicative dimensional reduction method is utilised to simplify the calculation of fractional moments and improve the computational efficiency. The results showed that the dynamics response of the vehicle with wear/comfort Pareto optimised values of bogie suspension is robust against uncertainties in the design parameters and the probability of failure is small for parameter uncertainties with COV up to 0.1.

A computationally efficient ductile damage model accounting for nucleation and micro-inertia at high triaxialities

DOE PAGES

Versino, Daniele; Bronkhorst, Curt Allan

2018-01-31

The computational formulation of a micro-mechanical material model for the dynamic failure of ductile metals is presented in this paper. The statistical nature of porosity initiation is accounted for by introducing an arbitrary probability density function which describes the pores nucleation pressures. Each micropore within the representative volume element is modeled as a thick spherical shell made of plastically incompressible material. The treatment of porosity by a distribution of thick-walled spheres also allows for the inclusion of micro-inertia effects under conditions of shock and dynamic loading. The second order ordinary differential equation governing the microscopic porosity evolution is solved withmore » a robust implicit procedure. A new Chebyshev collocation method is employed to approximate the porosity distribution and remapping is used to optimize memory usage. The adaptive approximation of the porosity distribution leads to a reduction of computational time and memory usage of up to two orders of magnitude. Moreover, the proposed model affords consistent performance: changing the nucleation pressure probability density function and/or the applied strain rate does not reduce accuracy or computational efficiency of the material model. The numerical performance of the model and algorithms presented is tested against three problems for high density tantalum: single void, one-dimensional uniaxial strain, and two-dimensional plate impact. Here, the results using the integration and algorithmic advances suggest a significant improvement in computational efficiency and accuracy over previous treatments for dynamic loading conditions.« less

Boolean and brain-inspired computing using spin-transfer torque devices

NASA Astrophysics Data System (ADS)

Fan, Deliang

Several completely new approaches (such as spintronic, carbon nanotube, graphene, TFETs, etc.) to information processing and data storage technologies are emerging to address the time frame beyond current Complementary Metal-Oxide-Semiconductor (CMOS) roadmap. The high speed magnetization switching of a nano-magnet due to current induced spin-transfer torque (STT) have been demonstrated in recent experiments. Such STT devices can be explored in compact, low power memory and logic design. In order to truly leverage STT devices based computing, researchers require a re-think of circuit, architecture, and computing model, since the STT devices are unlikely to be drop-in replacements for CMOS. The potential of STT devices based computing will be best realized by considering new computing models that are inherently suited to the characteristics of STT devices, and new applications that are enabled by their unique capabilities, thereby attaining performance that CMOS cannot achieve. The goal of this research is to conduct synergistic exploration in architecture, circuit and device levels for Boolean and brain-inspired computing using nanoscale STT devices. Specifically, we first show that the non-volatile STT devices can be used in designing configurable Boolean logic blocks. We propose a spin-memristor threshold logic (SMTL) gate design, where memristive cross-bar array is used to perform current mode summation of binary inputs and the low power current mode spintronic threshold device carries out the energy efficient threshold operation. Next, for brain-inspired computing, we have exploited different spin-transfer torque device structures that can implement the hard-limiting and soft-limiting artificial neuron transfer functions respectively. We apply such STT based neuron (or 'spin-neuron') in various neural network architectures, such as hierarchical temporal memory and feed-forward neural network, for performing "human-like" cognitive computing, which show more than two orders of lower energy consumption compared to state of the art CMOS implementation. Finally, we show the dynamics of injection locked Spin Hall Effect Spin-Torque Oscillator (SHE-STO) cluster can be exploited as a robust multi-dimensional distance metric for associative computing, image/ video analysis, etc. Our simulation results show that the proposed system architecture with injection locked SHE-STOs and the associated CMOS interface circuits can be suitable for robust and energy efficient associative computing and pattern matching.

Algorithmics - Is There Hope for a Unified Theory?

NASA Astrophysics Data System (ADS)

Hromkovič, Juraj

Computer science was born with the formal definition of the notion of an algorithm. This definition provides clear limits of automatization, separating problems into algorithmically solvable problems and algorithmically unsolvable ones. The second big bang of computer science was the development of the concept of computational complexity. People recognized that problems that do not admit efficient algorithms are not solvable in practice. The search for a reasonable, clear and robust definition of the class of practically solvable algorithmic tasks started with the notion of the class {P} and of {NP}-completeness. In spite of the fact that this robust concept is still fundamental for judging the hardness of computational problems, a variety of approaches was developed for solving instances of {NP}-hard problems in many applications. Our 40-years short attempt to fix the fuzzy border between the practically solvable problems and the practically unsolvable ones partially reminds of the never-ending search for the definition of "life" in biology or for the definitions of matter and energy in physics. Can the search for the formal notion of "practical solvability" also become a never-ending story or is there hope for getting a well-accepted, robust definition of it? Hopefully, it is not surprising that we are not able to answer this question in this invited talk. But to deal with this question is of crucial importance, because only due to enormous effort scientists get a better and better feeling of what the fundamental notions of science like life and energy mean. In the flow of numerous technical results, we must not forget the fact that most of the essential revolutionary contributions to science were done by defining new concepts and notions.

Aerothermodynamic Design Sensitivities for a Reacting Gas Flow Solver on an Unstructured Mesh Using a Discrete Adjoint Formulation

NASA Astrophysics Data System (ADS)

Thompson, Kyle Bonner

An algorithm is described to efficiently compute aerothermodynamic design sensitivities using a decoupled variable set. In a conventional approach to computing design sensitivities for reacting flows, the species continuity equations are fully coupled to the conservation laws for momentum and energy. In this algorithm, the species continuity equations are solved separately from the mixture continuity, momentum, and total energy equations. This decoupling simplifies the implicit system, so that the flow solver can be made significantly more efficient, with very little penalty on overall scheme robustness. Most importantly, the computational cost of the point implicit relaxation is shown to scale linearly with the number of species for the decoupled system, whereas the fully coupled approach scales quadratically. Also, the decoupled method significantly reduces the cost in wall time and memory in comparison to the fully coupled approach. This decoupled approach for computing design sensitivities with the adjoint system is demonstrated for inviscid flow in chemical non-equilibrium around a re-entry vehicle with a retro-firing annular nozzle. The sensitivities of the surface temperature and mass flow rate through the nozzle plenum are computed with respect to plenum conditions and verified against sensitivities computed using a complex-variable finite-difference approach. The decoupled scheme significantly reduces the computational time and memory required to complete the optimization, making this an attractive method for high-fidelity design of hypersonic vehicles.

The novel high-performance 3-D MT inverse solver

NASA Astrophysics Data System (ADS)

Kruglyakov, Mikhail; Geraskin, Alexey; Kuvshinov, Alexey

2016-04-01

We present novel, robust, scalable, and fast 3-D magnetotelluric (MT) inverse solver. The solver is written in multi-language paradigm to make it as efficient, readable and maintainable as possible. Separation of concerns and single responsibility concepts go through implementation of the solver. As a forward modelling engine a modern scalable solver extrEMe, based on contracting integral equation approach, is used. Iterative gradient-type (quasi-Newton) optimization scheme is invoked to search for (regularized) inverse problem solution, and adjoint source approach is used to calculate efficiently the gradient of the misfit. The inverse solver is able to deal with highly detailed and contrasting models, allows for working (separately or jointly) with any type of MT responses, and supports massive parallelization. Moreover, different parallelization strategies implemented in the code allow optimal usage of available computational resources for a given problem statement. To parameterize an inverse domain the so-called mask parameterization is implemented, which means that one can merge any subset of forward modelling cells in order to account for (usually) irregular distribution of observation sites. We report results of 3-D numerical experiments aimed at analysing the robustness, performance and scalability of the code. In particular, our computational experiments carried out at different platforms ranging from modern laptops to HPC Piz Daint (6th supercomputer in the world) demonstrate practically linear scalability of the code up to thousands of nodes.

Effects of high-frequency damping on iterative convergence of implicit viscous solver

NASA Astrophysics Data System (ADS)

Nishikawa, Hiroaki; Nakashima, Yoshitaka; Watanabe, Norihiko

2017-11-01

This paper discusses effects of high-frequency damping on iterative convergence of an implicit defect-correction solver for viscous problems. The study targets a finite-volume discretization with a one parameter family of damped viscous schemes. The parameter α controls high-frequency damping: zero damping with α = 0, and larger damping for larger α (> 0). Convergence rates are predicted for a model diffusion equation by a Fourier analysis over a practical range of α. It is shown that the convergence rate attains its minimum at α = 1 on regular quadrilateral grids, and deteriorates for larger values of α. A similar behavior is observed for regular triangular grids. In both quadrilateral and triangular grids, the solver is predicted to diverge for α smaller than approximately 0.5. Numerical results are shown for the diffusion equation and the Navier-Stokes equations on regular and irregular grids. The study suggests that α = 1 and 4/3 are suitable values for robust and efficient computations, and α = 4 / 3 is recommended for the diffusion equation, which achieves higher-order accuracy on regular quadrilateral grids. Finally, a Jacobian-Free Newton-Krylov solver with the implicit solver (a low-order Jacobian approximately inverted by a multi-color Gauss-Seidel relaxation scheme) used as a variable preconditioner is recommended for practical computations, which provides robust and efficient convergence for a wide range of α.

Interface COMSOL-PHREEQC (iCP), an efficient numerical framework for the solution of coupled multiphysics and geochemistry

NASA Astrophysics Data System (ADS)

Nardi, Albert; Idiart, Andrés; Trinchero, Paolo; de Vries, Luis Manuel; Molinero, Jorge

2014-08-01

This paper presents the development, verification and application of an efficient interface, denoted as iCP, which couples two standalone simulation programs: the general purpose Finite Element framework COMSOL Multiphysics® and the geochemical simulator PHREEQC. The main goal of the interface is to maximize the synergies between the aforementioned codes, providing a numerical platform that can efficiently simulate a wide number of multiphysics problems coupled with geochemistry. iCP is written in Java and uses the IPhreeqc C++ dynamic library and the COMSOL Java-API. Given the large computational requirements of the aforementioned coupled models, special emphasis has been placed on numerical robustness and efficiency. To this end, the geochemical reactions are solved in parallel by balancing the computational load over multiple threads. First, a benchmark exercise is used to test the reliability of iCP regarding flow and reactive transport. Then, a large scale thermo-hydro-chemical (THC) problem is solved to show the code capabilities. The results of the verification exercise are successfully compared with those obtained using PHREEQC and the application case demonstrates the scalability of a large scale model, at least up to 32 threads.

Efficient iterative method for solving the Dirac-Kohn-Sham density functional theory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lin, Lin; Shao, Sihong; E, Weinan

2012-11-06

We present for the first time an efficient iterative method to directly solve the four-component Dirac-Kohn-Sham (DKS) density functional theory. Due to the existence of the negative energy continuum in the DKS operator, the existing iterative techniques for solving the Kohn-Sham systems cannot be efficiently applied to solve the DKS systems. The key component of our method is a novel filtering step (F) which acts as a preconditioner in the framework of the locally optimal block preconditioned conjugate gradient (LOBPCG) method. The resulting method, dubbed the LOBPCG-F method, is able to compute the desired eigenvalues and eigenvectors in the positive energy band without computing any state in the negative energy band. The LOBPCG-F method introduces mild extra cost compared to the standard LOBPCG method and can be easily implemented. We demonstrate our method in the pseudopotential framework with a planewave basis set which naturally satisfies the kinetic balance prescription. Numerical results for Ptmore » $$_{2}$$, Au$$_{2}$$, TlF, and Bi$$_{2}$$Se$$_{3}$$ indicate that the LOBPCG-F method is a robust and efficient method for investigating the relativistic effect in systems containing heavy elements.« less

Energy efficient wireless sensor network for structural health monitoring using distributed embedded piezoelectric transducers

NASA Astrophysics Data System (ADS)

Li, Peng; Olmi, Claudio; Song, Gangbing

2010-04-01

Piezoceramic based transducers are widely researched and used for structural health monitoring (SHM) systems due to the piezoceramic material's inherent advantage of dual sensing and actuation. Wireless sensor network (WSN) technology benefits from advances made in piezoceramic based structural health monitoring systems, allowing easy and flexible installation, low system cost, and increased robustness over wired system. However, piezoceramic wireless SHM systems still faces some drawbacks, one of these is that the piezoceramic based SHM systems require relatively high computational capabilities to calculate damage information, however, battery powered WSN sensor nodes have strict power consumption limitation and hence limited computational power. On the other hand, commonly used centralized processing networks require wireless sensors to transmit all data back to the network coordinator for analysis. This signal processing procedure can be problematic for piezoceramic based SHM applications as it is neither energy efficient nor robust. In this paper, we aim to solve these problems with a distributed wireless sensor network for piezoceramic base structural health monitoring systems. Three important issues: power system, waking up from sleep impact detection, and local data processing, are addressed to reach optimized energy efficiency. Instead of sweep sine excitation that was used in the early research, several sine frequencies were used in sequence to excite the concrete structure. The wireless sensors record the sine excitations and compute the time domain energy for each sine frequency locally to detect the energy change. By comparing the data of the damaged concrete frame with the healthy data, we are able to find out the damage information of the concrete frame. A relative powerful wireless microcontroller was used to carry out the sampling and distributed data processing in real-time. The distributed wireless network dramatically reduced the data transmission between wireless sensor and the wireless coordinator, which in turn reduced the power consumption of the overall system.

Semiquantum key distribution with secure delegated quantum computation

PubMed Central

Li, Qin; Chan, Wai Hong; Zhang, Shengyu

2016-01-01

Semiquantum key distribution allows a quantum party to share a random key with a “classical” party who only can prepare and measure qubits in the computational basis or reorder some qubits when he has access to a quantum channel. In this work, we present a protocol where a secret key can be established between a quantum user and an almost classical user who only needs the quantum ability to access quantum channels, by securely delegating quantum computation to a quantum server. We show the proposed protocol is robust even when the delegated quantum server is a powerful adversary, and is experimentally feasible with current technology. As one party of our protocol is the most quantum-resource efficient, it can be more practical and significantly widen the applicability scope of quantum key distribution. PMID:26813384

A Benders based rolling horizon algorithm for a dynamic facility location problem

DOE PAGES

Marufuzzaman,, Mohammad; Gedik, Ridvan; Roni, Mohammad S.

2016-06-28

This study presents a well-known capacitated dynamic facility location problem (DFLP) that satisfies the customer demand at a minimum cost by determining the time period for opening, closing, or retaining an existing facility in a given location. To solve this challenging NP-hard problem, this paper develops a unique hybrid solution algorithm that combines a rolling horizon algorithm with an accelerated Benders decomposition algorithm. Extensive computational experiments are performed on benchmark test instances to evaluate the hybrid algorithm’s efficiency and robustness in solving the DFLP problem. Computational results indicate that the hybrid Benders based rolling horizon algorithm consistently offers high qualitymore » feasible solutions in a much shorter computational time period than the standalone rolling horizon and accelerated Benders decomposition algorithms in the experimental range.« less

Transformational electronics: a powerful way to revolutionize our information world

NASA Astrophysics Data System (ADS)

Rojas, Jhonathan P.; Torres Sevilla, Galo A.; Ghoneim, Mohamed T.; Hussain, Aftab M.; Ahmed, Sally M.; Nassar, Joanna M.; Bahabry, Rabab R.; Nour, Maha; Kutbee, Arwa T.; Byas, Ernesto; Al-Saif, Bidoor; Alamri, Amal M.; Hussain, Muhammad M.

2014-06-01

With the emergence of cloud computation, we are facing the rising waves of big data. It is our time to leverage such opportunity by increasing data usage both by man and machine. We need ultra-mobile computation with high data processing speed, ultra-large memory, energy efficiency and multi-functionality. Additionally, we have to deploy energy-efficient multi-functional 3D ICs for robust cyber-physical system establishment. To achieve such lofty goals we have to mimic human brain, which is inarguably the world's most powerful and energy efficient computer. Brain's cortex has folded architecture to increase surface area in an ultra-compact space to contain its neuron and synapses. Therefore, it is imperative to overcome two integration challenges: (i) finding out a low-cost 3D IC fabrication process and (ii) foldable substrates creation with ultra-large-scale-integration of high performance energy efficient electronics. Hence, we show a low-cost generic batch process based on trench-protect-peel-recycle to fabricate rigid and flexible 3D ICs as well as high performance flexible electronics. As of today we have made every single component to make a fully flexible computer including non-planar state-of-the-art FinFETs. Additionally we have demonstrated various solid-state memory, movable MEMS devices, energy harvesting and storage components. To show the versatility of our process, we have extended our process towards other inorganic semiconductor substrates such as silicon germanium and III-V materials. Finally, we report first ever fully flexible programmable silicon based microprocessor towards foldable brain computation and wirelessly programmable stretchable and flexible thermal patch for pain management for smart bionics.

Algorithms for Efficient Computation of Transfer Functions for Large Order Flexible Systems

NASA Technical Reports Server (NTRS)

Maghami, Peiman G.; Giesy, Daniel P.

1998-01-01

An efficient and robust computational scheme is given for the calculation of the frequency response function of a large order, flexible system implemented with a linear, time invariant control system. Advantage is taken of the highly structured sparsity of the system matrix of the plant based on a model of the structure using normal mode coordinates. The computational time per frequency point of the new computational scheme is a linear function of system size, a significant improvement over traditional, still-matrix techniques whose computational times per frequency point range from quadratic to cubic functions of system size. This permits the practical frequency domain analysis of systems of much larger order than by traditional, full-matrix techniques. Formulations are given for both open- and closed-loop systems. Numerical examples are presented showing the advantages of the present formulation over traditional approaches, both in speed and in accuracy. Using a model with 703 structural modes, the present method was up to two orders of magnitude faster than a traditional method. The present method generally showed good to excellent accuracy throughout the range of test frequencies, while traditional methods gave adequate accuracy for lower frequencies, but generally deteriorated in performance at higher frequencies with worst case errors being many orders of magnitude times the correct values.

From Three-Photon Greenberger-Horne-Zeilinger States to Ballistic Universal Quantum Computation.

PubMed

Gimeno-Segovia, Mercedes; Shadbolt, Pete; Browne, Dan E; Rudolph, Terry

2015-07-10

Single photons, manipulated using integrated linear optics, constitute a promising platform for universal quantum computation. A series of increasingly efficient proposals have shown linear-optical quantum computing to be formally scalable. However, existing schemes typically require extensive adaptive switching, which is experimentally challenging and noisy, thousands of photon sources per renormalized qubit, and/or large quantum memories for repeat-until-success strategies. Our work overcomes all these problems. We present a scheme to construct a cluster state universal for quantum computation, which uses no adaptive switching, no large memories, and which is at least an order of magnitude more resource efficient than previous passive schemes. Unlike previous proposals, it is constructed entirely from loss-detecting gates and offers a robustness to photon loss. Even without the use of an active loss-tolerant encoding, our scheme naturally tolerates a total loss rate ∼1.6% in the photons detected in the gates. This scheme uses only 3 Greenberger-Horne-Zeilinger states as a resource, together with a passive linear-optical network. We fully describe and model the iterative process of cluster generation, including photon loss and gate failure. This demonstrates that building a linear-optical quantum computer needs to be less challenging than previously thought.

Modeling of the WSTF frictional heating apparatus in high pressure systems

NASA Technical Reports Server (NTRS)

Skowlund, Christopher T.

1992-01-01

In order to develop a computer program able to model the frictional heating of metals in high pressure oxygen or nitrogen a number of additions have been made to the frictional heating model originally developed for tests in low pressure helium. These additions include: (1) a physical property package for the gases to account for departures from the ideal gas state; (2) two methods for spatial discretization (finite differences with quadratic interpolation or orthogonal collocation on finite elements) which substantially reduce the computer time required to solve the transient heat balance; (3) more efficient programs for the integration of the ordinary differential equations resulting from the discretization of the partial differential equations; and (4) two methods for determining the best-fit parameters via minimization of the mean square error (either a direct search multivariable simplex method or a modified Levenburg-Marquardt algorithm). The resulting computer program has been shown to be accurate, efficient and robust for determining the heat flux or friction coefficient vs. time at the interface of the stationary and rotating samples.

Real-time probabilistic covariance tracking with efficient model update.

PubMed

Wu, Yi; Cheng, Jian; Wang, Jinqiao; Lu, Hanqing; Wang, Jun; Ling, Haibin; Blasch, Erik; Bai, Li

2012-05-01

The recently proposed covariance region descriptor has been proven robust and versatile for a modest computational cost. The covariance matrix enables efficient fusion of different types of features, where the spatial and statistical properties, as well as their correlation, are characterized. The similarity between two covariance descriptors is measured on Riemannian manifolds. Based on the same metric but with a probabilistic framework, we propose a novel tracking approach on Riemannian manifolds with a novel incremental covariance tensor learning (ICTL). To address the appearance variations, ICTL incrementally learns a low-dimensional covariance tensor representation and efficiently adapts online to appearance changes of the target with only O(1) computational complexity, resulting in a real-time performance. The covariance-based representation and the ICTL are then combined with the particle filter framework to allow better handling of background clutter, as well as the temporary occlusions. We test the proposed probabilistic ICTL tracker on numerous benchmark sequences involving different types of challenges including occlusions and variations in illumination, scale, and pose. The proposed approach demonstrates excellent real-time performance, both qualitatively and quantitatively, in comparison with several previously proposed trackers.

Efficient and accurate two-scale FE-FFT-based prediction of the effective material behavior of elasto-viscoplastic polycrystals

NASA Astrophysics Data System (ADS)

Kochmann, Julian; Wulfinghoff, Stephan; Ehle, Lisa; Mayer, Joachim; Svendsen, Bob; Reese, Stefanie

2018-06-01

Recently, two-scale FE-FFT-based methods (e.g., Spahn et al. in Comput Methods Appl Mech Eng 268:871-883, 2014; Kochmann et al. in Comput Methods Appl Mech Eng 305:89-110, 2016) have been proposed to predict the microscopic and overall mechanical behavior of heterogeneous materials. The purpose of this work is the extension to elasto-viscoplastic polycrystals, efficient and robust Fourier solvers and the prediction of micromechanical fields during macroscopic deformation processes. Assuming scale separation, the macroscopic problem is solved using the finite element method. The solution of the microscopic problem, which is embedded as a periodic unit cell (UC) in each macroscopic integration point, is found by employing fast Fourier transforms, fixed-point and Newton-Krylov methods. The overall material behavior is defined by the mean UC response. In order to ensure spatially converged micromechanical fields as well as feasible overall CPU times, an efficient but simple solution strategy for two-scale simulations is proposed. As an example, the constitutive behavior of 42CrMo4 steel is predicted during macroscopic three-point bending tests.

Artificial Neuron Based on Integrated Semiconductor Quantum Dot Mode-Locked Lasers

NASA Astrophysics Data System (ADS)

Mesaritakis, Charis; Kapsalis, Alexandros; Bogris, Adonis; Syvridis, Dimitris

2016-12-01

Neuro-inspired implementations have attracted strong interest as a power efficient and robust alternative to the digital model of computation with a broad range of applications. Especially, neuro-mimetic systems able to produce and process spike-encoding schemes can offer merits like high noise-resiliency and increased computational efficiency. Towards this direction, integrated photonics can be an auspicious platform due to its multi-GHz bandwidth, its high wall-plug efficiency and the strong similarity of its dynamics under excitation with biological spiking neurons. Here, we propose an integrated all-optical neuron based on an InAs/InGaAs semiconductor quantum-dot passively mode-locked laser. The multi-band emission capabilities of these lasers allows, through waveband switching, the emulation of the excitation and inhibition modes of operation. Frequency-response effects, similar to biological neural circuits, are observed just as in a typical two-section excitable laser. The demonstrated optical building block can pave the way for high-speed photonic integrated systems able to address tasks ranging from pattern recognition to cognitive spectrum management and multi-sensory data processing.

Efficient and accurate two-scale FE-FFT-based prediction of the effective material behavior of elasto-viscoplastic polycrystals

NASA Astrophysics Data System (ADS)

Kochmann, Julian; Wulfinghoff, Stephan; Ehle, Lisa; Mayer, Joachim; Svendsen, Bob; Reese, Stefanie

2017-09-01

Recently, two-scale FE-FFT-based methods (e.g., Spahn et al. in Comput Methods Appl Mech Eng 268:871-883, 2014; Kochmann et al. in Comput Methods Appl Mech Eng 305:89-110, 2016) have been proposed to predict the microscopic and overall mechanical behavior of heterogeneous materials. The purpose of this work is the extension to elasto-viscoplastic polycrystals, efficient and robust Fourier solvers and the prediction of micromechanical fields during macroscopic deformation processes. Assuming scale separation, the macroscopic problem is solved using the finite element method. The solution of the microscopic problem, which is embedded as a periodic unit cell (UC) in each macroscopic integration point, is found by employing fast Fourier transforms, fixed-point and Newton-Krylov methods. The overall material behavior is defined by the mean UC response. In order to ensure spatially converged micromechanical fields as well as feasible overall CPU times, an efficient but simple solution strategy for two-scale simulations is proposed. As an example, the constitutive behavior of 42CrMo4 steel is predicted during macroscopic three-point bending tests.

Artificial Neuron Based on Integrated Semiconductor Quantum Dot Mode-Locked Lasers

PubMed Central

Mesaritakis, Charis; Kapsalis, Alexandros; Bogris, Adonis; Syvridis, Dimitris

2016-01-01

Neuro-inspired implementations have attracted strong interest as a power efficient and robust alternative to the digital model of computation with a broad range of applications. Especially, neuro-mimetic systems able to produce and process spike-encoding schemes can offer merits like high noise-resiliency and increased computational efficiency. Towards this direction, integrated photonics can be an auspicious platform due to its multi-GHz bandwidth, its high wall-plug efficiency and the strong similarity of its dynamics under excitation with biological spiking neurons. Here, we propose an integrated all-optical neuron based on an InAs/InGaAs semiconductor quantum-dot passively mode-locked laser. The multi-band emission capabilities of these lasers allows, through waveband switching, the emulation of the excitation and inhibition modes of operation. Frequency-response effects, similar to biological neural circuits, are observed just as in a typical two-section excitable laser. The demonstrated optical building block can pave the way for high-speed photonic integrated systems able to address tasks ranging from pattern recognition to cognitive spectrum management and multi-sensory data processing. PMID:27991574

DREAMS and IMAGE: A Model and Computer Implementation for Concurrent, Life-Cycle Design of Complex Systems

NASA Technical Reports Server (NTRS)

Hale, Mark A.; Craig, James I.; Mistree, Farrokh; Schrage, Daniel P.

1995-01-01

Computing architectures are being assembled that extend concurrent engineering practices by providing more efficient execution and collaboration on distributed, heterogeneous computing networks. Built on the successes of initial architectures, requirements for a next-generation design computing infrastructure can be developed. These requirements concentrate on those needed by a designer in decision-making processes from product conception to recycling and can be categorized in two areas: design process and design information management. A designer both designs and executes design processes throughout design time to achieve better product and process capabilities while expanding fewer resources. In order to accomplish this, information, or more appropriately design knowledge, needs to be adequately managed during product and process decomposition as well as recomposition. A foundation has been laid that captures these requirements in a design architecture called DREAMS (Developing Robust Engineering Analysis Models and Specifications). In addition, a computing infrastructure, called IMAGE (Intelligent Multidisciplinary Aircraft Generation Environment), is being developed that satisfies design requirements defined in DREAMS and incorporates enabling computational technologies.

Automated Development of Accurate Algorithms and Efficient Codes for Computational Aeroacoustics

NASA Technical Reports Server (NTRS)

Goodrich, John W.; Dyson, Rodger W.

1999-01-01

The simulation of sound generation and propagation in three space dimensions with realistic aircraft components is a very large time dependent computation with fine details. Simulations in open domains with embedded objects require accurate and robust algorithms for propagation, for artificial inflow and outflow boundaries, and for the definition of geometrically complex objects. The development, implementation, and validation of methods for solving these demanding problems is being done to support the NASA pillar goals for reducing aircraft noise levels. Our goal is to provide algorithms which are sufficiently accurate and efficient to produce usable results rapidly enough to allow design engineers to study the effects on sound levels of design changes in propulsion systems, and in the integration of propulsion systems with airframes. There is a lack of design tools for these purposes at this time. Our technical approach to this problem combines the development of new, algorithms with the use of Mathematica and Unix utilities to automate the algorithm development, code implementation, and validation. We use explicit methods to ensure effective implementation by domain decomposition for SPMD parallel computing. There are several orders of magnitude difference in the computational efficiencies of the algorithms which we have considered. We currently have new artificial inflow and outflow boundary conditions that are stable, accurate, and unobtrusive, with implementations that match the accuracy and efficiency of the propagation methods. The artificial numerical boundary treatments have been proven to have solutions which converge to the full open domain problems, so that the error from the boundary treatments can be driven as low as is required. The purpose of this paper is to briefly present a method for developing highly accurate algorithms for computational aeroacoustics, the use of computer automation in this process, and a brief survey of the algorithms that have resulted from this work. A review of computational aeroacoustics has recently been given by Lele.

Perspective: Memcomputing: Leveraging memory and physics to compute efficiently

NASA Astrophysics Data System (ADS)

Di Ventra, Massimiliano; Traversa, Fabio L.

2018-05-01

It is well known that physical phenomena may be of great help in computing some difficult problems efficiently. A typical example is prime factorization that may be solved in polynomial time by exploiting quantum entanglement on a quantum computer. There are, however, other types of (non-quantum) physical properties that one may leverage to compute efficiently a wide range of hard problems. In this perspective, we discuss how to employ one such property, memory (time non-locality), in a novel physics-based approach to computation: Memcomputing. In particular, we focus on digital memcomputing machines (DMMs) that are scalable. DMMs can be realized with non-linear dynamical systems with memory. The latter property allows the realization of a new type of Boolean logic, one that is self-organizing. Self-organizing logic gates are "terminal-agnostic," namely, they do not distinguish between the input and output terminals. When appropriately assembled to represent a given combinatorial/optimization problem, the corresponding self-organizing circuit converges to the equilibrium points that express the solutions of the problem at hand. In doing so, DMMs take advantage of the long-range order that develops during the transient dynamics. This collective dynamical behavior, reminiscent of a phase transition, or even the "edge of chaos," is mediated by families of classical trajectories (instantons) that connect critical points of increasing stability in the system's phase space. The topological character of the solution search renders DMMs robust against noise and structural disorder. Since DMMs are non-quantum systems described by ordinary differential equations, not only can they be built in hardware with the available technology, they can also be simulated efficiently on modern classical computers. As an example, we will show the polynomial-time solution of the subset-sum problem for the worst cases, and point to other types of hard problems where simulations of DMMs' equations of motion on classical computers have already demonstrated substantial advantages over traditional approaches. We conclude this article by outlining further directions of study.

Unified commutation-pruning technique for efficient computation of composite DFTs

NASA Astrophysics Data System (ADS)

Castro-Palazuelos, David E.; Medina-Melendrez, Modesto Gpe.; Torres-Roman, Deni L.; Shkvarko, Yuriy V.

2015-12-01

An efficient computation of a composite length discrete Fourier transform (DFT), as well as a fast Fourier transform (FFT) of both time and space data sequences in uncertain (non-sparse or sparse) computational scenarios, requires specific processing algorithms. Traditional algorithms typically employ some pruning methods without any commutations, which prevents them from attaining the potential computational efficiency. In this paper, we propose an alternative unified approach with automatic commutations between three computational modalities aimed at efficient computations of the pruned DFTs adapted for variable composite lengths of the non-sparse input-output data. The first modality is an implementation of the direct computation of a composite length DFT, the second one employs the second-order recursive filtering method, and the third one performs the new pruned decomposed transform. The pruned decomposed transform algorithm performs the decimation in time or space (DIT) data acquisition domain and, then, decimation in frequency (DIF). The unified combination of these three algorithms is addressed as the DFTCOMM technique. Based on the treatment of the combinational-type hypotheses testing optimization problem of preferable allocations between all feasible commuting-pruning modalities, we have found the global optimal solution to the pruning problem that always requires a fewer or, at most, the same number of arithmetic operations than other feasible modalities. The DFTCOMM method outperforms the existing competing pruning techniques in the sense of attainable savings in the number of required arithmetic operations. It requires fewer or at most the same number of arithmetic operations for its execution than any other of the competing pruning methods reported in the literature. Finally, we provide the comparison of the DFTCOMM with the recently developed sparse fast Fourier transform (SFFT) algorithmic family. We feature that, in the sensing scenarios with sparse/non-sparse data Fourier spectrum, the DFTCOMM technique manifests robustness against such model uncertainties in the sense of insensitivity for sparsity/non-sparsity restrictions and the variability of the operating parameters.

Automatic registration of terrestrial point clouds based on panoramic reflectance images and efficient BaySAC

NASA Astrophysics Data System (ADS)

Kang, Zhizhong

2013-10-01

This paper presents a new approach to automatic registration of terrestrial laser scanning (TLS) point clouds utilizing a novel robust estimation method by an efficient BaySAC (BAYes SAmpling Consensus). The proposed method directly generates reflectance images from 3D point clouds, and then using SIFT algorithm extracts keypoints to identify corresponding image points. The 3D corresponding points, from which transformation parameters between point clouds are computed, are acquired by mapping the 2D ones onto the point cloud. To remove false accepted correspondences, we implement a conditional sampling method to select the n data points with the highest inlier probabilities as a hypothesis set and update the inlier probabilities of each data point using simplified Bayes' rule for the purpose of improving the computation efficiency. The prior probability is estimated by the verification of the distance invariance between correspondences. The proposed approach is tested on four data sets acquired by three different scanners. The results show that, comparing with the performance of RANSAC, BaySAC leads to less iterations and cheaper computation cost when the hypothesis set is contaminated with more outliers. The registration results also indicate that, the proposed algorithm can achieve high registration accuracy on all experimental datasets.

TU-EF-304-03: 4D Monte Carlo Robustness Test for Proton Therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Souris, K; Sterpin, E; Lee, J

Purpose: Breathing motion and approximate dose calculation engines may increase proton range uncertainties. We address these two issues using a comprehensive 4D robustness evaluation tool based on an efficient Monte Carlo (MC) engine, which can simulate breathing with no significant increase in computation time. Methods: To assess the robustness of the treatment plan, multiple scenarios of uncertainties are simulated, taking into account the systematic and random setup errors, range uncertainties, and organ motion. Our fast MC dose engine, called MCsquare, implements optimized models on a massively-parallel computation architecture and allows us to accurately simulate a scenario in less than onemore » minute. The deviations of the uncertainty scenarios are then reported on a DVH-band and compared to the nominal plan.The robustness evaluation tool is illustrated in a lung case by comparing three 60Gy treatment plans. First, a plan is optimized on a PTV obtained by extending the CTV with an 8mm margin, in order to take into account systematic geometrical uncertainties, like in our current practice in radiotherapy. No specific strategy is employed to correct for tumor and organ motions. The second plan involves a PTV generated from the ITV, which encompasses the tumor volume in all breathing phases. The last plan results from robust optimization performed on the ITV, with robustness parameters of 3% for tissue density and 8 mm for positioning errors. Results: The robustness test revealed that the first two plans could not properly cover the target in the presence of uncertainties. CTV-coverage (D95) in the three plans ranged respectively between 39.4–55.5Gy, 50.2–57.5Gy, and 55.1–58.6Gy. Conclusion: A realistic robustness verification tool based on a fast MC dose engine has been developed. This test is essential to assess the quality of proton therapy plan and very useful to study various planning strategies for mobile tumors. This work is partly funded by IBA (Louvain-la-Neuve, Belgium)« less

Improving membrane protein expression by optimizing integration efficiency

PubMed Central

2017-01-01

The heterologous overexpression of integral membrane proteins in Escherichia coli often yields insufficient quantities of purifiable protein for applications of interest. The current study leverages a recently demonstrated link between co-translational membrane integration efficiency and protein expression levels to predict protein sequence modifications that improve expression. Membrane integration efficiencies, obtained using a coarse-grained simulation approach, robustly predicted effects on expression of the integral membrane protein TatC for a set of 140 sequence modifications, including loop-swap chimeras and single-residue mutations distributed throughout the protein sequence. Mutations that improve simulated integration efficiency were 4-fold enriched with respect to improved experimentally observed expression levels. Furthermore, the effects of double mutations on both simulated integration efficiency and experimentally observed expression levels were cumulative and largely independent, suggesting that multiple mutations can be introduced to yield higher levels of purifiable protein. This work provides a foundation for a general method for the rational overexpression of integral membrane proteins based on computationally simulated membrane integration efficiencies. PMID:28918393

Resilient and Robust High Performance Computing Platforms for Scientific Computing Integrity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jin, Yier

As technology advances, computer systems are subject to increasingly sophisticated cyber-attacks that compromise both their security and integrity. High performance computing platforms used in commercial and scientific applications involving sensitive, or even classified data, are frequently targeted by powerful adversaries. This situation is made worse by a lack of fundamental security solutions that both perform efficiently and are effective at preventing threats. Current security solutions fail to address the threat landscape and ensure the integrity of sensitive data. As challenges rise, both private and public sectors will require robust technologies to protect its computing infrastructure. The research outcomes from thismore » project try to address all these challenges. For example, we present LAZARUS, a novel technique to harden kernel Address Space Layout Randomization (KASLR) against paging-based side-channel attacks. In particular, our scheme allows for fine-grained protection of the virtual memory mappings that implement the randomization. We demonstrate the effectiveness of our approach by hardening a recent Linux kernel with LAZARUS, mitigating all of the previously presented side-channel attacks on KASLR. Our extensive evaluation shows that LAZARUS incurs only 0.943% overhead for standard benchmarks, and is therefore highly practical. We also introduced HA2lloc, a hardware-assisted allocator that is capable of leveraging an extended memory management unit to detect memory errors in the heap. We also perform testing using HA2lloc in a simulation environment and find that the approach is capable of preventing common memory vulnerabilities.« less

Quantum Simulation of Helium Hydride Cation in a Solid-State Spin Register.

PubMed

Wang, Ya; Dolde, Florian; Biamonte, Jacob; Babbush, Ryan; Bergholm, Ville; Yang, Sen; Jakobi, Ingmar; Neumann, Philipp; Aspuru-Guzik, Alán; Whitfield, James D; Wrachtrup, Jörg

2015-08-25

Ab initio computation of molecular properties is one of the most promising applications of quantum computing. While this problem is widely believed to be intractable for classical computers, efficient quantum algorithms exist which have the potential to vastly accelerate research throughput in fields ranging from material science to drug discovery. Using a solid-state quantum register realized in a nitrogen-vacancy (NV) defect in diamond, we compute the bond dissociation curve of the minimal basis helium hydride cation, HeH(+). Moreover, we report an energy uncertainty (given our model basis) of the order of 10(-14) hartree, which is 10 orders of magnitude below the desired chemical precision. As NV centers in diamond provide a robust and straightforward platform for quantum information processing, our work provides an important step toward a fully scalable solid-state implementation of a quantum chemistry simulator.

MER-DIMES : a planetary landing application of computer vision

NASA Technical Reports Server (NTRS)

Cheng, Yang; Johnson, Andrew; Matthies, Larry

2005-01-01

During the Mars Exploration Rovers (MER) landings, the Descent Image Motion Estimation System (DIMES) was used for horizontal velocity estimation. The DIMES algorithm combines measurements from a descent camera, a radar altimeter and an inertial measurement unit. To deal with large changes in scale and orientation between descent images, the algorithm uses altitude and attitude measurements to rectify image data to level ground plane. Feature selection and tracking is employed in the rectified data to compute the horizontal motion between images. Differences of motion estimates are then compared to inertial measurements to verify correct feature tracking. DIMES combines sensor data from multiple sources in a novel way to create a low-cost, robust and computationally efficient velocity estimation solution, and DIMES is the first use of computer vision to control a spacecraft during planetary landing. In this paper, the detailed implementation of the DIMES algorithm and the results from the two landings on Mars are presented.

Efficient Robust Regression via Two-Stage Generalized Empirical Likelihood

PubMed Central

Bondell, Howard D.; Stefanski, Leonard A.

2013-01-01

Large- and finite-sample efficiency and resistance to outliers are the key goals of robust statistics. Although often not simultaneously attainable, we develop and study a linear regression estimator that comes close. Efficiency obtains from the estimator’s close connection to generalized empirical likelihood, and its favorable robustness properties are obtained by constraining the associated sum of (weighted) squared residuals. We prove maximum attainable finite-sample replacement breakdown point, and full asymptotic efficiency for normal errors. Simulation evidence shows that compared to existing robust regression estimators, the new estimator has relatively high efficiency for small sample sizes, and comparable outlier resistance. The estimator is further illustrated and compared to existing methods via application to a real data set with purported outliers. PMID:23976805

Robust numerical solution of the reservoir routing equation

NASA Astrophysics Data System (ADS)

Fiorentini, Marcello; Orlandini, Stefano

2013-09-01

The robustness of numerical methods for the solution of the reservoir routing equation is evaluated. The methods considered in this study are: (1) the Laurenson-Pilgrim method, (2) the fourth-order Runge-Kutta method, and (3) the fixed order Cash-Karp method. Method (1) is unable to handle nonmonotonic outflow rating curves. Method (2) is found to fail under critical conditions occurring, especially at the end of inflow recession limbs, when large time steps (greater than 12 min in this application) are used. Method (3) is computationally intensive and it does not solve the limitations of method (2). The limitations of method (2) can be efficiently overcome by reducing the time step in the critical phases of the simulation so as to ensure that water level remains inside the domains of the storage function and the outflow rating curve. The incorporation of a simple backstepping procedure implementing this control into the method (2) yields a robust and accurate reservoir routing method that can be safely used in distributed time-continuous catchment models.

Blind calibration of radio interferometric arrays using sparsity constraints and its implications for self-calibration

NASA Astrophysics Data System (ADS)

Chiarucci, Simone; Wijnholds, Stefan J.

2018-02-01

Blind calibration, i.e. calibration without a priori knowledge of the source model, is robust to the presence of unknown sources such as transient phenomena or (low-power) broad-band radio frequency interference that escaped detection. In this paper, we present a novel method for blind calibration of a radio interferometric array assuming that the observed field only contains a small number of discrete point sources. We show the huge computational advantage over previous blind calibration methods and we assess its statistical efficiency and robustness to noise and the quality of the initial estimate. We demonstrate the method on actual data from a Low-Frequency Array low-band antenna station showing that our blind calibration is able to recover the same gain solutions as the regular calibration approach, as expected from theory and simulations. We also discuss the implications of our findings for the robustness of regular self-calibration to poor starting models.

Fast and robust control of two interacting spins

NASA Astrophysics Data System (ADS)

Yu, Xiao-Tong; Zhang, Qi; Ban, Yue; Chen, Xi

2018-06-01

Rapid preparation, manipulation, and correction of spin states with high fidelity are requisite for quantum information processing and quantum computing. In this paper, we propose a fast and robust approach for controlling two spins with Heisenberg and Ising interactions. By using the concept of shortcuts to adiabaticity, we first inverse design the driving magnetic fields for achieving fast spin flip or generating the entangled Bell state, and further optimize them with respect to the error and fluctuation. In particular, the designed shortcut protocols can efficiently suppress the unwanted transition or control error induced by anisotropic antisymmetric Dzyaloshinskii-Moriya exchange. Several examples and comparisons are illustrated, showing the advantages of our methods. Finally, we emphasize that the results can be naturally extended to multiple interacting spins and other quantum systems in an analogous fashion.

Nonlinear Krylov and moving nodes in the method of lines

NASA Astrophysics Data System (ADS)

Miller, Keith

2005-11-01

We report on some successes and problem areas in the Method of Lines from our work with moving node finite element methods. First, we report on our "nonlinear Krylov accelerator" for the modified Newton's method on the nonlinear equations of our stiff ODE solver. Since 1990 it has been robust, simple, cheap, and automatic on all our moving node computations. We publicize further trials with it here because it should be of great general usefulness to all those solving evolutionary equations. Second, we discuss the need for reliable automatic choice of spatially variable time steps. Third, we discuss the need for robust and efficient iterative solvers for the difficult linearized equations (Jx=b) of our stiff ODE solver. Here, the 1997 thesis of Zulu Xaba has made significant progress.

Factorizable Upwind Schemes: The Triangular Unstructured Grid Formulation

NASA Technical Reports Server (NTRS)

Sidilkover, David; Nielsen, Eric J.

2001-01-01

The upwind factorizable schemes for the equations of fluid were introduced recently. They facilitate achieving the Textbook Multigrid Efficiency (TME) and are expected also to result in the solvers of unparalleled robustness. The approach itself is very general. Therefore, it may well become a general framework for the large-scale, Computational Fluid Dynamics. In this paper we outline the triangular grid formulation of the factorizable schemes. The derivation is based on the fact that the factorizable schemes can be expressed entirely using vector notation. without explicitly mentioning a particular coordinate frame. We, describe the resulting discrete scheme in detail and present some computational results verifying the basic properties of the scheme/solver.

Quantum computing gates via optimal control

NASA Astrophysics Data System (ADS)

Atia, Yosi; Elias, Yuval; Mor, Tal; Weinstein, Yossi

2014-10-01

We demonstrate the use of optimal control to design two entropy-manipulating quantum gates which are more complex than the corresponding, commonly used, gates, such as CNOT and Toffoli (CCNOT): A two-qubit gate called polarization exchange (PE) and a three-qubit gate called polarization compression (COMP) were designed using GRAPE, an optimal control algorithm. Both gates were designed for a three-spin system. Our design provided efficient and robust nuclear magnetic resonance (NMR) radio frequency (RF) pulses for 13C2-trichloroethylene (TCE), our chosen three-spin system. We then experimentally applied these two quantum gates onto TCE at the NMR lab. Such design of these gates and others could be relevant for near-future applications of quantum computing devices.

GPU-based real-time soft tissue deformation with cutting and haptic feedback.

PubMed

Courtecuisse, Hadrien; Jung, Hoeryong; Allard, Jérémie; Duriez, Christian; Lee, Doo Yong; Cotin, Stéphane

2010-12-01

This article describes a series of contributions in the field of real-time simulation of soft tissue biomechanics. These contributions address various requirements for interactive simulation of complex surgical procedures. In particular, this article presents results in the areas of soft tissue deformation, contact modelling, simulation of cutting, and haptic rendering, which are all relevant to a variety of medical interventions. The contributions described in this article share a common underlying model of deformation and rely on GPU implementations to significantly improve computation times. This consistency in the modelling technique and computational approach ensures coherent results as well as efficient, robust and flexible solutions. Copyright © 2010 Elsevier Ltd. All rights reserved.

Global Optimization of N-Maneuver, High-Thrust Trajectories Using Direct Multiple Shooting

NASA Technical Reports Server (NTRS)

Vavrina, Matthew A.; Englander, Jacob A.; Ellison, Donald H.

2016-01-01

The performance of impulsive, gravity-assist trajectories often improves with the inclusion of one or more maneuvers between flybys. However, grid-based scans over the entire design space can become computationally intractable for even one deep-space maneuver, and few global search routines are capable of an arbitrary number of maneuvers. To address this difficulty a trajectory transcription allowing for any number of maneuvers is developed within a multi-objective, global optimization framework for constrained, multiple gravity-assist trajectories. The formulation exploits a robust shooting scheme and analytic derivatives for computational efficiency. The approach is applied to several complex, interplanetary problems, achieving notable performance without a user-supplied initial guess.

A Parallel Nonrigid Registration Algorithm Based on B-Spline for Medical Images

PubMed Central

Wang, Yangping; Wang, Song

2016-01-01

The nonrigid registration algorithm based on B-spline Free-Form Deformation (FFD) plays a key role and is widely applied in medical image processing due to the good flexibility and robustness. However, it requires a tremendous amount of computing time to obtain more accurate registration results especially for a large amount of medical image data. To address the issue, a parallel nonrigid registration algorithm based on B-spline is proposed in this paper. First, the Logarithm Squared Difference (LSD) is considered as the similarity metric in the B-spline registration algorithm to improve registration precision. After that, we create a parallel computing strategy and lookup tables (LUTs) to reduce the complexity of the B-spline registration algorithm. As a result, the computing time of three time-consuming steps including B-splines interpolation, LSD computation, and the analytic gradient computation of LSD, is efficiently reduced, for the B-spline registration algorithm employs the Nonlinear Conjugate Gradient (NCG) optimization method. Experimental results of registration quality and execution efficiency on the large amount of medical images show that our algorithm achieves a better registration accuracy in terms of the differences between the best deformation fields and ground truth and a speedup of 17 times over the single-threaded CPU implementation due to the powerful parallel computing ability of Graphics Processing Unit (GPU). PMID:28053653

Generalizing the Nonlocal-Means to Super-Resolution Reconstruction

DTIC Science & Technology

2008-12-12

Image Process., vol. 5, no. 6, pp. 996–1011, Jun. 1996. [7] A. J. Patti, M. I. Sezan, and M. A. Tekalp, “ Superresolution video reconstruction with...computationally efficient image superresolution algorithm,” IEEE Trans. Image Process., vol. 10, no. 4, pp. 573–583, Apr. 2001. [13] M. Elad and Y...pp. 21–36, May 2003. [18] S. Farsiu, D. Robinson, M. Elad, and P. Milanfar, “Robust shift and add approach to superresolution ,” in Proc. SPIE Conf

Safe Maneuvering Envelope Estimation Based on a Physical Approach

NASA Technical Reports Server (NTRS)

Lombaerts, Thomas J. J.; Schuet, Stefan R.; Wheeler, Kevin R.; Acosta, Diana; Kaneshige, John T.

2013-01-01

This paper discusses a computationally efficient algorithm for estimating the safe maneuvering envelope of damaged aircraft. The algorithm performs a robust reachability analysis through an optimal control formulation while making use of time scale separation and taking into account uncertainties in the aerodynamic derivatives. This approach differs from others since it is physically inspired. This more transparent approach allows interpreting data in each step, and it is assumed that these physical models based upon flight dynamics theory will therefore facilitate certification for future real life applications.

Computational unsteady aerodynamics for lifting surfaces

NASA Technical Reports Server (NTRS)

Edwards, John W.

1988-01-01

Two dimensional problems are solved using numerical techniques. Navier-Stokes equations are studied both in the vorticity-stream function formulation which appears to be the optimal choice for two dimensional problems, using a storage approach, and in the velocity pressure formulation which minimizes the number of unknowns in three dimensional problems. Analysis shows that compact centered conservative second order schemes for the vorticity equation are the most robust for high Reynolds number flows. Serious difficulties remain in the choice of turbulent models, to keep reasonable CPU efficiency.

YAP Version 4.0

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nelson, Eric M.

2004-05-20

The YAP software library computes (1) electromagnetic modes, (2) electrostatic fields, (3) magnetostatic fields and (4) particle trajectories in 2d and 3d models. The code employs finite element methods on unstructured grids of tetrahedral, hexahedral, prism and pyramid elements, with linear through cubic element shapes and basis functions to provide high accuracy. The novel particle tracker is robust, accurate and efficient, even on unstructured grids with discontinuous fields. This software library is a component of the MICHELLE 3d finite element gun code.

Modal control theory and application to aircraft lateral handling qualities design

NASA Technical Reports Server (NTRS)

Srinathkumar, S.

1978-01-01

A multivariable synthesis procedure based on eigenvalue/eigenvector assignment is reviewed and is employed to develop a systematic design procedure to meet the lateral handling qualities design objectives of a fighter aircraft over a wide range of flight conditions. The closed loop modal characterization developed provides significant insight into the design process and plays a pivotal role in the synthesis of robust feedback systems. The simplicity of the synthesis algorithm yields an efficient computer aided interactive design tool for flight control system synthesis.

Magnetohydrodynamic Simulations of Black Hole Accretion Flows Using PATCHWORK, a Multi-Patch, multi-code approach

NASA Astrophysics Data System (ADS)

Avara, Mark J.; Noble, Scott; Shiokawa, Hotaka; Cheng, Roseanne; Campanelli, Manuela; Krolik, Julian H.

2017-08-01

A multi-patch approach to numerical simulations of black hole accretion flows allows one to robustly match numerical grid shape and equations solved to the natural structure of the physical system. For instance, a cartesian gridded patch can be used to cover coordinate singularities on a spherical-polar grid, increasing computational efficiency and better capturing the physical system through natural symmetries. We will present early tests, initial applications, and first results from the new MHD implementation of the PATCHWORK framework.

Homo Heuristicus: Less-is-More Effects in Adaptive Cognition

PubMed Central

Brighton, Henry; Gigerenzer, Gerd

2012-01-01

Heuristics are efficient cognitive processes that ignore information. In contrast to the widely held view that less processing reduces accuracy, the study of heuristics shows that less information, computation, and time can in fact improve accuracy. We discuss some of the major progress made so far, focusing on the discovery of less-is-more effects and the study of the ecological rationality of heuristics which examines in which environments a given strategy succeeds or fails, and why. Homo heuristicus has a biased mind and ignores part of the available information, yet a biased mind can handle uncertainty more efficiently and robustly than an unbiased mind relying on more resource-intensive and general-purpose processing strategies. PMID:23613644

Efficient implementations of a pseudodynamical stochastic filtering strategy for static elastography.

PubMed

Banerjee, Biswanath; Roy, Debasish; Vasu, Ram Mohan

2009-08-01

A computationally efficient pseudodynamical filtering setup is established for elasticity imaging (i.e., reconstruction of shear modulus distribution) in soft-tissue organs given statically recorded and partially measured displacement data. Unlike a regularized quasi-Newton method (QNM) that needs inversion of ill-conditioned matrices, the authors explore pseudodynamic extended and ensemble Kalman filters (PD-EKF and PD-EnKF) that use a parsimonious representation of states and bypass explicit regularization by recursion over pseudotime. Numerical experiments with QNM and the two filters suggest that the PD-EnKF is the most robust performer as it exhibits no sensitivity to process noise covariance and yields good reconstruction even with small ensemble sizes.

Design and construction of a cost-efficient Arduino-based mirror galvanometer system for scanning optical microscopy

NASA Astrophysics Data System (ADS)

Hsu, Jen-Feng; Dhingra, Shonali; D'Urso, Brian

2017-01-01

Mirror galvanometer systems (galvos) are commonly employed in research and commercial applications in areas involving laser imaging, laser machining, laser-light shows, and others. Here, we present a robust, moderate-speed, and cost-efficient home-built galvo system. The mechanical part of this design consists of one mirror, which is tilted around two axes with multiple surface transducers. We demonstrate the ability of this galvo by scanning the mirror using a computer, via a custom driver circuit. The performance of the galvo, including scan range, noise, linearity, and scan speed, is characterized. As an application, we show that this galvo system can be used in a confocal scanning microscopy system.

Efficient generalized cross-validation with applications to parametric image restoration and resolution enhancement.

PubMed

Nguyen, N; Milanfar, P; Golub, G

2001-01-01

In many image restoration/resolution enhancement applications, the blurring process, i.e., point spread function (PSF) of the imaging system, is not known or is known only to within a set of parameters. We estimate these PSF parameters for this ill-posed class of inverse problem from raw data, along with the regularization parameters required to stabilize the solution, using the generalized cross-validation method (GCV). We propose efficient approximation techniques based on the Lanczos algorithm and Gauss quadrature theory, reducing the computational complexity of the GCV. Data-driven PSF and regularization parameter estimation experiments with synthetic and real image sequences are presented to demonstrate the effectiveness and robustness of our method.

Event-Based Computation of Motion Flow on a Neuromorphic Analog Neural Platform

PubMed Central

Giulioni, Massimiliano; Lagorce, Xavier; Galluppi, Francesco; Benosman, Ryad B.

2016-01-01

Estimating the speed and direction of moving objects is a crucial component of agents behaving in a dynamic world. Biological organisms perform this task by means of the neural connections originating from their retinal ganglion cells. In artificial systems the optic flow is usually extracted by comparing activity of two or more frames captured with a vision sensor. Designing artificial motion flow detectors which are as fast, robust, and efficient as the ones found in biological systems is however a challenging task. Inspired by the architecture proposed by Barlow and Levick in 1965 to explain the spiking activity of the direction-selective ganglion cells in the rabbit's retina, we introduce an architecture for robust optical flow extraction with an analog neuromorphic multi-chip system. The task is performed by a feed-forward network of analog integrate-and-fire neurons whose inputs are provided by contrast-sensitive photoreceptors. Computation is supported by the precise time of spike emission, and the extraction of the optical flow is based on time lag in the activation of nearby retinal neurons. Mimicking ganglion cells our neuromorphic detectors encode the amplitude and the direction of the apparent visual motion in their output spiking pattern. Hereby we describe the architectural aspects, discuss its latency, scalability, and robustness properties and demonstrate that a network of mismatched delicate analog elements can reliably extract the optical flow from a simple visual scene. This work shows how precise time of spike emission used as a computational basis, biological inspiration, and neuromorphic systems can be used together for solving specific tasks. PMID:26909015

Event-Based Computation of Motion Flow on a Neuromorphic Analog Neural Platform.

PubMed

Giulioni, Massimiliano; Lagorce, Xavier; Galluppi, Francesco; Benosman, Ryad B

2016-01-01

Estimating the speed and direction of moving objects is a crucial component of agents behaving in a dynamic world. Biological organisms perform this task by means of the neural connections originating from their retinal ganglion cells. In artificial systems the optic flow is usually extracted by comparing activity of two or more frames captured with a vision sensor. Designing artificial motion flow detectors which are as fast, robust, and efficient as the ones found in biological systems is however a challenging task. Inspired by the architecture proposed by Barlow and Levick in 1965 to explain the spiking activity of the direction-selective ganglion cells in the rabbit's retina, we introduce an architecture for robust optical flow extraction with an analog neuromorphic multi-chip system. The task is performed by a feed-forward network of analog integrate-and-fire neurons whose inputs are provided by contrast-sensitive photoreceptors. Computation is supported by the precise time of spike emission, and the extraction of the optical flow is based on time lag in the activation of nearby retinal neurons. Mimicking ganglion cells our neuromorphic detectors encode the amplitude and the direction of the apparent visual motion in their output spiking pattern. Hereby we describe the architectural aspects, discuss its latency, scalability, and robustness properties and demonstrate that a network of mismatched delicate analog elements can reliably extract the optical flow from a simple visual scene. This work shows how precise time of spike emission used as a computational basis, biological inspiration, and neuromorphic systems can be used together for solving specific tasks.

A fast and robust computational method for the ionization cross sections of the driven Schrödinger equation using an O (N) multigrid-based scheme

NASA Astrophysics Data System (ADS)

Cools, S.; Vanroose, W.

2016-03-01

This paper improves the convergence and robustness of a multigrid-based solver for the cross sections of the driven Schrödinger equation. Adding a Coupled Channel Correction Step (CCCS) after each multigrid (MG) V-cycle efficiently removes the errors that remain after the V-cycle sweep. The combined iterative solution scheme (MG-CCCS) is shown to feature significantly improved convergence rates over the classical MG method at energies where bound states dominate the solution, resulting in a fast and scalable solution method for the complex-valued Schrödinger break-up problem for any energy regime. The proposed solver displays optimal scaling; a solution is found in a time that is linear in the number of unknowns. The method is validated on a 2D Temkin-Poet model problem, and convergence results both as a solver and preconditioner are provided to support the O (N) scalability of the method. This paper extends the applicability of the complex contour approach for far field map computation (Cools et al. (2014) [10]).

3D Boolean operations in virtual surgical planning.

PubMed

Charton, Jerome; Laurentjoye, Mathieu; Kim, Youngjun

2017-10-01

Boolean operations in computer-aided design or computer graphics are a set of operations (e.g. intersection, union, subtraction) between two objects (e.g. a patient model and an implant model) that are important in performing accurate and reproducible virtual surgical planning. This requires accurate and robust techniques that can handle various types of data, such as a surface extracted from volumetric data, synthetic models, and 3D scan data. This article compares the performance of the proposed method (Boolean operations by a robust, exact, and simple method between two colliding shells (BORES)) and an existing method based on the Visualization Toolkit (VTK). In all tests presented in this article, BORES could handle complex configurations as well as report impossible configurations of the input. In contrast, the VTK implementations were unstable, do not deal with singular edges and coplanar collisions, and have created several defects. The proposed method of Boolean operations, BORES, is efficient and appropriate for virtual surgical planning. Moreover, it is simple and easy to implement. In future work, we will extend the proposed method to handle non-colliding components.

Object tracking on mobile devices using binary descriptors

NASA Astrophysics Data System (ADS)

Savakis, Andreas; Quraishi, Mohammad Faiz; Minnehan, Breton

2015-03-01

With the growing ubiquity of mobile devices, advanced applications are relying on computer vision techniques to provide novel experiences for users. Currently, few tracking approaches take into consideration the resource constraints on mobile devices. Designing efficient tracking algorithms and optimizing performance for mobile devices can result in better and more efficient tracking for applications, such as augmented reality. In this paper, we use binary descriptors, including Fast Retina Keypoint (FREAK), Oriented FAST and Rotated BRIEF (ORB), Binary Robust Independent Features (BRIEF), and Binary Robust Invariant Scalable Keypoints (BRISK) to obtain real time tracking performance on mobile devices. We consider both Google's Android and Apple's iOS operating systems to implement our tracking approach. The Android implementation is done using Android's Native Development Kit (NDK), which gives the performance benefits of using native code as well as access to legacy libraries. The iOS implementation was created using both the native Objective-C and the C++ programing languages. We also introduce simplified versions of the BRIEF and BRISK descriptors that improve processing speed without compromising tracking accuracy.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yu, Kuang; Libisch, Florian; Carter, Emily A., E-mail: eac@princeton.edu

We report a new implementation of the density functional embedding theory (DFET) in the VASP code, using the projector-augmented-wave (PAW) formalism. Newly developed algorithms allow us to efficiently perform optimized effective potential optimizations within PAW. The new algorithm generates robust and physically correct embedding potentials, as we verified using several test systems including a covalently bound molecule, a metal surface, and bulk semiconductors. We show that with the resulting embedding potential, embedded cluster models can reproduce the electronic structure of point defects in bulk semiconductors, thereby demonstrating the validity of DFET in semiconductors for the first time. Compared to ourmore » previous version, the new implementation of DFET within VASP affords use of all features of VASP (e.g., a systematic PAW library, a wide selection of functionals, a more flexible choice of U correction formalisms, and faster computational speed) with DFET. Furthermore, our results are fairly robust with respect to both plane-wave and Gaussian type orbital basis sets in the embedded cluster calculations. This suggests that the density functional embedding method is potentially an accurate and efficient way to study properties of isolated defects in semiconductors.« less

Communication target object recognition for D2D connection with feature size limit

NASA Astrophysics Data System (ADS)

Ok, Jiheon; Kim, Soochang; Kim, Young-hoon; Lee, Chulhee

2015-03-01

Recently, a new concept of device-to-device (D2D) communication, which is called "point-and-link communication" has attracted great attentions due to its intuitive and simple operation. This approach enables user to communicate with target devices without any pre-identification information such as SSIDs, MAC addresses by selecting the target image displayed on the user's own device. In this paper, we present an efficient object matching algorithm that can be applied to look(point)-and-link communications for mobile services. Due to the limited channel bandwidth and low computational power of mobile terminals, the matching algorithm should satisfy low-complexity, low-memory and realtime requirements. To meet these requirements, we propose fast and robust feature extraction by considering the descriptor size and processing time. The proposed algorithm utilizes a HSV color histogram, SIFT (Scale Invariant Feature Transform) features and object aspect ratios. To reduce the descriptor size under 300 bytes, a limited number of SIFT key points were chosen as feature points and histograms were binarized while maintaining required performance. Experimental results show the robustness and the efficiency of the proposed algorithm.

An efficient framework for optimization and parameter sensitivity analysis in arterial growth and remodeling computations

PubMed Central

Sankaran, Sethuraman; Humphrey, Jay D.; Marsden, Alison L.

2013-01-01

Computational models for vascular growth and remodeling (G&R) are used to predict the long-term response of vessels to changes in pressure, flow, and other mechanical loading conditions. Accurate predictions of these responses are essential for understanding numerous disease processes. Such models require reliable inputs of numerous parameters, including material properties and growth rates, which are often experimentally derived, and inherently uncertain. While earlier methods have used a brute force approach, systematic uncertainty quantification in G&R models promises to provide much better information. In this work, we introduce an efficient framework for uncertainty quantification and optimal parameter selection, and illustrate it via several examples. First, an adaptive sparse grid stochastic collocation scheme is implemented in an established G&R solver to quantify parameter sensitivities, and near-linear scaling with the number of parameters is demonstrated. This non-intrusive and parallelizable algorithm is compared with standard sampling algorithms such as Monte-Carlo. Second, we determine optimal arterial wall material properties by applying robust optimization. We couple the G&R simulator with an adaptive sparse grid collocation approach and a derivative-free optimization algorithm. We show that an artery can achieve optimal homeostatic conditions over a range of alterations in pressure and flow; robustness of the solution is enforced by including uncertainty in loading conditions in the objective function. We then show that homeostatic intramural and wall shear stress is maintained for a wide range of material properties, though the time it takes to achieve this state varies. We also show that the intramural stress is robust and lies within 5% of its mean value for realistic variability of the material parameters. We observe that prestretch of elastin and collagen are most critical to maintaining homeostasis, while values of the material properties are most critical in determining response time. Finally, we outline several challenges to the G&R community for future work. We suggest that these tools provide the first systematic and efficient framework to quantify uncertainties and optimally identify G&R model parameters. PMID:23626380

Assessment of electron propagator methods for the simulation of vibrationally-resolved valence and core photoionization spectra

PubMed Central

Baiardi, A.; Paoloni, L.; Barone, V.; Zakrzewski, V.G.; Ortiz, J.V.

2017-01-01

The analysis of photoelectron spectra is usually facilitated by quantum mechanical simulations. Due to the recent improvement of experimental techniques, the resolution of experimental spectra is rapidly increasing, and the inclusion of vibrational effects is usually mandatory to obtain a reliable reproduction of the spectra. With the aim of defining a robust computational protocol, a general time-independent formulation to compute different kinds of vibrationally-resolved electronic spectra has been generalized to support also photoelectron spectroscopy. The electronic structure data underlying the simulation are computed using different electron propagator approaches. In addition to the more standard approaches, a new and robust implementation of the second-order self-energy approximation of the electron propagator based on a transition operator reference (TOEP2) is presented. To validate our implementation, a series of molecules has been used as test cases. The result of the simulations shows that, for ultraviolet photoionization spectra, the more accurate non-diagonal approaches are needed to obtain a reliable reproduction of vertical ionization energies, but diagonal approaches are sufficient for energy gradients and pole strengths. For X-ray photoelectron spectroscopy, the TOEP2 approach, besides being more efficient, is also the most accurate in the reproduction of both vertical ionization energies and vibrationally-resolved bandshapes. PMID:28521087

Computational modeling of chemo-electro-mechanical coupling: A novel implicit monolithic finite element approach

PubMed Central

Wong, J.; Göktepe, S.; Kuhl, E.

2014-01-01

Summary Computational modeling of the human heart allows us to predict how chemical, electrical, and mechanical fields interact throughout a cardiac cycle. Pharmacological treatment of cardiac disease has advanced significantly over the past decades, yet it remains unclear how the local biochemistry of an individual heart cell translates into global cardiac function. Here we propose a novel, unified strategy to simulate excitable biological systems across three biological scales. To discretize the governing chemical, electrical, and mechanical equations in space, we propose a monolithic finite element scheme. We apply a highly efficient and inherently modular global-local split, in which the deformation and the transmembrane potential are introduced globally as nodal degrees of freedom, while the chemical state variables are treated locally as internal variables. To ensure unconditional algorithmic stability, we apply an implicit backward Euler finite difference scheme to discretize the resulting system in time. To increase algorithmic robustness and guarantee optimal quadratic convergence, we suggest an incremental iterative Newton-Raphson scheme. The proposed algorithm allows us to simulate the interaction of chemical, electrical, and mechanical fields during a representative cardiac cycle on a patient-specific geometry, robust and stable, with calculation times on the order of four days on a standard desktop computer. PMID:23798328

Efficient analytical implementation of the DOT Riemann solver for the de Saint Venant-Exner morphodynamic model

NASA Astrophysics Data System (ADS)

Carraro, F.; Valiani, A.; Caleffi, V.

2018-03-01

Within the framework of the de Saint Venant equations coupled with the Exner equation for morphodynamic evolution, this work presents a new efficient implementation of the Dumbser-Osher-Toro (DOT) scheme for non-conservative problems. The DOT path-conservative scheme is a robust upwind method based on a complete Riemann solver, but it has the drawback of requiring expensive numerical computations. Indeed, to compute the non-linear time evolution in each time step, the DOT scheme requires numerical computation of the flux matrix eigenstructure (the totality of eigenvalues and eigenvectors) several times at each cell edge. In this work, an analytical and compact formulation of the eigenstructure for the de Saint Venant-Exner (dSVE) model is introduced and tested in terms of numerical efficiency and stability. Using the original DOT and PRICE-C (a very efficient FORCE-type method) as reference methods, we present a convergence analysis (error against CPU time) to study the performance of the DOT method with our new analytical implementation of eigenstructure calculations (A-DOT). In particular, the numerical performance of the three methods is tested in three test cases: a movable bed Riemann problem with analytical solution; a problem with smooth analytical solution; a test in which the water flow is characterised by subcritical and supercritical regions. For a given target error, the A-DOT method is always the most efficient choice. Finally, two experimental data sets and different transport formulae are considered to test the A-DOT model in more practical case studies.

Robust Flutter Margin Analysis that Incorporates Flight Data

NASA Technical Reports Server (NTRS)

Lind, Rick; Brenner, Martin J.

1998-01-01

An approach for computing worst-case flutter margins has been formulated in a robust stability framework. Uncertainty operators are included with a linear model to describe modeling errors and flight variations. The structured singular value, mu, computes a stability margin that directly accounts for these uncertainties. This approach introduces a new method of computing flutter margins and an associated new parameter for describing these margins. The mu margins are robust margins that indicate worst-case stability estimates with respect to the defined uncertainty. Worst-case flutter margins are computed for the F/A-18 Systems Research Aircraft using uncertainty sets generated by flight data analysis. The robust margins demonstrate flight conditions for flutter may lie closer to the flight envelope than previously estimated by p-k analysis.

Computation of incompressible viscous flows through artificial heart devices with moving boundaries

NASA Technical Reports Server (NTRS)

Kiris, Cetin; Rogers, Stuart; Kwak, Dochan; Chang, I.-DEE

1991-01-01

The extension of computational fluid dynamics techniques to artificial heart flow simulations is illustrated. Unsteady incompressible Navier-Stokes equations written in 3-D generalized curvilinear coordinates are solved iteratively at each physical time step until the incompressibility condition is satisfied. The solution method is based on the pseudo compressibility approach and uses an implicit upwind differencing scheme together with the Gauss-Seidel line relaxation method. The efficiency and robustness of the time accurate formulation of the algorithm are tested by computing the flow through model geometries. A channel flow with a moving indentation is computed and validated with experimental measurements and other numerical solutions. In order to handle the geometric complexity and the moving boundary problems, a zonal method and an overlapping grid embedding scheme are used, respectively. Steady state solutions for the flow through a tilting disk heart valve was compared against experimental measurements. Good agreement was obtained. The flow computation during the valve opening and closing is carried out to illustrate the moving boundary capability.

Common spatial pattern combined with kernel linear discriminate and generalized radial basis function for motor imagery-based brain computer interface applications

NASA Astrophysics Data System (ADS)

Hekmatmanesh, Amin; Jamaloo, Fatemeh; Wu, Huapeng; Handroos, Heikki; Kilpeläinen, Asko

2018-04-01

Brain Computer Interface (BCI) can be a challenge for developing of robotic, prosthesis and human-controlled systems. This work focuses on the implementation of a common spatial pattern (CSP) base algorithm to detect event related desynchronization patterns. Utilizing famous previous work in this area, features are extracted by filter bank with common spatial pattern (FBCSP) method, and then weighted by a sensitive learning vector quantization (SLVQ) algorithm. In the current work, application of the radial basis function (RBF) as a mapping kernel of linear discriminant analysis (KLDA) method on the weighted features, allows the transfer of data into a higher dimension for more discriminated data scattering by RBF kernel. Afterwards, support vector machine (SVM) with generalized radial basis function (GRBF) kernel is employed to improve the efficiency and robustness of the classification. Averagely, 89.60% accuracy and 74.19% robustness are achieved. BCI Competition III, Iva data set is used to evaluate the algorithm for detecting right hand and foot imagery movement patterns. Results show that combination of KLDA with SVM-GRBF classifier makes 8.9% and 14.19% improvements in accuracy and robustness, respectively. For all the subjects, it is concluded that mapping the CSP features into a higher dimension by RBF and utilization GRBF as a kernel of SVM, improve the accuracy and reliability of the proposed method.

Recent applications of the transonic wing analysis computer code, TWING

NASA Technical Reports Server (NTRS)

Subramanian, N. R.; Holst, T. L.; Thomas, S. D.

1982-01-01

An evaluation of the transonic-wing-analysis computer code TWING is given. TWING utilizes a fully implicit approximate factorization iteration scheme to solve the full potential equation in conservative form. A numerical elliptic-solver grid-generation scheme is used to generate the required finite-difference mesh. Several wing configurations were analyzed, and the limits of applicability of this code was evaluated. Comparisons of computed results were made with available experimental data. Results indicate that the code is robust, accurate (when significant viscous effects are not present), and efficient. TWING generally produces solutions an order of magnitude faster than other conservative full potential codes using successive-line overrelaxation. The present method is applicable to a wide range of isolated wing configurations including high-aspect-ratio transport wings and low-aspect-ratio, high-sweep, fighter configurations.

Exploring Valleys without Climbing Every Peak: More Efficient and Forgiving Metabasin Metadynamics via Robust On-the-Fly Bias Domain Restriction

PubMed Central

2015-01-01

Metadynamics is an enhanced sampling method designed to flatten free energy surfaces uniformly. However, the highest-energy regions are often irrelevant to study and dangerous to explore because systems often change irreversibly in unforeseen ways in response to driving forces in these regions, spoiling the sampling. Introducing an on-the-fly domain restriction allows metadynamics to flatten only up to a specified energy level and no further, improving efficiency and safety while decreasing the pressure on practitioners to design collective variables that are robust to otherwise irrelevant high energy driving. This paper describes a new method that achieves this using sequential on-the-fly estimation of energy wells and redefinition of the metadynamics hill shape, termed metabasin metadynamics. The energy level may be defined a priori or relative to unknown barrier energies estimated on-the-fly. Altering only the hill ensures that the method is compatible with many other advances in metadynamics methodology. The hill shape has a natural interpretation in terms of multiscale dynamics, and the computational overhead in simulation is minimal when studying systems of any reasonable size, for instance proteins or other macromolecules. Three example applications show that the formula is accurate and robust to complex dynamics, making metadynamics significantly more forgiving with respect to CV quality and thus more feasible to apply to the most challenging biomolecular systems. PMID:26587809

An optimization program based on the method of feasible directions: Theory and users guide

NASA Technical Reports Server (NTRS)

Belegundu, Ashok D.; Berke, Laszlo; Patnaik, Surya N.

1994-01-01

The theory and user instructions for an optimization code based on the method of feasible directions are presented. The code was written for wide distribution and ease of attachment to other simulation software. Although the theory of the method of feasible direction was developed in the 1960's, many considerations are involved in its actual implementation as a computer code. Included in the code are a number of features to improve robustness in optimization. The search direction is obtained by solving a quadratic program using an interior method based on Karmarkar's algorithm. The theory is discussed focusing on the important and often overlooked role played by the various parameters guiding the iterations within the program. Also discussed is a robust approach for handling infeasible starting points. The code was validated by solving a variety of structural optimization test problems that have known solutions obtained by other optimization codes. It has been observed that this code is robust: it has solved a variety of problems from different starting points. However, the code is inefficient in that it takes considerable CPU time as compared with certain other available codes. Further work is required to improve its efficiency while retaining its robustness.

Effective real-time vehicle tracking using discriminative sparse coding on local patches

NASA Astrophysics Data System (ADS)

Chen, XiangJun; Ye, Feiyue; Ruan, Yaduan; Chen, Qimei

2016-01-01

A visual tracking framework that provides an object detector and tracker, which focuses on effective and efficient visual tracking in surveillance of real-world intelligent transport system applications, is proposed. The framework casts the tracking task as problems of object detection, feature representation, and classification, which is different from appearance model-matching approaches. Through a feature representation of discriminative sparse coding on local patches called DSCLP, which trains a dictionary on local clustered patches sampled from both positive and negative datasets, the discriminative power and robustness has been improved remarkably, which makes our method more robust to a complex realistic setting with all kinds of degraded image quality. Moreover, by catching objects through one-time background subtraction, along with offline dictionary training, computation time is dramatically reduced, which enables our framework to achieve real-time tracking performance even in a high-definition sequence with heavy traffic. Experiment results show that our work outperforms some state-of-the-art methods in terms of speed, accuracy, and robustness and exhibits increased robustness in a complex real-world scenario with degraded image quality caused by vehicle occlusion, image blur of rain or fog, and change in viewpoint or scale.

Contextuality supplies the 'magic' for quantum computation.

PubMed

Howard, Mark; Wallman, Joel; Veitch, Victor; Emerson, Joseph

2014-06-19

Quantum computers promise dramatic advantages over their classical counterparts, but the source of the power in quantum computing has remained elusive. Here we prove a remarkable equivalence between the onset of contextuality and the possibility of universal quantum computation via 'magic state' distillation, which is the leading model for experimentally realizing a fault-tolerant quantum computer. This is a conceptually satisfying link, because contextuality, which precludes a simple 'hidden variable' model of quantum mechanics, provides one of the fundamental characterizations of uniquely quantum phenomena. Furthermore, this connection suggests a unifying paradigm for the resources of quantum information: the non-locality of quantum theory is a particular kind of contextuality, and non-locality is already known to be a critical resource for achieving advantages with quantum communication. In addition to clarifying these fundamental issues, this work advances the resource framework for quantum computation, which has a number of practical applications, such as characterizing the efficiency and trade-offs between distinct theoretical and experimental schemes for achieving robust quantum computation, and putting bounds on the overhead cost for the classical simulation of quantum algorithms.

Quantifying aggregated uncertainty in Plasmodium falciparum malaria prevalence and populations at risk via efficient space-time geostatistical joint simulation.

PubMed

Gething, Peter W; Patil, Anand P; Hay, Simon I

2010-04-01

Risk maps estimating the spatial distribution of infectious diseases are required to guide public health policy from local to global scales. The advent of model-based geostatistics (MBG) has allowed these maps to be generated in a formal statistical framework, providing robust metrics of map uncertainty that enhances their utility for decision-makers. In many settings, decision-makers require spatially aggregated measures over large regions such as the mean prevalence within a country or administrative region, or national populations living under different levels of risk. Existing MBG mapping approaches provide suitable metrics of local uncertainty--the fidelity of predictions at each mapped pixel--but have not been adapted for measuring uncertainty over large areas, due largely to a series of fundamental computational constraints. Here the authors present a new efficient approximating algorithm that can generate for the first time the necessary joint simulation of prevalence values across the very large prediction spaces needed for global scale mapping. This new approach is implemented in conjunction with an established model for P. falciparum allowing robust estimates of mean prevalence at any specified level of spatial aggregation. The model is used to provide estimates of national populations at risk under three policy-relevant prevalence thresholds, along with accompanying model-based measures of uncertainty. By overcoming previously unchallenged computational barriers, this study illustrates how MBG approaches, already at the forefront of infectious disease mapping, can be extended to provide large-scale aggregate measures appropriate for decision-makers.

Time-variant random interval natural frequency analysis of structures

NASA Astrophysics Data System (ADS)

Wu, Binhua; Wu, Di; Gao, Wei; Song, Chongmin

2018-02-01

This paper presents a new robust method namely, unified interval Chebyshev-based random perturbation method, to tackle hybrid random interval structural natural frequency problem. In the proposed approach, random perturbation method is implemented to furnish the statistical features (i.e., mean and standard deviation) and Chebyshev surrogate model strategy is incorporated to formulate the statistical information of natural frequency with regards to the interval inputs. The comprehensive analysis framework combines the superiority of both methods in a way that computational cost is dramatically reduced. This presented method is thus capable of investigating the day-to-day based time-variant natural frequency of structures accurately and efficiently under concrete intrinsic creep effect with probabilistic and interval uncertain variables. The extreme bounds of the mean and standard deviation of natural frequency are captured through the embedded optimization strategy within the analysis procedure. Three particularly motivated numerical examples with progressive relationship in perspective of both structure type and uncertainty variables are demonstrated to justify the computational applicability, accuracy and efficiency of the proposed method.

Transition probabilities for general birth-death processes with applications in ecology, genetics, and evolution

PubMed Central

Crawford, Forrest W.; Suchard, Marc A.

2011-01-01

A birth-death process is a continuous-time Markov chain that counts the number of particles in a system over time. In the general process with n current particles, a new particle is born with instantaneous rate λn and a particle dies with instantaneous rate μn. Currently no robust and efficient method exists to evaluate the finite-time transition probabilities in a general birth-death process with arbitrary birth and death rates. In this paper, we first revisit the theory of continued fractions to obtain expressions for the Laplace transforms of these transition probabilities and make explicit an important derivation connecting transition probabilities and continued fractions. We then develop an efficient algorithm for computing these probabilities that analyzes the error associated with approximations in the method. We demonstrate that this error-controlled method agrees with known solutions and outperforms previous approaches to computing these probabilities. Finally, we apply our novel method to several important problems in ecology, evolution, and genetics. PMID:21984359

DOE Office of Scientific and Technical Information (OSTI.GOV)

Suryanarayana, Phanish, E-mail: phanish.suryanarayana@ce.gatech.edu; Phanish, Deepa

We present an Augmented Lagrangian formulation and its real-space implementation for non-periodic Orbital-Free Density Functional Theory (OF-DFT) calculations. In particular, we rewrite the constrained minimization problem of OF-DFT as a sequence of minimization problems without any constraint, thereby making it amenable to powerful unconstrained optimization algorithms. Further, we develop a parallel implementation of this approach for the Thomas–Fermi–von Weizsacker (TFW) kinetic energy functional in the framework of higher-order finite-differences and the conjugate gradient method. With this implementation, we establish that the Augmented Lagrangian approach is highly competitive compared to the penalty and Lagrange multiplier methods. Additionally, we show that higher-ordermore » finite-differences represent a computationally efficient discretization for performing OF-DFT simulations. Overall, we demonstrate that the proposed formulation and implementation are both efficient and robust by studying selected examples, including systems consisting of thousands of atoms. We validate the accuracy of the computed energies and forces by comparing them with those obtained by existing plane-wave methods.« less

Comparison of three newton-like nonlinear least-squares methods for estimating parameters of ground-water flow models

USGS Publications Warehouse

Cooley, R.L.; Hill, M.C.

1992-01-01

Three methods of solving nonlinear least-squares problems were compared for robustness and efficiency using a series of hypothetical and field problems. A modified Gauss-Newton/full Newton hybrid method (MGN/FN) and an analogous method for which part of the Hessian matrix was replaced by a quasi-Newton approximation (MGN/QN) solved some of the problems with appreciably fewer iterations than required using only a modified Gauss-Newton (MGN) method. In these problems, model nonlinearity and a large variance for the observed data apparently caused MGN to converge more slowly than MGN/FN or MGN/QN after the sum of squared errors had almost stabilized. Other problems were solved as efficiently with MGN as with MGN/FN or MGN/QN. Because MGN/FN can require significantly more computer time per iteration and more computer storage for transient problems, it is less attractive for a general purpose algorithm than MGN/QN.

Using hybrid implicit Monte Carlo diffusion to simulate gray radiation hydrodynamics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cleveland, Mathew A., E-mail: cleveland7@llnl.gov; Gentile, Nick

This work describes how to couple a hybrid Implicit Monte Carlo Diffusion (HIMCD) method with a Lagrangian hydrodynamics code to evaluate the coupled radiation hydrodynamics equations. This HIMCD method dynamically applies Implicit Monte Carlo Diffusion (IMD) [1] to regions of a problem that are opaque and diffusive while applying standard Implicit Monte Carlo (IMC) [2] to regions where the diffusion approximation is invalid. We show that this method significantly improves the computational efficiency as compared to a standard IMC/Hydrodynamics solver, when optically thick diffusive material is present, while maintaining accuracy. Two test cases are used to demonstrate the accuracy andmore » performance of HIMCD as compared to IMC and IMD. The first is the Lowrie semi-analytic diffusive shock [3]. The second is a simple test case where the source radiation streams through optically thin material and heats a thick diffusive region of material causing it to rapidly expand. We found that HIMCD proves to be accurate, robust, and computationally efficient for these test problems.« less

Robust and High Order Computational Method for Parachute and Air Delivery and MAV System

DTIC Science & Technology

2017-11-01

Report: Robust and High Order Computational Method for Parachute and Air Delivery and MAV System The views, opinions and/or findings contained in this...University Title: Robust and High Order Computational Method for Parachute and Air Delivery and MAV System Report Term: 0-Other Email: xiaolin.li...model coupled with an incompressible fluid solver through the impulse method . Our approach to simulating the parachute system is based on the front

A robust upscaling of the effective particle deposition rate in porous media

NASA Astrophysics Data System (ADS)

Boccardo, Gianluca; Crevacore, Eleonora; Sethi, Rajandrea; Icardi, Matteo

2018-05-01

In the upscaling from pore to continuum (Darcy) scale, reaction and deposition phenomena at the solid-liquid interface of a porous medium have to be represented by macroscopic reaction source terms. The effective rates can be computed, in the case of periodic media, from three-dimensional microscopic simulations of the periodic cell. Several computational and semi-analytical models have been studied in the field of colloid filtration to describe this problem. They typically rely on effective deposition rates defined by complex fitting procedures, neglecting the advection-diffusion interplay, the pore-scale flow complexity, and assuming slow reactions (or large Péclet numbers). Therefore, when these rates are inserted into general macroscopic transport equations, they can lead to several conceptual inconsistencies and significant errors. To study more accurately the dependence of deposition on the flow parameters, in this work we advocate a clear distinction between the surface processes (that altogether defines the so-called attachment efficiency), and the pore-scale processes. With this approach, valid when colloidal particles are small enough, we study Brownian and gravity-driven deposition on a face-centred cubic (FCC) arrangement of spherical grains, and define a robust upscaling based on a linear effective reaction rate. The case of partial deposition, defined by an attachment probability, is studied and the limit of perfect sink is retrieved as a particular case. We introduce a novel upscaling approach and a particularly convenient computational setup that allows the direct computation of the asymptotic stationary value of effective rates. This allows to drastically reduce the computational domain down to the scale of the single repeating periodic unit. The savings are ever more noticeable in the case of higher Péclet numbers, when larger physical times are needed to reach the asymptotic regime and thus, equivalently, much larger computational domain and simulation time would be needed in a traditional setup. We show how this new definition of deposition rate is more robust and extendable to the whole range of Péclet numbers; it also is consistent with the classical heat and mass transfer literature.

G-LoSA: An efficient computational tool for local structure-centric biological studies and drug design.

PubMed

Lee, Hui Sun; Im, Wonpil

2016-04-01

Molecular recognition by protein mostly occurs in a local region on the protein surface. Thus, an efficient computational method for accurate characterization of protein local structural conservation is necessary to better understand biology and drug design. We present a novel local structure alignment tool, G-LoSA. G-LoSA aligns protein local structures in a sequence order independent way and provides a GA-score, a chemical feature-based and size-independent structure similarity score. Our benchmark validation shows the robust performance of G-LoSA to the local structures of diverse sizes and characteristics, demonstrating its universal applicability to local structure-centric comparative biology studies. In particular, G-LoSA is highly effective in detecting conserved local regions on the entire surface of a given protein. In addition, the applications of G-LoSA to identifying template ligands and predicting ligand and protein binding sites illustrate its strong potential for computer-aided drug design. We hope that G-LoSA can be a useful computational method for exploring interesting biological problems through large-scale comparison of protein local structures and facilitating drug discovery research and development. G-LoSA is freely available to academic users at http://im.compbio.ku.edu/GLoSA/. © 2016 The Protein Society.

Fast global image smoothing based on weighted least squares.

PubMed

Min, Dongbo; Choi, Sunghwan; Lu, Jiangbo; Ham, Bumsub; Sohn, Kwanghoon; Do, Minh N

2014-12-01

This paper presents an efficient technique for performing a spatially inhomogeneous edge-preserving image smoothing, called fast global smoother. Focusing on sparse Laplacian matrices consisting of a data term and a prior term (typically defined using four or eight neighbors for 2D image), our approach efficiently solves such global objective functions. In particular, we approximate the solution of the memory-and computation-intensive large linear system, defined over a d-dimensional spatial domain, by solving a sequence of 1D subsystems. Our separable implementation enables applying a linear-time tridiagonal matrix algorithm to solve d three-point Laplacian matrices iteratively. Our approach combines the best of two paradigms, i.e., efficient edge-preserving filters and optimization-based smoothing. Our method has a comparable runtime to the fast edge-preserving filters, but its global optimization formulation overcomes many limitations of the local filtering approaches. Our method also achieves high-quality results as the state-of-the-art optimization-based techniques, but runs ∼10-30 times faster. Besides, considering the flexibility in defining an objective function, we further propose generalized fast algorithms that perform Lγ norm smoothing (0 < γ < 2) and support an aggregated (robust) data term for handling imprecise data constraints. We demonstrate the effectiveness and efficiency of our techniques in a range of image processing and computer graphics applications.

Posture recognition based on fuzzy logic for home monitoring of the elderly.

PubMed

Brulin, Damien; Benezeth, Yannick; Courtial, Estelle

2012-09-01

We propose in this paper a computer vision-based posture recognition method for home monitoring of the elderly. The proposed system performs human detection prior to the posture analysis; posture recognition is performed only on a human silhouette. The human detection approach has been designed to be robust to different environmental stimuli. Thus, posture is analyzed with simple and efficient features that are not designed to manage constraints related to the environment but only designed to describe human silhouettes. The posture recognition method, based on fuzzy logic, identifies four static postures and is robust to variation in the distance between the camera and the person, and to the person's morphology. With an accuracy of 74.29% of satisfactory posture recognition, this approach can detect emergency situations such as a fall within a health smart home.

A robust cloud registration method based on redundant data reduction using backpropagation neural network and shift window

NASA Astrophysics Data System (ADS)

Xin, Meiting; Li, Bing; Yan, Xiao; Chen, Lei; Wei, Xiang

2018-02-01

A robust coarse-to-fine registration method based on the backpropagation (BP) neural network and shift window technology is proposed in this study. Specifically, there are three steps: coarse alignment between the model data and measured data, data simplification based on the BP neural network and point reservation in the contour region of point clouds, and fine registration with the reweighted iterative closest point algorithm. In the process of rough alignment, the initial rotation matrix and the translation vector between the two datasets are obtained. After performing subsequent simplification operations, the number of points can be reduced greatly. Therefore, the time and space complexity of the accurate registration can be significantly reduced. The experimental results show that the proposed method improves the computational efficiency without loss of accuracy.

CoreTSAR: Core Task-Size Adapting Runtime

DOE PAGES

Scogland, Thomas R. W.; Feng, Wu-chun; Rountree, Barry; ...

2014-10-27

Heterogeneity continues to increase at all levels of computing, with the rise of accelerators such as GPUs, FPGAs, and other co-processors into everything from desktops to supercomputers. As a consequence, efficiently managing such disparate resources has become increasingly complex. CoreTSAR seeks to reduce this complexity by adaptively worksharing parallel-loop regions across compute resources without requiring any transformation of the code within the loop. Lastly, our results show performance improvements of up to three-fold over a current state-of-the-art heterogeneous task scheduler as well as linear performance scaling from a single GPU to four GPUs for many codes. In addition, CoreTSAR demonstratesmore » a robust ability to adapt to both a variety of workloads and underlying system configurations.« less

Face pose tracking using the four-point algorithm

NASA Astrophysics Data System (ADS)

Fung, Ho Yin; Wong, Kin Hong; Yu, Ying Kin; Tsui, Kwan Pang; Kam, Ho Chuen

2017-06-01

In this paper, we have developed an algorithm to track the pose of a human face robustly and efficiently. Face pose estimation is very useful in many applications such as building virtual reality systems and creating an alternative input method for the disabled. Firstly, we have modified a face detection toolbox called DLib for the detection of a face in front of a camera. The detected face features are passed to a pose estimation method, known as the four-point algorithm, for pose computation. The theory applied and the technical problems encountered during system development are discussed in the paper. It is demonstrated that the system is able to track the pose of a face in real time using a consumer grade laptop computer.

An open-source computational and data resource to analyze digital maps of immunopeptidomes

PubMed Central

Caron, Etienne; Espona, Lucia; Kowalewski, Daniel J; Schuster, Heiko; Ternette, Nicola; Alpízar, Adán; Schittenhelm, Ralf B; Ramarathinam, Sri H; Lindestam Arlehamn, Cecilia S; Chiek Koh, Ching; Gillet, Ludovic C; Rabsteyn, Armin; Navarro, Pedro; Kim, Sangtae; Lam, Henry; Sturm, Theo; Marcilla, Miguel; Sette, Alessandro; Campbell, David S; Deutsch, Eric W; Moritz, Robert L; Purcell, Anthony W; Rammensee, Hans-Georg; Stevanovic, Stefan; Aebersold, Ruedi

2015-01-01

We present a novel mass spectrometry-based high-throughput workflow and an open-source computational and data resource to reproducibly identify and quantify HLA-associated peptides. Collectively, the resources support the generation of HLA allele-specific peptide assay libraries consisting of consensus fragment ion spectra, and the analysis of quantitative digital maps of HLA peptidomes generated from a range of biological sources by SWATH mass spectrometry (MS). This study represents the first community-based effort to develop a robust platform for the reproducible and quantitative measurement of the entire repertoire of peptides presented by HLA molecules, an essential step towards the design of efficient immunotherapies. DOI: http://dx.doi.org/10.7554/eLife.07661.001 PMID:26154972

Large-scale linear rankSVM.

PubMed

Lee, Ching-Pei; Lin, Chih-Jen

2014-04-01

Linear rankSVM is one of the widely used methods for learning to rank. Although its performance may be inferior to nonlinear methods such as kernel rankSVM and gradient boosting decision trees, linear rankSVM is useful to quickly produce a baseline model. Furthermore, following its recent development for classification, linear rankSVM may give competitive performance for large and sparse data. A great deal of works have studied linear rankSVM. The focus is on the computational efficiency when the number of preference pairs is large. In this letter, we systematically study existing works, discuss their advantages and disadvantages, and propose an efficient algorithm. We discuss different implementation issues and extensions with detailed experiments. Finally, we develop a robust linear rankSVM tool for public use.

Advanced Applications of Adifor 3.0 for Efficient Calculation of First-and Second-Order CFD Sensitivity Derivatives

NASA Technical Reports Server (NTRS)

Taylor, Arthur C., III

2004-01-01

This final report will document the accomplishments of the work of this project. 1) The incremental-iterative (II) form of the reverse-mode (adjoint) method for computing first-order (FO) aerodynamic sensitivity derivatives (SDs) has been successfully implemented and tested in a 2D CFD code (called ANSERS) using the reverse-mode capability of ADIFOR 3.0. These preceding results compared very well with similar SDS computed via a black-box (BB) application of the reverse-mode capability of ADIFOR 3.0, and also with similar SDs calculated via the method of finite differences. 2) Second-order (SO) SDs have been implemented in the 2D ASNWERS code using the very efficient strategy that was originally proposed (but not previously tested) of Reference 3, Appendix A. Furthermore, these SO SOs have been validated for accuracy and computational efficiency. 3) Studies were conducted in Quasi-1D and 2D concerning the smoothness (or lack of smoothness) of the FO and SO SD's for flows with shock waves. The phenomenon is documented in the publications of this study (listed subsequently), however, the specific numerical mechanism which is responsible for this unsmoothness phenomenon was not discovered. 4) The FO and SO derivatives for Quasi-1D and 2D flows were applied to predict aerodynamic design uncertainties, and were also applied in robust design optimization studies.

A CLASS OF RECONSTRUCTED DISCONTINUOUS GALERKIN METHODS IN COMPUTATIONAL FLUID DYNAMICS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hong Luo; Yidong Xia; Robert Nourgaliev

2011-05-01

A class of reconstructed discontinuous Galerkin (DG) methods is presented to solve compressible flow problems on arbitrary grids. The idea is to combine the efficiency of the reconstruction methods in finite volume methods and the accuracy of the DG methods to obtain a better numerical algorithm in computational fluid dynamics. The beauty of the resulting reconstructed discontinuous Galerkin (RDG) methods is that they provide a unified formulation for both finite volume and DG methods, and contain both classical finite volume and standard DG methods as two special cases of the RDG methods, and thus allow for a direct efficiency comparison.more » Both Green-Gauss and least-squares reconstruction methods and a least-squares recovery method are presented to obtain a quadratic polynomial representation of the underlying linear discontinuous Galerkin solution on each cell via a so-called in-cell reconstruction process. The devised in-cell reconstruction is aimed to augment the accuracy of the discontinuous Galerkin method by increasing the order of the underlying polynomial solution. These three reconstructed discontinuous Galerkin methods are used to compute a variety of compressible flow problems on arbitrary meshes to assess their accuracy. The numerical experiments demonstrate that all three reconstructed discontinuous Galerkin methods can significantly improve the accuracy of the underlying second-order DG method, although the least-squares reconstructed DG method provides the best performance in terms of both accuracy, efficiency, and robustness.« less

Computer-aided detection of renal calculi from noncontrast CT images using TV-flow and MSER features.

PubMed

Liu, Jianfei; Wang, Shijun; Turkbey, Evrim B; Linguraru, Marius George; Yao, Jianhua; Summers, Ronald M

2015-01-01

Renal calculi are common extracolonic incidental findings on computed tomographic colonography (CTC). This work aims to develop a fully automated computer-aided diagnosis system to accurately detect renal calculi on CTC images. The authors developed a total variation (TV) flow method to reduce image noise within the kidneys while maintaining the characteristic appearance of renal calculi. Maximally stable extremal region (MSER) features were then calculated to robustly identify calculi candidates. Finally, the authors computed texture and shape features that were imported to support vector machines for calculus classification. The method was validated on a dataset of 192 patients and compared to a baseline approach that detects calculi by thresholding. The authors also compared their method with the detection approaches using anisotropic diffusion and nonsmoothing. At a false positive rate of 8 per patient, the sensitivities of the new method and the baseline thresholding approach were 69% and 35% (p < 1e - 3) on all calculi from 1 to 433 mm(3) in the testing dataset. The sensitivities of the detection methods using anisotropic diffusion and nonsmoothing were 36% and 0%, respectively. The sensitivity of the new method increased to 90% if only larger and more clinically relevant calculi were considered. Experimental results demonstrated that TV-flow and MSER features are efficient means to robustly and accurately detect renal calculi on low-dose, high noise CTC images. Thus, the proposed method can potentially improve diagnosis.

Computer-aided detection of renal calculi from noncontrast CT images using TV-flow and MSER features

PubMed Central

Liu, Jianfei; Wang, Shijun; Turkbey, Evrim B.; Linguraru, Marius George; Yao, Jianhua; Summers, Ronald M.

2015-01-01

Purpose: Renal calculi are common extracolonic incidental findings on computed tomographic colonography (CTC). This work aims to develop a fully automated computer-aided diagnosis system to accurately detect renal calculi on CTC images. Methods: The authors developed a total variation (TV) flow method to reduce image noise within the kidneys while maintaining the characteristic appearance of renal calculi. Maximally stable extremal region (MSER) features were then calculated to robustly identify calculi candidates. Finally, the authors computed texture and shape features that were imported to support vector machines for calculus classification. The method was validated on a dataset of 192 patients and compared to a baseline approach that detects calculi by thresholding. The authors also compared their method with the detection approaches using anisotropic diffusion and nonsmoothing. Results: At a false positive rate of 8 per patient, the sensitivities of the new method and the baseline thresholding approach were 69% and 35% (p < 1e − 3) on all calculi from 1 to 433 mm3 in the testing dataset. The sensitivities of the detection methods using anisotropic diffusion and nonsmoothing were 36% and 0%, respectively. The sensitivity of the new method increased to 90% if only larger and more clinically relevant calculi were considered. Conclusions: Experimental results demonstrated that TV-flow and MSER features are efficient means to robustly and accurately detect renal calculi on low-dose, high noise CTC images. Thus, the proposed method can potentially improve diagnosis. PMID:25563255

Patch forest: a hybrid framework of random forest and patch-based segmentation

NASA Astrophysics Data System (ADS)

Xie, Zhongliu; Gillies, Duncan

2016-03-01

The development of an accurate, robust and fast segmentation algorithm has long been a research focus in medical computer vision. State-of-the-art practices often involve non-rigidly registering a target image with a set of training atlases for label propagation over the target space to perform segmentation, a.k.a. multi-atlas label propagation (MALP). In recent years, the patch-based segmentation (PBS) framework has gained wide attention due to its advantage of relaxing the strict voxel-to-voxel correspondence to a series of pair-wise patch comparisons for contextual pattern matching. Despite a high accuracy reported in many scenarios, computational efficiency has consistently been a major obstacle for both approaches. Inspired by recent work on random forest, in this paper we propose a patch forest approach, which by equipping the conventional PBS with a fast patch search engine, is able to boost segmentation speed significantly while retaining an equal level of accuracy. In addition, a fast forest training mechanism is also proposed, with the use of a dynamic grid framework to efficiently approximate data compactness computation and a 3D integral image technique for fast box feature retrieval.

Scalable domain decomposition solvers for stochastic PDEs in high performance computing

DOE PAGES

Desai, Ajit; Khalil, Mohammad; Pettit, Chris; ...

2017-09-21

Stochastic spectral finite element models of practical engineering systems may involve solutions of linear systems or linearized systems for non-linear problems with billions of unknowns. For stochastic modeling, it is therefore essential to design robust, parallel and scalable algorithms that can efficiently utilize high-performance computing to tackle such large-scale systems. Domain decomposition based iterative solvers can handle such systems. And though these algorithms exhibit excellent scalabilities, significant algorithmic and implementational challenges exist to extend them to solve extreme-scale stochastic systems using emerging computing platforms. Intrusive polynomial chaos expansion based domain decomposition algorithms are extended here to concurrently handle high resolutionmore » in both spatial and stochastic domains using an in-house implementation. Sparse iterative solvers with efficient preconditioners are employed to solve the resulting global and subdomain level local systems through multi-level iterative solvers. We also use parallel sparse matrix–vector operations to reduce the floating-point operations and memory requirements. Numerical and parallel scalabilities of these algorithms are presented for the diffusion equation having spatially varying diffusion coefficient modeled by a non-Gaussian stochastic process. Scalability of the solvers with respect to the number of random variables is also investigated.« less

Scalable domain decomposition solvers for stochastic PDEs in high performance computing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Desai, Ajit; Khalil, Mohammad; Pettit, Chris

Stochastic spectral finite element models of practical engineering systems may involve solutions of linear systems or linearized systems for non-linear problems with billions of unknowns. For stochastic modeling, it is therefore essential to design robust, parallel and scalable algorithms that can efficiently utilize high-performance computing to tackle such large-scale systems. Domain decomposition based iterative solvers can handle such systems. And though these algorithms exhibit excellent scalabilities, significant algorithmic and implementational challenges exist to extend them to solve extreme-scale stochastic systems using emerging computing platforms. Intrusive polynomial chaos expansion based domain decomposition algorithms are extended here to concurrently handle high resolutionmore » in both spatial and stochastic domains using an in-house implementation. Sparse iterative solvers with efficient preconditioners are employed to solve the resulting global and subdomain level local systems through multi-level iterative solvers. We also use parallel sparse matrix–vector operations to reduce the floating-point operations and memory requirements. Numerical and parallel scalabilities of these algorithms are presented for the diffusion equation having spatially varying diffusion coefficient modeled by a non-Gaussian stochastic process. Scalability of the solvers with respect to the number of random variables is also investigated.« less

Agile Multi-Scale Decompositions for Automatic Image Registration

NASA Technical Reports Server (NTRS)

Murphy, James M.; Leija, Omar Navarro; Le Moigne, Jacqueline

2016-01-01

In recent works, the first and third authors developed an automatic image registration algorithm based on a multiscale hybrid image decomposition with anisotropic shearlets and isotropic wavelets. This prototype showed strong performance, improving robustness over registration with wavelets alone. However, this method imposed a strict hierarchy on the order in which shearlet and wavelet features were used in the registration process, and also involved an unintegrated mixture of MATLAB and C code. In this paper, we introduce a more agile model for generating features, in which a flexible and user-guided mix of shearlet and wavelet features are computed. Compared to the previous prototype, this method introduces a flexibility to the order in which shearlet and wavelet features are used in the registration process. Moreover, the present algorithm is now fully coded in C, making it more efficient and portable than the MATLAB and C prototype. We demonstrate the versatility and computational efficiency of this approach by performing registration experiments with the fully-integrated C algorithm. In particular, meaningful timing studies can now be performed, to give a concrete analysis of the computational costs of the flexible feature extraction. Examples of synthetically warped and real multi-modal images are analyzed.

CNVcaller: highly efficient and widely applicable software for detecting copy number variations in large populations.

PubMed

Wang, Xihong; Zheng, Zhuqing; Cai, Yudong; Chen, Ting; Li, Chao; Fu, Weiwei; Jiang, Yu

2017-12-01

The increasing amount of sequencing data available for a wide variety of species can be theoretically used for detecting copy number variations (CNVs) at the population level. However, the growing sample sizes and the divergent complexity of nonhuman genomes challenge the efficiency and robustness of current human-oriented CNV detection methods. Here, we present CNVcaller, a read-depth method for discovering CNVs in population sequencing data. The computational speed of CNVcaller was 1-2 orders of magnitude faster than CNVnator and Genome STRiP for complex genomes with thousands of unmapped scaffolds. CNV detection of 232 goats required only 1.4 days on a single compute node. Additionally, the Mendelian consistency of sheep trios indicated that CNVcaller mitigated the influence of high proportions of gaps and misassembled duplications in the nonhuman reference genome assembly. Furthermore, multiple evaluations using real sheep and human data indicated that CNVcaller achieved the best accuracy and sensitivity for detecting duplications. The fast generalized detection algorithms included in CNVcaller overcome prior computational barriers for detecting CNVs in large-scale sequencing data with complex genomic structures. Therefore, CNVcaller promotes population genetic analyses of functional CNVs in more species. © The Authors 2017. Published by Oxford University Press.

CNVcaller: highly efficient and widely applicable software for detecting copy number variations in large populations

PubMed Central

Wang, Xihong; Zheng, Zhuqing; Cai, Yudong; Chen, Ting; Li, Chao; Fu, Weiwei

2017-01-01

Abstract Background The increasing amount of sequencing data available for a wide variety of species can be theoretically used for detecting copy number variations (CNVs) at the population level. However, the growing sample sizes and the divergent complexity of nonhuman genomes challenge the efficiency and robustness of current human-oriented CNV detection methods. Results Here, we present CNVcaller, a read-depth method for discovering CNVs in population sequencing data. The computational speed of CNVcaller was 1–2 orders of magnitude faster than CNVnator and Genome STRiP for complex genomes with thousands of unmapped scaffolds. CNV detection of 232 goats required only 1.4 days on a single compute node. Additionally, the Mendelian consistency of sheep trios indicated that CNVcaller mitigated the influence of high proportions of gaps and misassembled duplications in the nonhuman reference genome assembly. Furthermore, multiple evaluations using real sheep and human data indicated that CNVcaller achieved the best accuracy and sensitivity for detecting duplications. Conclusions The fast generalized detection algorithms included in CNVcaller overcome prior computational barriers for detecting CNVs in large-scale sequencing data with complex genomic structures. Therefore, CNVcaller promotes population genetic analyses of functional CNVs in more species. PMID:29220491

Addressing Curse of Dimensionality in Sensitivity Analysis: How Can We Handle High-Dimensional Problems?

NASA Astrophysics Data System (ADS)

Safaei, S.; Haghnegahdar, A.; Razavi, S.

2016-12-01

Complex environmental models are now the primary tool to inform decision makers for the current or future management of environmental resources under the climate and environmental changes. These complex models often contain a large number of parameters that need to be determined by a computationally intensive calibration procedure. Sensitivity analysis (SA) is a very useful tool that not only allows for understanding the model behavior, but also helps in reducing the number of calibration parameters by identifying unimportant ones. The issue is that most global sensitivity techniques are highly computationally demanding themselves for generating robust and stable sensitivity metrics over the entire model response surface. Recently, a novel global sensitivity analysis method, Variogram Analysis of Response Surfaces (VARS), is introduced that can efficiently provide a comprehensive assessment of global sensitivity using the Variogram concept. In this work, we aim to evaluate the effectiveness of this highly efficient GSA method in saving computational burden, when applied to systems with extra-large number of input factors ( 100). We use a test function and a hydrological modelling case study to demonstrate the capability of VARS method in reducing problem dimensionality by identifying important vs unimportant input factors.

High-performance integrated virtual environment (HIVE): a robust infrastructure for next-generation sequence data analysis

PubMed Central

Simonyan, Vahan; Chumakov, Konstantin; Dingerdissen, Hayley; Faison, William; Goldweber, Scott; Golikov, Anton; Gulzar, Naila; Karagiannis, Konstantinos; Vinh Nguyen Lam, Phuc; Maudru, Thomas; Muravitskaja, Olesja; Osipova, Ekaterina; Pan, Yang; Pschenichnov, Alexey; Rostovtsev, Alexandre; Santana-Quintero, Luis; Smith, Krista; Thompson, Elaine E.; Tkachenko, Valery; Torcivia-Rodriguez, John; Wan, Quan; Wang, Jing; Wu, Tsung-Jung; Wilson, Carolyn; Mazumder, Raja

2016-01-01

The High-performance Integrated Virtual Environment (HIVE) is a distributed storage and compute environment designed primarily to handle next-generation sequencing (NGS) data. This multicomponent cloud infrastructure provides secure web access for authorized users to deposit, retrieve, annotate and compute on NGS data, and to analyse the outcomes using web interface visual environments appropriately built in collaboration with research and regulatory scientists and other end users. Unlike many massively parallel computing environments, HIVE uses a cloud control server which virtualizes services, not processes. It is both very robust and flexible due to the abstraction layer introduced between computational requests and operating system processes. The novel paradigm of moving computations to the data, instead of moving data to computational nodes, has proven to be significantly less taxing for both hardware and network infrastructure. The honeycomb data model developed for HIVE integrates metadata into an object-oriented model. Its distinction from other object-oriented databases is in the additional implementation of a unified application program interface to search, view and manipulate data of all types. This model simplifies the introduction of new data types, thereby minimizing the need for database restructuring and streamlining the development of new integrated information systems. The honeycomb model employs a highly secure hierarchical access control and permission system, allowing determination of data access privileges in a finely granular manner without flooding the security subsystem with a multiplicity of rules. HIVE infrastructure will allow engineers and scientists to perform NGS analysis in a manner that is both efficient and secure. HIVE is actively supported in public and private domains, and project collaborations are welcomed. Database URL: https://hive.biochemistry.gwu.edu PMID:26989153

High-performance integrated virtual environment (HIVE): a robust infrastructure for next-generation sequence data analysis.

PubMed

Simonyan, Vahan; Chumakov, Konstantin; Dingerdissen, Hayley; Faison, William; Goldweber, Scott; Golikov, Anton; Gulzar, Naila; Karagiannis, Konstantinos; Vinh Nguyen Lam, Phuc; Maudru, Thomas; Muravitskaja, Olesja; Osipova, Ekaterina; Pan, Yang; Pschenichnov, Alexey; Rostovtsev, Alexandre; Santana-Quintero, Luis; Smith, Krista; Thompson, Elaine E; Tkachenko, Valery; Torcivia-Rodriguez, John; Voskanian, Alin; Wan, Quan; Wang, Jing; Wu, Tsung-Jung; Wilson, Carolyn; Mazumder, Raja

2016-01-01

The High-performance Integrated Virtual Environment (HIVE) is a distributed storage and compute environment designed primarily to handle next-generation sequencing (NGS) data. This multicomponent cloud infrastructure provides secure web access for authorized users to deposit, retrieve, annotate and compute on NGS data, and to analyse the outcomes using web interface visual environments appropriately built in collaboration with research and regulatory scientists and other end users. Unlike many massively parallel computing environments, HIVE uses a cloud control server which virtualizes services, not processes. It is both very robust and flexible due to the abstraction layer introduced between computational requests and operating system processes. The novel paradigm of moving computations to the data, instead of moving data to computational nodes, has proven to be significantly less taxing for both hardware and network infrastructure.The honeycomb data model developed for HIVE integrates metadata into an object-oriented model. Its distinction from other object-oriented databases is in the additional implementation of a unified application program interface to search, view and manipulate data of all types. This model simplifies the introduction of new data types, thereby minimizing the need for database restructuring and streamlining the development of new integrated information systems. The honeycomb model employs a highly secure hierarchical access control and permission system, allowing determination of data access privileges in a finely granular manner without flooding the security subsystem with a multiplicity of rules. HIVE infrastructure will allow engineers and scientists to perform NGS analysis in a manner that is both efficient and secure. HIVE is actively supported in public and private domains, and project collaborations are welcomed. Database URL: https://hive.biochemistry.gwu.edu. © The Author(s) 2016. Published by Oxford University Press.

Loci-STREAM Version 0.9

NASA Technical Reports Server (NTRS)

Wright, Jeffrey; Thakur, Siddharth

2006-01-01

Loci-STREAM is an evolving computational fluid dynamics (CFD) software tool for simulating possibly chemically reacting, possibly unsteady flows in diverse settings, including rocket engines, turbomachines, oil refineries, etc. Loci-STREAM implements a pressure- based flow-solving algorithm that utilizes unstructured grids. (The benefit of low memory usage by pressure-based algorithms is well recognized by experts in the field.) The algorithm is robust for flows at all speeds from zero to hypersonic. The flexibility of arbitrary polyhedral grids enables accurate, efficient simulation of flows in complex geometries, including those of plume-impingement problems. The present version - Loci-STREAM version 0.9 - includes an interface with the Portable, Extensible Toolkit for Scientific Computation (PETSc) library for access to enhanced linear-equation-solving programs therein that accelerate convergence toward a solution. The name "Loci" reflects the creation of this software within the Loci computational framework, which was developed at Mississippi State University for the primary purpose of simplifying the writing of complex multidisciplinary application programs to run in distributed-memory computing environments including clusters of personal computers. Loci has been designed to relieve application programmers of the details of programming for distributed-memory computers.

GPU-accelerated FDTD modeling of radio-frequency field-tissue interactions in high-field MRI.

PubMed

Chi, Jieru; Liu, Feng; Weber, Ewald; Li, Yu; Crozier, Stuart

2011-06-01

The analysis of high-field RF field-tissue interactions requires high-performance finite-difference time-domain (FDTD) computing. Conventional CPU-based FDTD calculations offer limited computing performance in a PC environment. This study presents a graphics processing unit (GPU)-based parallel-computing framework, producing substantially boosted computing efficiency (with a two-order speedup factor) at a PC-level cost. Specific details of implementing the FDTD method on a GPU architecture have been presented and the new computational strategy has been successfully applied to the design of a novel 8-element transceive RF coil system at 9.4 T. Facilitated by the powerful GPU-FDTD computing, the new RF coil array offers optimized fields (averaging 25% improvement in sensitivity, and 20% reduction in loop coupling compared with conventional array structures of the same size) for small animal imaging with a robust RF configuration. The GPU-enabled acceleration paves the way for FDTD to be applied for both detailed forward modeling and inverse design of MRI coils, which were previously impractical.

Robust Online Monitoring for Calibration Assessment of Transmitters and Instrumentation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ramuhalli, Pradeep; Coble, Jamie B.; Shumaker, Brent

Robust online monitoring (OLM) technologies are expected to enable the extension or elimination of periodic sensor calibration intervals in operating and new reactors. These advances in OLM technologies will improve the safety and reliability of current and planned nuclear power systems through improved accuracy and increased reliability of sensors used to monitor key parameters. In this article, we discuss an overview of research being performed within the Nuclear Energy Enabling Technologies (NEET)/Advanced Sensors and Instrumentation (ASI) program, for the development of OLM algorithms to use sensor outputs and, in combination with other available information, 1) determine whether one or moremore » sensors are out of calibration or failing and 2) replace a failing sensor with reliable, accurate sensor outputs. Algorithm development is focused on the following OLM functions: • Signal validation • Virtual sensing • Sensor response-time assessment These algorithms incorporate, at their base, a Gaussian Process-based uncertainty quantification (UQ) method. Various plant models (using kernel regression, GP, or hierarchical models) may be used to predict sensor responses under various plant conditions. These predicted responses can then be applied in fault detection (sensor output and response time) and in computing the correct value (virtual sensing) of a failing physical sensor. The methods being evaluated in this work can compute confidence levels along with the predicted sensor responses, and as a result, may have the potential for compensating for sensor drift in real-time (online recalibration). Evaluation was conducted using data from multiple sources (laboratory flow loops and plant data). Ongoing research in this project is focused on further evaluation of the algorithms, optimization for accuracy and computational efficiency, and integration into a suite of tools for robust OLM that are applicable to monitoring sensor calibration state in nuclear power plants.« less

F-15B QuietSpike(TradeMark) Aeroservoelastic Flight Test Data Analysis

NASA Technical Reports Server (NTRS)

Kukreja, Sunil L.

2007-01-01

System identification or mathematical modelling is utilised in the aerospace community for the development of simulation models for robust control law design. These models are often described as linear, time-invariant processes and assumed to be uniform throughout the flight envelope. Nevertheless, it is well known that the underlying process is inherently nonlinear. The reason for utilising a linear approach has been due to the lack of a proper set of tools for the identification of nonlinear systems. Over the past several decades the controls and biomedical communities have made great advances in developing tools for the identification of nonlinear systems. These approaches are robust and readily applicable to aerospace systems. In this paper, we show the application of one such nonlinear system identification technique, structure detection, for the analysis of F-15B QuietSpike(TradeMark) aeroservoelastic flight test data. Structure detection is concerned with the selection of a subset of candidate terms that best describe the observed output. This is a necessary procedure to compute an efficient system description which may afford greater insight into the functionality of the system or a simpler controller design. Structure computation as a tool for black-box modelling may be of critical importance for the development of robust, parsimonious models for the flight-test community. Moreover, this approach may lead to efficient strategies for rapid envelope expansion which may save significant development time and costs. The objectives of this study are to demonstrate via analysis of F-15B QuietSpike(TradeMark) aeroservoelastic flight test data for several flight conditions (Mach number) that (i) linear models are inefficient for modelling aeroservoelastic data, (ii) nonlinear identification provides a parsimonious model description whilst providing a high percent fit for cross-validated data and (iii) the model structure and parameters vary as the flight condition is altered.

Fast Compressive Tracking.

PubMed

Zhang, Kaihua; Zhang, Lei; Yang, Ming-Hsuan

2014-10-01

It is a challenging task to develop effective and efficient appearance models for robust object tracking due to factors such as pose variation, illumination change, occlusion, and motion blur. Existing online tracking algorithms often update models with samples from observations in recent frames. Despite much success has been demonstrated, numerous issues remain to be addressed. First, while these adaptive appearance models are data-dependent, there does not exist sufficient amount of data for online algorithms to learn at the outset. Second, online tracking algorithms often encounter the drift problems. As a result of self-taught learning, misaligned samples are likely to be added and degrade the appearance models. In this paper, we propose a simple yet effective and efficient tracking algorithm with an appearance model based on features extracted from a multiscale image feature space with data-independent basis. The proposed appearance model employs non-adaptive random projections that preserve the structure of the image feature space of objects. A very sparse measurement matrix is constructed to efficiently extract the features for the appearance model. We compress sample images of the foreground target and the background using the same sparse measurement matrix. The tracking task is formulated as a binary classification via a naive Bayes classifier with online update in the compressed domain. A coarse-to-fine search strategy is adopted to further reduce the computational complexity in the detection procedure. The proposed compressive tracking algorithm runs in real-time and performs favorably against state-of-the-art methods on challenging sequences in terms of efficiency, accuracy and robustness.

Adaptive time-stepping Monte Carlo integration of Coulomb collisions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sarkimaki, Konsta; Hirvijoki, E.; Terava, J.

Here, we report an accessible and robust tool for evaluating the effects of Coulomb collisions on a test particle in a plasma that obeys Maxwell–Jüttner statistics. The implementation is based on the Beliaev–Budker collision integral which allows both the test particle and the background plasma to be relativistic. The integration method supports adaptive time stepping, which is shown to greatly improve the computational efficiency. The Monte Carlo method is implemented for both the three-dimensional particle momentum space and the five-dimensional guiding center phase space.

Adaptive time-stepping Monte Carlo integration of Coulomb collisions

DOE PAGES

Sarkimaki, Konsta; Hirvijoki, E.; Terava, J.

2017-10-12

Here, we report an accessible and robust tool for evaluating the effects of Coulomb collisions on a test particle in a plasma that obeys Maxwell–Jüttner statistics. The implementation is based on the Beliaev–Budker collision integral which allows both the test particle and the background plasma to be relativistic. The integration method supports adaptive time stepping, which is shown to greatly improve the computational efficiency. The Monte Carlo method is implemented for both the three-dimensional particle momentum space and the five-dimensional guiding center phase space.

Extended Closed-form Expressions for the Robust Symmetrical Number System Dynamic Range and an Efficient Algorithm for its Computation

DTIC Science & Technology

2014-01-01

and distance between all of the vector ambiguity pairs for the combined N−sequences. To simplify our derivation, we define the center of ambiguity (COA...modulo N . The resulting structure of the N sequences ensures that two successive RSNS vectors (paired terms from all N sequences) when considered...represented by a vector , Xh = [x1,h, x2,h, . . . , xN,h] T , of N paired integers from each se- quence at h. For example, a left-shifted, three-sequence

Robust Optimization Design Algorithm for High-Frequency TWTs

NASA Technical Reports Server (NTRS)

Wilson, Jeffrey D.; Chevalier, Christine T.

2010-01-01

Traveling-wave tubes (TWTs), such as the Ka-band (26-GHz) model recently developed for the Lunar Reconnaissance Orbiter, are essential as communication amplifiers in spacecraft for virtually all near- and deep-space missions. This innovation is a computational design algorithm that, for the first time, optimizes the efficiency and output power of a TWT while taking into account the effects of dimensional tolerance variations. Because they are primary power consumers and power generation is very expensive in space, much effort has been exerted over the last 30 years to increase the power efficiency of TWTs. However, at frequencies higher than about 60 GHz, efficiencies of TWTs are still quite low. A major reason is that at higher frequencies, dimensional tolerance variations from conventional micromachining techniques become relatively large with respect to the circuit dimensions. When this is the case, conventional design- optimization procedures, which ignore dimensional variations, provide inaccurate designs for which the actual amplifier performance substantially under-performs that of the design. Thus, this new, robust TWT optimization design algorithm was created to take account of and ameliorate the deleterious effects of dimensional variations and to increase efficiency, power, and yield of high-frequency TWTs. This design algorithm can help extend the use of TWTs into the terahertz frequency regime of 300-3000 GHz. Currently, these frequencies are under-utilized because of the lack of efficient amplifiers, thus this regime is known as the "terahertz gap." The development of an efficient terahertz TWT amplifier could enable breakthrough applications in space science molecular spectroscopy, remote sensing, nondestructive testing, high-resolution "through-the-wall" imaging, biomedical imaging, and detection of explosives and toxic biochemical agents.

Review of Sparse Representation-Based Classification Methods on EEG Signal Processing for Epilepsy Detection, Brain-Computer Interface and Cognitive Impairment

PubMed Central

Wen, Dong; Jia, Peilei; Lian, Qiusheng; Zhou, Yanhong; Lu, Chengbiao

2016-01-01

At present, the sparse representation-based classification (SRC) has become an important approach in electroencephalograph (EEG) signal analysis, by which the data is sparsely represented on the basis of a fixed dictionary or learned dictionary and classified based on the reconstruction criteria. SRC methods have been used to analyze the EEG signals of epilepsy, cognitive impairment and brain computer interface (BCI), which made rapid progress including the improvement in computational accuracy, efficiency and robustness. However, these methods have deficiencies in real-time performance, generalization ability and the dependence of labeled sample in the analysis of the EEG signals. This mini review described the advantages and disadvantages of the SRC methods in the EEG signal analysis with the expectation that these methods can provide the better tools for analyzing EEG signals. PMID:27458376

A self-adaptive-grid method with application to airfoil flow

NASA Technical Reports Server (NTRS)

Nakahashi, K.; Deiwert, G. S.

1985-01-01

A self-adaptive-grid method is described that is suitable for multidimensional steady and unsteady computations. Based on variational principles, a spring analogy is used to redistribute grid points in an optimal sense to reduce the overall solution error. User-specified parameters, denoting both maximum and minimum permissible grid spacings, are used to define the all-important constants, thereby minimizing the empiricism and making the method self-adaptive. Operator splitting and one-sided controls for orthogonality and smoothness are used to make the method practical, robust, and efficient. Examples are included for both steady and unsteady viscous flow computations about airfoils in two dimensions, as well as for a steady inviscid flow computation and a one-dimensional case. These examples illustrate the precise control the user has with the self-adaptive method and demonstrate a significant improvement in accuracy and quality of the solutions.

Perspective: Quantum mechanical methods in biochemistry and biophysics.

PubMed

Cui, Qiang

2016-10-14

In this perspective article, I discuss several research topics relevant to quantum mechanical (QM) methods in biophysical and biochemical applications. Due to the immense complexity of biological problems, the key is to develop methods that are able to strike the proper balance of computational efficiency and accuracy for the problem of interest. Therefore, in addition to the development of novel ab initio and density functional theory based QM methods for the study of reactive events that involve complex motifs such as transition metal clusters in metalloenzymes, it is equally important to develop inexpensive QM methods and advanced classical or quantal force fields to describe different physicochemical properties of biomolecules and their behaviors in complex environments. Maintaining a solid connection of these more approximate methods with rigorous QM methods is essential to their transferability and robustness. Comparison to diverse experimental observables helps validate computational models and mechanistic hypotheses as well as driving further development of computational methodologies.

Numerical Algorithms for Acoustic Integrals - The Devil is in the Details

NASA Technical Reports Server (NTRS)

Brentner, Kenneth S.

1996-01-01

The accurate prediction of the aeroacoustic field generated by aerospace vehicles or nonaerospace machinery is necessary for designers to control and reduce source noise. Powerful computational aeroacoustic methods, based on various acoustic analogies (primarily the Lighthill acoustic analogy) and Kirchhoff methods, have been developed for prediction of noise from complicated sources, such as rotating blades. Both methods ultimately predict the noise through a numerical evaluation of an integral formulation. In this paper, we consider three generic acoustic formulations and several numerical algorithms that have been used to compute the solutions to these formulations. Algorithms for retarded-time formulations are the most efficient and robust, but they are difficult to implement for supersonic-source motion. Collapsing-sphere and emission-surface formulations are good alternatives when supersonic-source motion is present, but the numerical implementations of these formulations are more computationally demanding. New algorithms - which utilize solution adaptation to provide a specified error level - are needed.

Robust Selection Algorithm (RSA) for Multi-Omic Biomarker Discovery; Integration with Functional Network Analysis to Identify miRNA Regulated Pathways in Multiple Cancers.

PubMed

Sehgal, Vasudha; Seviour, Elena G; Moss, Tyler J; Mills, Gordon B; Azencott, Robert; Ram, Prahlad T

2015-01-01

MicroRNAs (miRNAs) play a crucial role in the maintenance of cellular homeostasis by regulating the expression of their target genes. As such, the dysregulation of miRNA expression has been frequently linked to cancer. With rapidly accumulating molecular data linked to patient outcome, the need for identification of robust multi-omic molecular markers is critical in order to provide clinical impact. While previous bioinformatic tools have been developed to identify potential biomarkers in cancer, these methods do not allow for rapid classification of oncogenes versus tumor suppressors taking into account robust differential expression, cutoffs, p-values and non-normality of the data. Here, we propose a methodology, Robust Selection Algorithm (RSA) that addresses these important problems in big data omics analysis. The robustness of the survival analysis is ensured by identification of optimal cutoff values of omics expression, strengthened by p-value computed through intensive random resampling taking into account any non-normality in the data and integration into multi-omic functional networks. Here we have analyzed pan-cancer miRNA patient data to identify functional pathways involved in cancer progression that are associated with selected miRNA identified by RSA. Our approach demonstrates the way in which existing survival analysis techniques can be integrated with a functional network analysis framework to efficiently identify promising biomarkers and novel therapeutic candidates across diseases.

Towards a Video Passive Content Fingerprinting Method for Partial-Copy Detection Robust against Non-Simulated Attacks

PubMed Central

2016-01-01

Passive content fingerprinting is widely used for video content identification and monitoring. However, many challenges remain unsolved especially for partial-copies detection. The main challenge is to find the right balance between the computational cost of fingerprint extraction and fingerprint dimension, without compromising detection performance against various attacks (robustness). Fast video detection performance is desirable in several modern applications, for instance, in those where video detection involves the use of large video databases or in applications requiring real-time video detection of partial copies, a process whose difficulty increases when videos suffer severe transformations. In this context, conventional fingerprinting methods are not fully suitable to cope with the attacks and transformations mentioned before, either because the robustness of these methods is not enough or because their execution time is very high, where the time bottleneck is commonly found in the fingerprint extraction and matching operations. Motivated by these issues, in this work we propose a content fingerprinting method based on the extraction of a set of independent binary global and local fingerprints. Although these features are robust against common video transformations, their combination is more discriminant against severe video transformations such as signal processing attacks, geometric transformations and temporal and spatial desynchronization. Additionally, we use an efficient multilevel filtering system accelerating the processes of fingerprint extraction and matching. This multilevel filtering system helps to rapidly identify potential similar video copies upon which the fingerprint process is carried out only, thus saving computational time. We tested with datasets of real copied videos, and the results show how our method outperforms state-of-the-art methods regarding detection scores. Furthermore, the granularity of our method makes it suitable for partial-copy detection; that is, by processing only short segments of 1 second length. PMID:27861492

The OSG open facility: A sharing ecosystem

DOE PAGES

Jayatilaka, B.; Levshina, T.; Rynge, M.; ...

2015-12-23

The Open Science Grid (OSG) ties together individual experiments’ computing power, connecting their resources to create a large, robust computing grid, this computing infrastructure started primarily as a collection of sites associated with large HEP experiments such as ATLAS, CDF, CMS, and DZero. In the years since, the OSG has broadened its focus to also address the needs of other US researchers and increased delivery of Distributed High Through-put Computing (DHTC) to users from a wide variety of disciplines via the OSG Open Facility. Presently, the Open Facility delivers about 100 million computing wall hours per year to researchers whomore » are not already associated with the owners of the computing sites, this is primarily accomplished by harvesting and organizing the temporarily unused capacity (i.e. opportunistic cycles) from the sites in the OSG. Using these methods, OSG resource providers and scientists share computing hours with researchers in many other fields to enable their science, striving to make sure that these computing power used with maximal efficiency. Furthermore, we believe that expanded access to DHTC is an essential tool for scientific innovation and work continues in expanding this service.« less

Predicting phenotype from genotype: Improving accuracy through more robust experimental and computational modeling

PubMed Central

Gallion, Jonathan; Koire, Amanda; Katsonis, Panagiotis; Schoenegge, Anne‐Marie; Bouvier, Michel

2017-01-01

Abstract Computational prediction yields efficient and scalable initial assessments of how variants of unknown significance may affect human health. However, when discrepancies between these predictions and direct experimental measurements of functional impact arise, inaccurate computational predictions are frequently assumed as the source. Here, we present a methodological analysis indicating that shortcomings in both computational and biological data can contribute to these disagreements. We demonstrate that incomplete assaying of multifunctional proteins can affect the strength of correlations between prediction and experiments; a variant's full impact on function is better quantified by considering multiple assays that probe an ensemble of protein functions. Additionally, many variants predictions are sensitive to protein alignment construction and can be customized to maximize relevance of predictions to a specific experimental question. We conclude that inconsistencies between computation and experiment can often be attributed to the fact that they do not test identical hypotheses. Aligning the design of the computational input with the design of the experimental output will require cooperation between computational and biological scientists, but will also lead to improved estimations of computational prediction accuracy and a better understanding of the genotype–phenotype relationship. PMID:28230923

Predicting phenotype from genotype: Improving accuracy through more robust experimental and computational modeling.

PubMed

Gallion, Jonathan; Koire, Amanda; Katsonis, Panagiotis; Schoenegge, Anne-Marie; Bouvier, Michel; Lichtarge, Olivier

2017-05-01

Computational prediction yields efficient and scalable initial assessments of how variants of unknown significance may affect human health. However, when discrepancies between these predictions and direct experimental measurements of functional impact arise, inaccurate computational predictions are frequently assumed as the source. Here, we present a methodological analysis indicating that shortcomings in both computational and biological data can contribute to these disagreements. We demonstrate that incomplete assaying of multifunctional proteins can affect the strength of correlations between prediction and experiments; a variant's full impact on function is better quantified by considering multiple assays that probe an ensemble of protein functions. Additionally, many variants predictions are sensitive to protein alignment construction and can be customized to maximize relevance of predictions to a specific experimental question. We conclude that inconsistencies between computation and experiment can often be attributed to the fact that they do not test identical hypotheses. Aligning the design of the computational input with the design of the experimental output will require cooperation between computational and biological scientists, but will also lead to improved estimations of computational prediction accuracy and a better understanding of the genotype-phenotype relationship. © 2017 The Authors. **Human Mutation published by Wiley Periodicals, Inc.

A transient FETI methodology for large-scale parallel implicit computations in structural mechanics

NASA Technical Reports Server (NTRS)

Farhat, Charbel; Crivelli, Luis; Roux, Francois-Xavier

1992-01-01

Explicit codes are often used to simulate the nonlinear dynamics of large-scale structural systems, even for low frequency response, because the storage and CPU requirements entailed by the repeated factorizations traditionally found in implicit codes rapidly overwhelm the available computing resources. With the advent of parallel processing, this trend is accelerating because explicit schemes are also easier to parallelize than implicit ones. However, the time step restriction imposed by the Courant stability condition on all explicit schemes cannot yet -- and perhaps will never -- be offset by the speed of parallel hardware. Therefore, it is essential to develop efficient and robust alternatives to direct methods that are also amenable to massively parallel processing because implicit codes using unconditionally stable time-integration algorithms are computationally more efficient when simulating low-frequency dynamics. Here we present a domain decomposition method for implicit schemes that requires significantly less storage than factorization algorithms, that is several times faster than other popular direct and iterative methods, that can be easily implemented on both shared and local memory parallel processors, and that is both computationally and communication-wise efficient. The proposed transient domain decomposition method is an extension of the method of Finite Element Tearing and Interconnecting (FETI) developed by Farhat and Roux for the solution of static problems. Serial and parallel performance results on the CRAY Y-MP/8 and the iPSC-860/128 systems are reported and analyzed for realistic structural dynamics problems. These results establish the superiority of the FETI method over both the serial/parallel conjugate gradient algorithm with diagonal scaling and the serial/parallel direct method, and contrast the computational power of the iPSC-860/128 parallel processor with that of the CRAY Y-MP/8 system.

Cloud-based large-scale air traffic flow optimization

NASA Astrophysics Data System (ADS)

Cao, Yi

The ever-increasing traffic demand makes the efficient use of airspace an imperative mission, and this paper presents an effort in response to this call. Firstly, a new aggregate model, called Link Transmission Model (LTM), is proposed, which models the nationwide traffic as a network of flight routes identified by origin-destination pairs. The traversal time of a flight route is assumed to be the mode of distribution of historical flight records, and the mode is estimated by using Kernel Density Estimation. As this simplification abstracts away physical trajectory details, the complexity of modeling is drastically decreased, resulting in efficient traffic forecasting. The predicative capability of LTM is validated against recorded traffic data. Secondly, a nationwide traffic flow optimization problem with airport and en route capacity constraints is formulated based on LTM. The optimization problem aims at alleviating traffic congestions with minimal global delays. This problem is intractable due to millions of variables. A dual decomposition method is applied to decompose the large-scale problem such that the subproblems are solvable. However, the whole problem is still computational expensive to solve since each subproblem is an smaller integer programming problem that pursues integer solutions. Solving an integer programing problem is known to be far more time-consuming than solving its linear relaxation. In addition, sequential execution on a standalone computer leads to linear runtime increase when the problem size increases. To address the computational efficiency problem, a parallel computing framework is designed which accommodates concurrent executions via multithreading programming. The multithreaded version is compared with its monolithic version to show decreased runtime. Finally, an open-source cloud computing framework, Hadoop MapReduce, is employed for better scalability and reliability. This framework is an "off-the-shelf" parallel computing model that can be used for both offline historical traffic data analysis and online traffic flow optimization. It provides an efficient and robust platform for easy deployment and implementation. A small cloud consisting of five workstations was configured and used to demonstrate the advantages of cloud computing in dealing with large-scale parallelizable traffic problems.

Robust optical flow using adaptive Lorentzian filter for image reconstruction under noisy condition

NASA Astrophysics Data System (ADS)

Kesrarat, Darun; Patanavijit, Vorapoj

2017-02-01

In optical flow for motion allocation, the efficient result in Motion Vector (MV) is an important issue. Several noisy conditions may cause the unreliable result in optical flow algorithms. We discover that many classical optical flows algorithms perform better result under noisy condition when combined with modern optimized model. This paper introduces effective robust models of optical flow by using Robust high reliability spatial based optical flow algorithms using the adaptive Lorentzian norm influence function in computation on simple spatial temporal optical flows algorithm. Experiment on our proposed models confirm better noise tolerance in optical flow's MV under noisy condition when they are applied over simple spatial temporal optical flow algorithms as a filtering model in simple frame-to-frame correlation technique. We illustrate the performance of our models by performing an experiment on several typical sequences with differences in movement speed of foreground and background where the experiment sequences are contaminated by the additive white Gaussian noise (AWGN) at different noise decibels (dB). This paper shows very high effectiveness of noise tolerance models that they are indicated by peak signal to noise ratio (PSNR).

Noise tolerant spatiotemporal chaos computing.

PubMed

Kia, Behnam; Kia, Sarvenaz; Lindner, John F; Sinha, Sudeshna; Ditto, William L

2014-12-01

We introduce and design a noise tolerant chaos computing system based on a coupled map lattice (CML) and the noise reduction capabilities inherent in coupled dynamical systems. The resulting spatiotemporal chaos computing system is more robust to noise than a single map chaos computing system. In this CML based approach to computing, under the coupled dynamics, the local noise from different nodes of the lattice diffuses across the lattice, and it attenuates each other's effects, resulting in a system with less noise content and a more robust chaos computing architecture.

Noise tolerant spatiotemporal chaos computing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kia, Behnam; Kia, Sarvenaz; Ditto, William L.

We introduce and design a noise tolerant chaos computing system based on a coupled map lattice (CML) and the noise reduction capabilities inherent in coupled dynamical systems. The resulting spatiotemporal chaos computing system is more robust to noise than a single map chaos computing system. In this CML based approach to computing, under the coupled dynamics, the local noise from different nodes of the lattice diffuses across the lattice, and it attenuates each other's effects, resulting in a system with less noise content and a more robust chaos computing architecture.

The new and computationally efficient MIL-SOM algorithm: potential benefits for visualization and analysis of a large-scale high-dimensional clinically acquired geographic data.

PubMed

Oyana, Tonny J; Achenie, Luke E K; Heo, Joon

2012-01-01

The objective of this paper is to introduce an efficient algorithm, namely, the mathematically improved learning-self organizing map (MIL-SOM) algorithm, which speeds up the self-organizing map (SOM) training process. In the proposed MIL-SOM algorithm, the weights of Kohonen's SOM are based on the proportional-integral-derivative (PID) controller. Thus, in a typical SOM learning setting, this improvement translates to faster convergence. The basic idea is primarily motivated by the urgent need to develop algorithms with the competence to converge faster and more efficiently than conventional techniques. The MIL-SOM algorithm is tested on four training geographic datasets representing biomedical and disease informatics application domains. Experimental results show that the MIL-SOM algorithm provides a competitive, better updating procedure and performance, good robustness, and it runs faster than Kohonen's SOM.

Genetic algorithms in adaptive fuzzy control

NASA Technical Reports Server (NTRS)

Karr, C. Lucas; Harper, Tony R.

1992-01-01

Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust fuzzy membership functions in response to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific computer-simulated chemical system is used to demonstrate the ideas presented.

The New and Computationally Efficient MIL-SOM Algorithm: Potential Benefits for Visualization and Analysis of a Large-Scale High-Dimensional Clinically Acquired Geographic Data

PubMed Central

Oyana, Tonny J.; Achenie, Luke E. K.; Heo, Joon

2012-01-01

The objective of this paper is to introduce an efficient algorithm, namely, the mathematically improved learning-self organizing map (MIL-SOM) algorithm, which speeds up the self-organizing map (SOM) training process. In the proposed MIL-SOM algorithm, the weights of Kohonen's SOM are based on the proportional-integral-derivative (PID) controller. Thus, in a typical SOM learning setting, this improvement translates to faster convergence. The basic idea is primarily motivated by the urgent need to develop algorithms with the competence to converge faster and more efficiently than conventional techniques. The MIL-SOM algorithm is tested on four training geographic datasets representing biomedical and disease informatics application domains. Experimental results show that the MIL-SOM algorithm provides a competitive, better updating procedure and performance, good robustness, and it runs faster than Kohonen's SOM. PMID:22481977

Computer-operated analytical platform for the determination of nutrients in hydroponic systems.

PubMed

Rius-Ruiz, F Xavier; Andrade, Francisco J; Riu, Jordi; Rius, F Xavier

2014-03-15

Hydroponics is a water, energy, space, and cost efficient system for growing plants in constrained spaces or land exhausted areas. Precise control of hydroponic nutrients is essential for growing healthy plants and producing high yields. In this article we report for the first time on a new computer-operated analytical platform which can be readily used for the determination of essential nutrients in hydroponic growing systems. The liquid-handling system uses inexpensive components (i.e., peristaltic pump and solenoid valves), which are discretely computer-operated to automatically condition, calibrate and clean a multi-probe of solid-contact ion-selective electrodes (ISEs). These ISEs, which are based on carbon nanotubes, offer high portability, robustness and easy maintenance and storage. With this new computer-operated analytical platform we performed automatic measurements of K(+), Ca(2+), NO3(-) and Cl(-) during tomato plants growth in order to assure optimal nutritional uptake and tomato production. Copyright © 2013 Elsevier Ltd. All rights reserved.

CAGI: Computer Aided Grid Interface. A work in progress

NASA Technical Reports Server (NTRS)

Soni, Bharat K.; Yu, Tzu-Yi; Vaughn, David

1992-01-01

Progress realized in the development of a Computer Aided Grid Interface (CAGI) software system in integrating CAD/CAM geometric system output and/or Interactive Graphics Exchange Standard (IGES) files, geometry manipulations associated with grid generation, and robust grid generation methodologies is presented. CAGI is being developed in a modular fashion and will offer fast, efficient and economical response to geometry/grid preparation, allowing the ability to upgrade basic geometry in a step-by-step fashion interactively and under permanent visual control along with minimizing the differences between the actual hardware surface descriptions and corresponding numerical analog. The computer code GENIE is used as a basis. The Non-Uniform Rational B-Splines (NURBS) representation of sculptured surfaces is utilized for surface grid redistribution. The computer aided analysis system, PATRAN, is adapted as a CAD/CAM system. The progress realized in NURBS surface grid generation, the development of IGES transformer, and geometry adaption using PATRAN will be presented along with their applicability to grid generation associated with rocket propulsion applications.

Fast and robust multimodal image registration using a local derivative pattern.

PubMed

Jiang, Dongsheng; Shi, Yonghong; Chen, Xinrong; Wang, Manning; Song, Zhijian

2017-02-01

Deformable multimodal image registration, which can benefit radiotherapy and image guided surgery by providing complementary information, remains a challenging task in the medical image analysis field due to the difficulty of defining a proper similarity measure. This article presents a novel, robust and fast binary descriptor, the discriminative local derivative pattern (dLDP), which is able to encode images of different modalities into similar image representations. dLDP calculates a binary string for each voxel according to the pattern of intensity derivatives in its neighborhood. The descriptor similarity is evaluated using the Hamming distance, which can be efficiently computed, instead of conventional L1 or L2 norms. For the first time, we validated the effectiveness and feasibility of the local derivative pattern for multimodal deformable image registration with several multi-modal registration applications. dLDP was compared with three state-of-the-art methods in artificial image and clinical settings. In the experiments of deformable registration between different magnetic resonance imaging (MRI) modalities from BrainWeb, between computed tomography and MRI images from patient data, and between MRI and ultrasound images from BITE database, we show our method outperforms localized mutual information and entropy images in terms of both accuracy and time efficiency. We have further validated dLDP for the deformable registration of preoperative MRI and three-dimensional intraoperative ultrasound images. Our results indicate that dLDP reduces the average mean target registration error from 4.12 mm to 2.30 mm. This accuracy is statistically equivalent to the accuracy of the state-of-the-art methods in the study; however, in terms of computational complexity, our method significantly outperforms other methods and is even comparable to the sum of the absolute difference. The results reveal that dLDP can achieve superior performance regarding both accuracy and time efficiency in general multimodal image registration. In addition, dLDP also indicates the potential for clinical ultrasound guided intervention. © 2016 The Authors. Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

3D robust Chan-Vese model for industrial computed tomography volume data segmentation

NASA Astrophysics Data System (ADS)

Liu, Linghui; Zeng, Li; Luan, Xiao

2013-11-01

Industrial computed tomography (CT) has been widely applied in many areas of non-destructive testing (NDT) and non-destructive evaluation (NDE). In practice, CT volume data to be dealt with may be corrupted by noise. This paper addresses the segmentation of noisy industrial CT volume data. Motivated by the research on the Chan-Vese (CV) model, we present a region-based active contour model that draws upon intensity information in local regions with a controllable scale. In the presence of noise, a local energy is firstly defined according to the intensity difference within a local neighborhood. Then a global energy is defined to integrate local energy with respect to all image points. In a level set formulation, this energy is represented by a variational level set function, where a surface evolution equation is derived for energy minimization. Comparative analysis with the CV model indicates the comparable performance of the 3D robust Chan-Vese (RCV) model. The quantitative evaluation also shows the segmentation accuracy of 3D RCV. In addition, the efficiency of our approach is validated under several types of noise, such as Poisson noise, Gaussian noise, salt-and-pepper noise and speckle noise.

An Automated Pipeline for Engineering Many-Enzyme Pathways: Computational Sequence Design, Pathway Expression-Flux Mapping, and Scalable Pathway Optimization.

PubMed

Halper, Sean M; Cetnar, Daniel P; Salis, Howard M

2018-01-01

Engineering many-enzyme metabolic pathways suffers from the design curse of dimensionality. There are an astronomical number of synonymous DNA sequence choices, though relatively few will express an evolutionary robust, maximally productive pathway without metabolic bottlenecks. To solve this challenge, we have developed an integrated, automated computational-experimental pipeline that identifies a pathway's optimal DNA sequence without high-throughput screening or many cycles of design-build-test. The first step applies our Operon Calculator algorithm to design a host-specific evolutionary robust bacterial operon sequence with maximally tunable enzyme expression levels. The second step applies our RBS Library Calculator algorithm to systematically vary enzyme expression levels with the smallest-sized library. After characterizing a small number of constructed pathway variants, measurements are supplied to our Pathway Map Calculator algorithm, which then parameterizes a kinetic metabolic model that ultimately predicts the pathway's optimal enzyme expression levels and DNA sequences. Altogether, our algorithms provide the ability to efficiently map the pathway's sequence-expression-activity space and predict DNA sequences with desired metabolic fluxes. Here, we provide a step-by-step guide to applying the Pathway Optimization Pipeline on a desired multi-enzyme pathway in a bacterial host.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rajagopal, K. R.; Rao, I. J.

The procedures in place for producing materials in order to optimize their performance with respect to creep characteristics, oxidation resistance, elevation of melting point, thermal and electrical conductivity and other thermal and electrical properties are essentially trial and error experimentation that tend to be tremendously time consuming and expensive. A computational approach has been developed that can replace the trial and error procedures in order that one can efficiently design and engineer materials based on the application in question can lead to enhanced performance of the material, significant decrease in costs and cut down the time necessary to produce suchmore » materials. The work has relevance to the design and manufacture of turbine blades operating at high operating temperature, development of armor and missiles heads; corrosion resistant tanks and containers, better conductors of electricity, and the numerous other applications that are envisaged for specially structured nanocrystalline solids. A robust thermodynamic framework is developed within which the computational approach is developed. The procedure takes into account microstructural features such as the dislocation density, lattice mismatch, stacking faults, volume fractions of inclusions, interfacial area, etc. A robust model for single crystal superalloys that takes into account the microstructure of the alloy within the context of a continuum model is developed. Having developed the model, we then implement in a computational scheme using the software ABAQUS/STANDARD. The results of the simulation are compared against experimental data in realistic geometries.« less

Numerical calculation of thermo-mechanical problems at large strains based on complex step derivative approximation of tangent stiffness matrices

NASA Astrophysics Data System (ADS)

Balzani, Daniel; Gandhi, Ashutosh; Tanaka, Masato; Schröder, Jörg

2015-05-01

In this paper a robust approximation scheme for the numerical calculation of tangent stiffness matrices is presented in the context of nonlinear thermo-mechanical finite element problems and its performance is analyzed. The scheme extends the approach proposed in Kim et al. (Comput Methods Appl Mech Eng 200:403-413, 2011) and Tanaka et al. (Comput Methods Appl Mech Eng 269:454-470, 2014 and bases on applying the complex-step-derivative approximation to the linearizations of the weak forms of the balance of linear momentum and the balance of energy. By incorporating consistent perturbations along the imaginary axis to the displacement as well as thermal degrees of freedom, we demonstrate that numerical tangent stiffness matrices can be obtained with accuracy up to computer precision leading to quadratically converging schemes. The main advantage of this approach is that contrary to the classical forward difference scheme no round-off errors due to floating-point arithmetics exist within the calculation of the tangent stiffness. This enables arbitrarily small perturbation values and therefore leads to robust schemes even when choosing small values. An efficient algorithmic treatment is presented which enables a straightforward implementation of the method in any standard finite-element program. By means of thermo-elastic and thermo-elastoplastic boundary value problems at finite strains the performance of the proposed approach is analyzed.

Transistor analogs of emergent iono-neuronal dynamics.

PubMed

Rachmuth, Guy; Poon, Chi-Sang

2008-06-01

Neuromorphic analog metal-oxide-silicon (MOS) transistor circuits promise compact, low-power, and high-speed emulations of iono-neuronal dynamics orders-of-magnitude faster than digital simulation. However, their inherently limited input voltage dynamic range vs power consumption and silicon die area tradeoffs makes them highly sensitive to transistor mismatch due to fabrication inaccuracy, device noise, and other nonidealities. This limitation precludes robust analog very-large-scale-integration (aVLSI) circuits implementation of emergent iono-neuronal dynamics computations beyond simple spiking with limited ion channel dynamics. Here we present versatile neuromorphic analog building-block circuits that afford near-maximum voltage dynamic range operating within the low-power MOS transistor weak-inversion regime which is ideal for aVLSI implementation or implantable biomimetic device applications. The fabricated microchip allowed robust realization of dynamic iono-neuronal computations such as coincidence detection of presynaptic spikes or pre- and postsynaptic activities. As a critical performance benchmark, the high-speed and highly interactive iono-neuronal simulation capability on-chip enabled our prompt discovery of a minimal model of chaotic pacemaker bursting, an emergent iono-neuronal behavior of fundamental biological significance which has hitherto defied experimental testing or computational exploration via conventional digital or analog simulations. These compact and power-efficient transistor analogs of emergent iono-neuronal dynamics open new avenues for next-generation neuromorphic, neuroprosthetic, and brain-machine interface applications.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Jianfei; Wang, Shijun; Turkbey, Evrim B.

Purpose: Renal calculi are common extracolonic incidental findings on computed tomographic colonography (CTC). This work aims to develop a fully automated computer-aided diagnosis system to accurately detect renal calculi on CTC images. Methods: The authors developed a total variation (TV) flow method to reduce image noise within the kidneys while maintaining the characteristic appearance of renal calculi. Maximally stable extremal region (MSER) features were then calculated to robustly identify calculi candidates. Finally, the authors computed texture and shape features that were imported to support vector machines for calculus classification. The method was validated on a dataset of 192 patients andmore » compared to a baseline approach that detects calculi by thresholding. The authors also compared their method with the detection approaches using anisotropic diffusion and nonsmoothing. Results: At a false positive rate of 8 per patient, the sensitivities of the new method and the baseline thresholding approach were 69% and 35% (p < 1e − 3) on all calculi from 1 to 433 mm{sup 3} in the testing dataset. The sensitivities of the detection methods using anisotropic diffusion and nonsmoothing were 36% and 0%, respectively. The sensitivity of the new method increased to 90% if only larger and more clinically relevant calculi were considered. Conclusions: Experimental results demonstrated that TV-flow and MSER features are efficient means to robustly and accurately detect renal calculi on low-dose, high noise CTC images. Thus, the proposed method can potentially improve diagnosis.« less

Noise in Neuronal and Electronic Circuits: A General Modeling Framework and Non-Monte Carlo Simulation Techniques.

PubMed

Kilinc, Deniz; Demir, Alper

2017-08-01

The brain is extremely energy efficient and remarkably robust in what it does despite the considerable variability and noise caused by the stochastic mechanisms in neurons and synapses. Computational modeling is a powerful tool that can help us gain insight into this important aspect of brain mechanism. A deep understanding and computational design tools can help develop robust neuromorphic electronic circuits and hybrid neuroelectronic systems. In this paper, we present a general modeling framework for biological neuronal circuits that systematically captures the nonstationary stochastic behavior of ion channels and synaptic processes. In this framework, fine-grained, discrete-state, continuous-time Markov chain models of both ion channels and synaptic processes are treated in a unified manner. Our modeling framework features a mechanism for the automatic generation of the corresponding coarse-grained, continuous-state, continuous-time stochastic differential equation models for neuronal variability and noise. Furthermore, we repurpose non-Monte Carlo noise analysis techniques, which were previously developed for analog electronic circuits, for the stochastic characterization of neuronal circuits both in time and frequency domain. We verify that the fast non-Monte Carlo analysis methods produce results with the same accuracy as computationally expensive Monte Carlo simulations. We have implemented the proposed techniques in a prototype simulator, where both biological neuronal and analog electronic circuits can be simulated together in a coupled manner.

An efficient fully-implicit multislope MUSCL method for multiphase flow with gravity in discrete fractured media

NASA Astrophysics Data System (ADS)

Jiang, Jiamin; Younis, Rami M.

2017-06-01

The first-order methods commonly employed in reservoir simulation for computing the convective fluxes introduce excessive numerical diffusion leading to severe smoothing of displacement fronts. We present a fully-implicit cell-centered finite-volume (CCFV) framework that can achieve second-order spatial accuracy on smooth solutions, while at the same time maintain robustness and nonlinear convergence performance. A novel multislope MUSCL method is proposed to construct the required values at edge centroids in a straightforward and effective way by taking advantage of the triangular mesh geometry. In contrast to the monoslope methods in which a unique limited gradient is used, the multislope concept constructs specific scalar slopes for the interpolations on each edge of a given element. Through the edge centroids, the numerical diffusion caused by mesh skewness is reduced, and optimal second order accuracy can be achieved. Moreover, an improved smooth flux-limiter is introduced to ensure monotonicity on non-uniform meshes. The flux-limiter provides high accuracy without degrading nonlinear convergence performance. The CCFV framework is adapted to accommodate a lower-dimensional discrete fracture-matrix (DFM) model. Several numerical tests with discrete fractured system are carried out to demonstrate the efficiency and robustness of the numerical model.

A multimedia retrieval framework based on semi-supervised ranking and relevance feedback.

PubMed

Yang, Yi; Nie, Feiping; Xu, Dong; Luo, Jiebo; Zhuang, Yueting; Pan, Yunhe

2012-04-01

We present a new framework for multimedia content analysis and retrieval which consists of two independent algorithms. First, we propose a new semi-supervised algorithm called ranking with Local Regression and Global Alignment (LRGA) to learn a robust Laplacian matrix for data ranking. In LRGA, for each data point, a local linear regression model is used to predict the ranking scores of its neighboring points. A unified objective function is then proposed to globally align the local models from all the data points so that an optimal ranking score can be assigned to each data point. Second, we propose a semi-supervised long-term Relevance Feedback (RF) algorithm to refine the multimedia data representation. The proposed long-term RF algorithm utilizes both the multimedia data distribution in multimedia feature space and the history RF information provided by users. A trace ratio optimization problem is then formulated and solved by an efficient algorithm. The algorithms have been applied to several content-based multimedia retrieval applications, including cross-media retrieval, image retrieval, and 3D motion/pose data retrieval. Comprehensive experiments on four data sets have demonstrated its advantages in precision, robustness, scalability, and computational efficiency.

Joint detection and localization of multiple anatomical landmarks through learning

NASA Astrophysics Data System (ADS)

Dikmen, Mert; Zhan, Yiqiang; Zhou, Xiang Sean

2008-03-01

Reliable landmark detection in medical images provides the essential groundwork for successful automation of various open problems such as localization, segmentation, and registration of anatomical structures. In this paper, we present a learning-based system to jointly detect (is it there?) and localize (where?) multiple anatomical landmarks in medical images. The contributions of this work exist in two aspects. First, this method takes the advantage from the learning scenario that is able to automatically extract the most distinctive features for multi-landmark detection. Therefore, it is easily adaptable to detect arbitrary landmarks in various kinds of imaging modalities, e.g., CT, MRI and PET. Second, the use of multi-class/cascaded classifier architecture in different phases of the detection stage combined with robust features that are highly efficient in terms of computation time enables a seemingly real time performance, with very high localization accuracy. This method is validated on CT scans of different body sections, e.g., whole body scans, chest scans and abdominal scans. Aside from improved robustness (due to the exploitation of spatial correlations), it gains a run time efficiency in landmark detection. It also shows good scalability performance under increasing number of landmarks.

Hierarchical extraction of urban objects from mobile laser scanning data

NASA Astrophysics Data System (ADS)

Yang, Bisheng; Dong, Zhen; Zhao, Gang; Dai, Wenxia

2015-01-01

Point clouds collected in urban scenes contain a huge number of points (e.g., billions), numerous objects with significant size variability, complex and incomplete structures, and variable point densities, raising great challenges for the automated extraction of urban objects in the field of photogrammetry, computer vision, and robotics. This paper addresses these challenges by proposing an automated method to extract urban objects robustly and efficiently. The proposed method generates multi-scale supervoxels from 3D point clouds using the point attributes (e.g., colors, intensities) and spatial distances between points, and then segments the supervoxels rather than individual points by combining graph based segmentation with multiple cues (e.g., principal direction, colors) of the supervoxels. The proposed method defines a set of rules for merging segments into meaningful units according to types of urban objects and forms the semantic knowledge of urban objects for the classification of objects. Finally, the proposed method extracts and classifies urban objects in a hierarchical order ranked by the saliency of the segments. Experiments show that the proposed method is efficient and robust for extracting buildings, streetlamps, trees, telegraph poles, traffic signs, cars, and enclosures from mobile laser scanning (MLS) point clouds, with an overall accuracy of 92.3%.

Visual Tracking Based on Extreme Learning Machine and Sparse Representation

PubMed Central

Wang, Baoxian; Tang, Linbo; Yang, Jinglin; Zhao, Baojun; Wang, Shuigen

2015-01-01

The existing sparse representation-based visual trackers mostly suffer from both being time consuming and having poor robustness problems. To address these issues, a novel tracking method is presented via combining sparse representation and an emerging learning technique, namely extreme learning machine (ELM). Specifically, visual tracking can be divided into two consecutive processes. Firstly, ELM is utilized to find the optimal separate hyperplane between the target observations and background ones. Thus, the trained ELM classification function is able to remove most of the candidate samples related to background contents efficiently, thereby reducing the total computational cost of the following sparse representation. Secondly, to further combine ELM and sparse representation, the resultant confidence values (i.e., probabilities to be a target) of samples on the ELM classification function are used to construct a new manifold learning constraint term of the sparse representation framework, which tends to achieve robuster results. Moreover, the accelerated proximal gradient method is used for deriving the optimal solution (in matrix form) of the constrained sparse tracking model. Additionally, the matrix form solution allows the candidate samples to be calculated in parallel, thereby leading to a higher efficiency. Experiments demonstrate the effectiveness of the proposed tracker. PMID:26506359

Robust autofocus algorithm for ISAR imaging of moving targets

NASA Astrophysics Data System (ADS)

Li, Jian; Wu, Renbiao; Chen, Victor C.

2000-08-01

A robust autofocus approach, referred to as AUTOCLEAN (AUTOfocus via CLEAN), is proposed for the motion compensation in ISAR (inverse synthetic aperture radar) imaging of moving targets. It is a parametric algorithm based on a very flexible data model which takes into account arbitrary range migration and arbitrary phase errors across the synthetic aperture that may be induced by unwanted radial motion of the target as well as propagation or system instability. AUTOCLEAN can be classified as a multiple scatterer algorithm (MSA), but it differs considerably from other existing MSAs in several aspects: (1) dominant scatterers are selected automatically in the two-dimensional (2-D) image domain; (2) scatterers may not be well-isolated or very dominant; (3) phase and RCS (radar cross section) information from each selected scatterer are combined in an optimal way; (4) the troublesome phase unwrapping step is avoided. AUTOCLEAN is computationally efficient and involves only a sequence of FFTs (fast Fourier Transforms). Another good feature associated with AUTOCLEAN is that its performance can be progressively improved by assuming a larger number of dominant scatterers for the target. Hence it can be easily configured for real-time applications including, for example, ATR (automatic target recognition) of non-cooperative moving targets, and for some other applications where the image quality is of the major concern but not the computational time including, for example, for the development and maintenance of low observable aircrafts. Numerical and experimental results have shown that AUTOCLEAN is a very robust autofocus tool for ISAR imaging.

Efficient robust conditional random fields.

PubMed

Song, Dongjin; Liu, Wei; Zhou, Tianyi; Tao, Dacheng; Meyer, David A

2015-10-01

Conditional random fields (CRFs) are a flexible yet powerful probabilistic approach and have shown advantages for popular applications in various areas, including text analysis, bioinformatics, and computer vision. Traditional CRF models, however, are incapable of selecting relevant features as well as suppressing noise from noisy original features. Moreover, conventional optimization methods often converge slowly in solving the training procedure of CRFs, and will degrade significantly for tasks with a large number of samples and features. In this paper, we propose robust CRFs (RCRFs) to simultaneously select relevant features. An optimal gradient method (OGM) is further designed to train RCRFs efficiently. Specifically, the proposed RCRFs employ the l1 norm of the model parameters to regularize the objective used by traditional CRFs, therefore enabling discovery of the relevant unary features and pairwise features of CRFs. In each iteration of OGM, the gradient direction is determined jointly by the current gradient together with the historical gradients, and the Lipschitz constant is leveraged to specify the proper step size. We show that an OGM can tackle the RCRF model training very efficiently, achieving the optimal convergence rate [Formula: see text] (where k is the number of iterations). This convergence rate is theoretically superior to the convergence rate O(1/k) of previous first-order optimization methods. Extensive experiments performed on three practical image segmentation tasks demonstrate the efficacy of OGM in training our proposed RCRFs.

Exploring Valleys without Climbing Every Peak: More Efficient and Forgiving Metabasin Metadynamics via Robust On-the-Fly Bias Domain Restriction

DOE PAGES

Dama, James F.; Hocky, Glen M.; Sun, Rui; ...

2015-11-03

Metadynamics is an enhanced sampling method designed to flatten free energy surfaces uniformly. However, the highest-energy regions are often irrelevant to study and dangerous to explore because systems often respond irreversibly in unforeseen ways in response to driving forces in these regions, spoiling the sampling. Introducing an on-the-fly domain restriction allows metadynamics to flatten only up to a specified energy level and no further, improving efficiency and safety while decreasing the pressure on practitioners to design collective variables that are robust to otherwise irrelevant high energy driving. Here this paper describes a new method that achieves this using sequential on-the-flymore » estimation of energy wells and redefinition of the metadynamics hill shape, termed metabasin metadynamics. The energy level may be defined a priori or relative to unknown barrier energies estimated on the fly. Altering only the hill ensures that the method is compatible with many other advances in metadynamics methodology. The hill shape has a natural interpretation in terms of multiscale dynamics and the computational overhead in simulation is minimal when studying systems of any reasonable size, for instance proteins or other macromolecules. Ultimately, three example applications show that the formula is accurate and robust to complex dynamics, making metadynamics significantly more forgiving with respect to CV quality and thus more feasible to apply to the most challenging biomolecular systems.« less

Improved initial guess with semi-subpixel level accuracy in digital image correlation by feature-based method

NASA Astrophysics Data System (ADS)

Zhang, Yunlu; Yan, Lei; Liou, Frank

2018-05-01

The quality initial guess of deformation parameters in digital image correlation (DIC) has a serious impact on convergence, robustness, and efficiency of the following subpixel level searching stage. In this work, an improved feature-based initial guess (FB-IG) scheme is presented to provide initial guess for points of interest (POIs) inside a large region. Oriented FAST and Rotated BRIEF (ORB) features are semi-uniformly extracted from the region of interest (ROI) and matched to provide initial deformation information. False matched pairs are eliminated by the novel feature guided Gaussian mixture model (FG-GMM) point set registration algorithm, and nonuniform deformation parameters of the versatile reproducing kernel Hilbert space (RKHS) function are calculated simultaneously. Validations on simulated images and real-world mini tensile test verify that this scheme can robustly and accurately compute initial guesses with semi-subpixel level accuracy in cases with small or large translation, deformation, or rotation.

Synthesis and Control of Flexible Systems with Component-Level Uncertainties

NASA Technical Reports Server (NTRS)

Maghami, Peiman G.; Lim, Kyong B.

2009-01-01

An efficient and computationally robust method for synthesis of component dynamics is developed. The method defines the interface forces/moments as feasible vectors in transformed coordinates to ensure that connectivity requirements of the combined structure are met. The synthesized system is then defined in a transformed set of feasible coordinates. The simplicity of form is exploited to effectively deal with modeling parametric and non-parametric uncertainties at the substructure level. Uncertainty models of reasonable size and complexity are synthesized for the combined structure from those in the substructure models. In particular, we address frequency and damping uncertainties at the component level. The approach first considers the robustness of synthesized flexible systems. It is then extended to deal with non-synthesized dynamic models with component-level uncertainties by projecting uncertainties to the system level. A numerical example is given to demonstrate the feasibility of the proposed approach.

Inverse transport calculations in optical imaging with subspace optimization algorithms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ding, Tian, E-mail: tding@math.utexas.edu; Ren, Kui, E-mail: ren@math.utexas.edu

2014-09-15

Inverse boundary value problems for the radiative transport equation play an important role in optics-based medical imaging techniques such as diffuse optical tomography (DOT) and fluorescence optical tomography (FOT). Despite the rapid progress in the mathematical theory and numerical computation of these inverse problems in recent years, developing robust and efficient reconstruction algorithms remains a challenging task and an active research topic. We propose here a robust reconstruction method that is based on subspace minimization techniques. The method splits the unknown transport solution (or a functional of it) into low-frequency and high-frequency components, and uses singular value decomposition to analyticallymore » recover part of low-frequency information. Minimization is then applied to recover part of the high-frequency components of the unknowns. We present some numerical simulations with synthetic data to demonstrate the performance of the proposed algorithm.« less

Robust distant-entanglement generation using coherent multiphoton scattering

NASA Astrophysics Data System (ADS)

Chan, Ching-Kit; Sham, L. J.

2013-03-01

The generation and controllability of entanglement between distant quantum states have been the heart of quantum computation and quantum information processing. Existing schemes for solid state qubit entanglement are based on the single-photon spectroscopy that has the merit of a high fidelity entanglement creation, but with a very limited efficiency. This severely restricts the scalability for a qubit network system. Here, we describe a new distant entanglement protocol using coherent multiphoton scattering. The scheme makes use of the postselection of large and distinguishable photon signals, and has both a high success probability and a high entanglement fidelity. Our result shows that the entanglement generation is robust against photon fluctuations, and has an average entanglement duration within the decoherence time in various qubit systems, based on existing experimental parameters. This research was supported by the U.S. Army Research Office MURI award W911NF0910406 and by NSF grant PHY-1104446.

A robust anonymous biometric-based authenticated key agreement scheme for multi-server environments

PubMed Central

Huang, Yuanfei; Ma, Fangchao

2017-01-01

In order to improve the security in remote authentication systems, numerous biometric-based authentication schemes using smart cards have been proposed. Recently, Moon et al. presented an authentication scheme to remedy the flaws of Lu et al.’s scheme, and claimed that their improved protocol supports the required security properties. Unfortunately, we found that Moon et al.’s scheme still has weaknesses. In this paper, we show that Moon et al.’s scheme is vulnerable to insider attack, server spoofing attack, user impersonation attack and guessing attack. Furthermore, we propose a robust anonymous multi-server authentication scheme using public key encryption to remove the aforementioned problems. From the subsequent formal and informal security analysis, we demonstrate that our proposed scheme provides strong mutual authentication and satisfies the desirable security requirements. The functional and performance analysis shows that the improved scheme has the best secure functionality and is computational efficient. PMID:29121050

A robust anonymous biometric-based authenticated key agreement scheme for multi-server environments.

PubMed

Guo, Hua; Wang, Pei; Zhang, Xiyong; Huang, Yuanfei; Ma, Fangchao

2017-01-01

In order to improve the security in remote authentication systems, numerous biometric-based authentication schemes using smart cards have been proposed. Recently, Moon et al. presented an authentication scheme to remedy the flaws of Lu et al.'s scheme, and claimed that their improved protocol supports the required security properties. Unfortunately, we found that Moon et al.'s scheme still has weaknesses. In this paper, we show that Moon et al.'s scheme is vulnerable to insider attack, server spoofing attack, user impersonation attack and guessing attack. Furthermore, we propose a robust anonymous multi-server authentication scheme using public key encryption to remove the aforementioned problems. From the subsequent formal and informal security analysis, we demonstrate that our proposed scheme provides strong mutual authentication and satisfies the desirable security requirements. The functional and performance analysis shows that the improved scheme has the best secure functionality and is computational efficient.

Re-identification of persons in multi-camera surveillance under varying viewpoints and illumination

NASA Astrophysics Data System (ADS)

Bouma, Henri; Borsboom, Sander; den Hollander, Richard J. M.; Landsmeer, Sander H.; Worring, Marcel

2012-06-01

The capability to track individuals in CCTV cameras is important for surveillance and forensics alike. However, it is laborious to do over multiple cameras. Therefore, an automated system is desirable. In literature several methods have been proposed, but their robustness against varying viewpoints and illumination is limited. Hence performance in realistic settings is also limited. In this paper, we present a novel method for the automatic re-identification of persons in video from surveillance cameras in a realistic setting. The method is computationally efficient, robust to a wide variety of viewpoints and illumination, simple to implement and it requires no training. We compare the performance of our method to several state-of-the-art methods on a publically available dataset that contains the variety of viewpoints and illumination to allow benchmarking. The results indicate that our method shows good performance and enables a human operator to track persons five times faster.

Robust averaging protects decisions from noise in neural computations

PubMed Central

Herce Castañón, Santiago; Solomon, Joshua A.; Vandormael, Hildward

2017-01-01

An ideal observer will give equivalent weight to sources of information that are equally reliable. However, when averaging visual information, human observers tend to downweight or discount features that are relatively outlying or deviant (‘robust averaging’). Why humans adopt an integration policy that discards important decision information remains unknown. Here, observers were asked to judge the average tilt in a circular array of high-contrast gratings, relative to an orientation boundary defined by a central reference grating. Observers showed robust averaging of orientation, but the extent to which they did so was a positive predictor of their overall performance. Using computational simulations, we show that although robust averaging is suboptimal for a perfect integrator, it paradoxically enhances performance in the presence of “late” noise, i.e. which corrupts decisions during integration. In other words, robust decision strategies increase the brain’s resilience to noise arising in neural computations during decision-making. PMID:28841644

Multi-scale graph-cut algorithm for efficient water-fat separation.

PubMed

Berglund, Johan; Skorpil, Mikael

2017-09-01

To improve the accuracy and robustness to noise in water-fat separation by unifying the multiscale and graph cut based approaches to B 0 -correction. A previously proposed water-fat separation algorithm that corrects for B 0 field inhomogeneity in 3D by a single quadratic pseudo-Boolean optimization (QPBO) graph cut was incorporated into a multi-scale framework, where field map solutions are propagated from coarse to fine scales for voxels that are not resolved by the graph cut. The accuracy of the single-scale and multi-scale QPBO algorithms was evaluated against benchmark reference datasets. The robustness to noise was evaluated by adding noise to the input data prior to water-fat separation. Both algorithms achieved the highest accuracy when compared with seven previously published methods, while computation times were acceptable for implementation in clinical routine. The multi-scale algorithm was more robust to noise than the single-scale algorithm, while causing only a small increase (+10%) of the reconstruction time. The proposed 3D multi-scale QPBO algorithm offers accurate water-fat separation, robustness to noise, and fast reconstruction. The software implementation is freely available to the research community. Magn Reson Med 78:941-949, 2017. © 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Preprocessing of gene expression data by optimally robust estimators

PubMed Central

2010-01-01

Background The preprocessing of gene expression data obtained from several platforms routinely includes the aggregation of multiple raw signal intensities to one expression value. Examples are the computation of a single expression measure based on the perfect match (PM) and mismatch (MM) probes for the Affymetrix technology, the summarization of bead level values to bead summary values for the Illumina technology or the aggregation of replicated measurements in the case of other technologies including real-time quantitative polymerase chain reaction (RT-qPCR) platforms. The summarization of technical replicates is also performed in other "-omics" disciplines like proteomics or metabolomics. Preprocessing methods like MAS 5.0, Illumina's default summarization method, RMA, or VSN show that the use of robust estimators is widely accepted in gene expression analysis. However, the selection of robust methods seems to be mainly driven by their high breakdown point and not by efficiency. Results We describe how optimally robust radius-minimax (rmx) estimators, i.e. estimators that minimize an asymptotic maximum risk on shrinking neighborhoods about an ideal model, can be used for the aggregation of multiple raw signal intensities to one expression value for Affymetrix and Illumina data. With regard to the Affymetrix data, we have implemented an algorithm which is a variant of MAS 5.0. Using datasets from the literature and Monte-Carlo simulations we provide some reasoning for assuming approximate log-normal distributions of the raw signal intensities by means of the Kolmogorov distance, at least for the discussed datasets, and compare the results of our preprocessing algorithms with the results of Affymetrix's MAS 5.0 and Illumina's default method. The numerical results indicate that when using rmx estimators an accuracy improvement of about 10-20% is obtained compared to Affymetrix's MAS 5.0 and about 1-5% compared to Illumina's default method. The improvement is also visible in the analysis of technical replicates where the reproducibility of the values (in terms of Pearson and Spearman correlation) is increased for all Affymetrix and almost all Illumina examples considered. Our algorithms are implemented in the R package named RobLoxBioC which is publicly available via CRAN, The Comprehensive R Archive Network (http://cran.r-project.org/web/packages/RobLoxBioC/). Conclusions Optimally robust rmx estimators have a high breakdown point and are computationally feasible. They can lead to a considerable gain in efficiency for well-established bioinformatics procedures and thus, can increase the reproducibility and power of subsequent statistical analysis. PMID:21118506

A practical approach to superresolution

NASA Astrophysics Data System (ADS)

Farsiu, Sina; Elad, Michael; Milanfar, Peyman

2006-01-01

Theoretical and practical limitations usually constrain the achievable resolution of any imaging device. Super-Resolution (SR) methods are developed through the years to go beyond this limit by acquiring and fusing several low-resolution (LR) images of the same scene, producing a high-resolution (HR) image. The early works on SR, although occasionally mathematically optimal for particular models of data and noise, produced poor results when applied to real images. In this paper, we discuss two of the main issues related to designing a practical SR system, namely reconstruction accuracy and computational efficiency. Reconstruction accuracy refers to the problem of designing a robust SR method applicable to images from different imaging systems. We study a general framework for optimal reconstruction of images from grayscale, color, or color filtered (CFA) cameras. The performance of our proposed method is boosted by using powerful priors and is robust to both measurement (e.g. CCD read out noise) and system noise (e.g. motion estimation error). Noting that the motion estimation is often considered a bottleneck in terms of SR performance, we introduce the concept of "constrained motions" for enhancing the quality of super-resolved images. We show that using such constraints will enhance the quality of the motion estimation and therefore results in more accurate reconstruction of the HR images. We also justify some practical assumptions that greatly reduce the computational complexity and memory requirements of the proposed methods. We use efficient approximation of the Kalman Filter (KF) and adopt a dynamic point of view to the SR problem. Novel methods for addressing these issues are accompanied by experimental results on real data.

Multidisciplinary Design Optimization of a Full Vehicle with High Performance Computing

NASA Technical Reports Server (NTRS)

Yang, R. J.; Gu, L.; Tho, C. H.; Sobieszczanski-Sobieski, Jaroslaw

2001-01-01

Multidisciplinary design optimization (MDO) of a full vehicle under the constraints of crashworthiness, NVH (Noise, Vibration and Harshness), durability, and other performance attributes is one of the imperative goals for automotive industry. However, it is often infeasible due to the lack of computational resources, robust simulation capabilities, and efficient optimization methodologies. This paper intends to move closer towards that goal by using parallel computers for the intensive computation and combining different approximations for dissimilar analyses in the MDO process. The MDO process presented in this paper is an extension of the previous work reported by Sobieski et al. In addition to the roof crush, two full vehicle crash modes are added: full frontal impact and 50% frontal offset crash. Instead of using an adaptive polynomial response surface method, this paper employs a DOE/RSM method for exploring the design space and constructing highly nonlinear crash functions. Two NMO strategies are used and results are compared. This paper demonstrates that with high performance computing, a conventionally intractable real world full vehicle multidisciplinary optimization problem considering all performance attributes with large number of design variables become feasible.

Marginal Structural Models with Counterfactual Effect Modifiers.

PubMed

Zheng, Wenjing; Luo, Zhehui; van der Laan, Mark J

2018-06-08

In health and social sciences, research questions often involve systematic assessment of the modification of treatment causal effect by patient characteristics. In longitudinal settings, time-varying or post-intervention effect modifiers are also of interest. In this work, we investigate the robust and efficient estimation of the Counterfactual-History-Adjusted Marginal Structural Model (van der Laan MJ, Petersen M. Statistical learning of origin-specific statically optimal individualized treatment rules. Int J Biostat. 2007;3), which models the conditional intervention-specific mean outcome given a counterfactual modifier history in an ideal experiment. We establish the semiparametric efficiency theory for these models, and present a substitution-based, semiparametric efficient and doubly robust estimator using the targeted maximum likelihood estimation methodology (TMLE, e.g. van der Laan MJ, Rubin DB. Targeted maximum likelihood learning. Int J Biostat. 2006;2, van der Laan MJ, Rose S. Targeted learning: causal inference for observational and experimental data, 1st ed. Springer Series in Statistics. Springer, 2011). To facilitate implementation in applications where the effect modifier is high dimensional, our third contribution is a projected influence function (and the corresponding projected TMLE estimator), which retains most of the robustness of its efficient peer and can be easily implemented in applications where the use of the efficient influence function becomes taxing. We compare the projected TMLE estimator with an Inverse Probability of Treatment Weighted estimator (e.g. Robins JM. Marginal structural models. In: Proceedings of the American Statistical Association. Section on Bayesian Statistical Science, 1-10. 1997a, Hernan MA, Brumback B, Robins JM. Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. 2000;11:561-570), and a non-targeted G-computation estimator (Robins JM. A new approach to causal inference in mortality studies with sustained exposure periods - application to control of the healthy worker survivor effect. Math Modell. 1986;7:1393-1512.). The comparative performance of these estimators is assessed in a simulation study. The use of the projected TMLE estimator is illustrated in a secondary data analysis for the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial where effect modifiers are subject to missing at random.

A Computational Framework for Automation of Point Defect Calculations

NASA Astrophysics Data System (ADS)

Goyal, Anuj; Gorai, Prashun; Peng, Haowei; Lany, Stephan; Stevanovic, Vladan; National Renewable Energy Laboratory, Golden, Colorado 80401 Collaboration

A complete and rigorously validated open-source Python framework to automate point defect calculations using density functional theory has been developed. The framework provides an effective and efficient method for defect structure generation, and creation of simple yet customizable workflows to analyze defect calculations. The package provides the capability to compute widely accepted correction schemes to overcome finite-size effects, including (1) potential alignment, (2) image-charge correction, and (3) band filling correction to shallow defects. Using Si, ZnO and In2O3as test examples, we demonstrate the package capabilities and validate the methodology. We believe that a robust automated tool like this will enable the materials by design community to assess the impact of point defects on materials performance. National Renewable Energy Laboratory, Golden, Colorado 80401.

Extreme learning machine for reduced order modeling of turbulent geophysical flows.

PubMed

San, Omer; Maulik, Romit

2018-04-01

We investigate the application of artificial neural networks to stabilize proper orthogonal decomposition-based reduced order models for quasistationary geophysical turbulent flows. An extreme learning machine concept is introduced for computing an eddy-viscosity closure dynamically to incorporate the effects of the truncated modes. We consider a four-gyre wind-driven ocean circulation problem as our prototype setting to assess the performance of the proposed data-driven approach. Our framework provides a significant reduction in computational time and effectively retains the dynamics of the full-order model during the forward simulation period beyond the training data set. Furthermore, we show that the method is robust for larger choices of time steps and can be used as an efficient and reliable tool for long time integration of general circulation models.

Anisotropic Laplace-Beltrami Eigenmaps: Bridging Reeb Graphs and Skeletons*

PubMed Central

Shi, Yonggang; Lai, Rongjie; Krishna, Sheila; Sicotte, Nancy; Dinov, Ivo; Toga, Arthur W.

2010-01-01

In this paper we propose a novel approach of computing skeletons of robust topology for simply connected surfaces with boundary by constructing Reeb graphs from the eigenfunctions of an anisotropic Laplace-Beltrami operator. Our work brings together the idea of Reeb graphs and skeletons by incorporating a flux-based weight function into the Laplace-Beltrami operator. Based on the intrinsic geometry of the surface, the resulting Reeb graph is pose independent and captures the global profile of surface geometry. Our algorithm is very efficient and it only takes several seconds to compute on neuroanatomical structures such as the cingulate gyrus and corpus callosum. In our experiments, we show that the Reeb graphs serve well as an approximate skeleton with consistent topology while following the main body of conventional skeletons quite accurately. PMID:21339850

On-Line Safe Flight Envelope Determination for Impaired Aircraft

NASA Technical Reports Server (NTRS)

Lombaerts, Thomas; Schuet, Stefan; Acosta, Diana; Kaneshige, John

2015-01-01

The design and simulation of an on-line algorithm which estimates the safe maneuvering envelope of aircraft is discussed in this paper. The trim envelope is estimated using probabilistic methods and efficient high-fidelity model based computations of attainable equilibrium sets. From this trim envelope, a robust reachability analysis provides the maneuverability limitations of the aircraft through an optimal control formulation. Both envelope limits are presented to the flight crew on the primary flight display. In the results section, scenarios are considered where this adaptive algorithm is capable of computing online changes to the maneuvering envelope due to impairment. Furthermore, corresponding updates to display features on the primary flight display are provided to potentially inform the flight crew of safety critical envelope alterations caused by the impairment.

Open Reading Frame Phylogenetic Analysis on the Cloud

PubMed Central

2013-01-01

Phylogenetic analysis has become essential in researching the evolutionary relationships between viruses. These relationships are depicted on phylogenetic trees, in which viruses are grouped based on sequence similarity. Viral evolutionary relationships are identified from open reading frames rather than from complete sequences. Recently, cloud computing has become popular for developing internet-based bioinformatics tools. Biocloud is an efficient, scalable, and robust bioinformatics computing service. In this paper, we propose a cloud-based open reading frame phylogenetic analysis service. The proposed service integrates the Hadoop framework, virtualization technology, and phylogenetic analysis methods to provide a high-availability, large-scale bioservice. In a case study, we analyze the phylogenetic relationships among Norovirus. Evolutionary relationships are elucidated by aligning different open reading frame sequences. The proposed platform correctly identifies the evolutionary relationships between members of Norovirus. PMID:23671843

Extreme learning machine for reduced order modeling of turbulent geophysical flows

NASA Astrophysics Data System (ADS)

San, Omer; Maulik, Romit

2018-04-01

We investigate the application of artificial neural networks to stabilize proper orthogonal decomposition-based reduced order models for quasistationary geophysical turbulent flows. An extreme learning machine concept is introduced for computing an eddy-viscosity closure dynamically to incorporate the effects of the truncated modes. We consider a four-gyre wind-driven ocean circulation problem as our prototype setting to assess the performance of the proposed data-driven approach. Our framework provides a significant reduction in computational time and effectively retains the dynamics of the full-order model during the forward simulation period beyond the training data set. Furthermore, we show that the method is robust for larger choices of time steps and can be used as an efficient and reliable tool for long time integration of general circulation models.

Evolutionary Technologies: Fundamentals and Applications to Information/Communication Systems and Manufacturing/Logistics Systems

NASA Astrophysics Data System (ADS)

Gen, Mitsuo; Kawakami, Hiroshi; Tsujimura, Yasuhiro; Handa, Hisashi; Lin, Lin; Okamoto, Azuma

As efficient utilization of computational resources is increasing, evolutionary technology based on the Genetic Algorithm (GA), Genetic Programming (GP), Evolution Strategy (ES) and other Evolutionary Computations (ECs) is making rapid progress, and its social recognition and the need as applied technology are increasing. This is explained by the facts that EC offers higher robustness for knowledge information processing systems, intelligent production and logistics systems, most advanced production scheduling and other various real-world problems compared to the approaches based on conventional theories, and EC ensures flexible applicability and usefulness for any unknown system environment even in a case where accurate mathematical modeling fails in the formulation. In this paper, we provide a comprehensive survey of the current state-of-the-art in the fundamentals and applications of evolutionary technologies.

A Shock-Adaptive Godunov Scheme Based on the Generalised Lagrangian Formulation

NASA Astrophysics Data System (ADS)

Lepage, C. Y.; Hui, W. H.

1995-12-01

Application of the Godunov scheme to the Euler equations of gas dynamics based on the Eulerian formulation of flow smears discontinuities, sliplines especially, over several computational cells, while the accuracy in the smooth flow region is of the order O( h), where h is the cell width. Based on the generalised Lagrangian formulation (GLF) of Hui et al., the Godunov scheme yields superior accuracy. By the use of coordinate streamlines in the GLF, the slipline—itself a streamline—is resolved crisply. Infinite shock resolution is achieved through the splitting of shock-cells. An improved entropy-conservation formulation of the governing equations is also proposed for computations in smooth flow regions. Finally, the use of the GLF substantially simplifies the programming logic resulting in a very robust, accurate, and efficient scheme.

Machine learning bandgaps of double perovskites

PubMed Central

Pilania, G.; Mannodi-Kanakkithodi, A.; Uberuaga, B. P.; Ramprasad, R.; Gubernatis, J. E.; Lookman, T.

2016-01-01

The ability to make rapid and accurate predictions on bandgaps of double perovskites is of much practical interest for a range of applications. While quantum mechanical computations for high-fidelity bandgaps are enormously computation-time intensive and thus impractical in high throughput studies, informatics-based statistical learning approaches can be a promising alternative. Here we demonstrate a systematic feature-engineering approach and a robust learning framework for efficient and accurate predictions of electronic bandgaps of double perovskites. After evaluating a set of more than 1.2 million features, we identify lowest occupied Kohn-Sham levels and elemental electronegativities of the constituent atomic species as the most crucial and relevant predictors. The developed models are validated and tested using the best practices of data science and further analyzed to rationalize their prediction performance. PMID:26783247

An Inviscid Decoupled Method for the Roe FDS Scheme in the Reacting Gas Path of FUN3D

NASA Technical Reports Server (NTRS)

Thompson, Kyle B.; Gnoffo, Peter A.

2016-01-01

An approach is described to decouple the species continuity equations from the mixture continuity, momentum, and total energy equations for the Roe flux difference splitting scheme. This decoupling simplifies the implicit system, so that the flow solver can be made significantly more efficient, with very little penalty on overall scheme robustness. Most importantly, the computational cost of the point implicit relaxation is shown to scale linearly with the number of species for the decoupled system, whereas the fully coupled approach scales quadratically. Also, the decoupled method significantly reduces the cost in wall time and memory in comparison to the fully coupled approach. This work lays the foundation for development of an efficient adjoint solution procedure for high speed reacting flow.

Integral Images: Efficient Algorithms for Their Computation and Storage in Resource-Constrained Embedded Vision Systems

PubMed Central

Ehsan, Shoaib; Clark, Adrian F.; ur Rehman, Naveed; McDonald-Maier, Klaus D.

2015-01-01

The integral image, an intermediate image representation, has found extensive use in multi-scale local feature detection algorithms, such as Speeded-Up Robust Features (SURF), allowing fast computation of rectangular features at constant speed, independent of filter size. For resource-constrained real-time embedded vision systems, computation and storage of integral image presents several design challenges due to strict timing and hardware limitations. Although calculation of the integral image only consists of simple addition operations, the total number of operations is large owing to the generally large size of image data. Recursive equations allow substantial decrease in the number of operations but require calculation in a serial fashion. This paper presents two new hardware algorithms that are based on the decomposition of these recursive equations, allowing calculation of up to four integral image values in a row-parallel way without significantly increasing the number of operations. An efficient design strategy is also proposed for a parallel integral image computation unit to reduce the size of the required internal memory (nearly 35% for common HD video). Addressing the storage problem of integral image in embedded vision systems, the paper presents two algorithms which allow substantial decrease (at least 44.44%) in the memory requirements. Finally, the paper provides a case study that highlights the utility of the proposed architectures in embedded vision systems. PMID:26184211

Methods for Efficiently and Accurately Computing Quantum Mechanical Free Energies for Enzyme Catalysis.

PubMed

Kearns, F L; Hudson, P S; Boresch, S; Woodcock, H L

2016-01-01

Enzyme activity is inherently linked to free energies of transition states, ligand binding, protonation/deprotonation, etc.; these free energies, and thus enzyme function, can be affected by residue mutations, allosterically induced conformational changes, and much more. Therefore, being able to predict free energies associated with enzymatic processes is critical to understanding and predicting their function. Free energy simulation (FES) has historically been a computational challenge as it requires both the accurate description of inter- and intramolecular interactions and adequate sampling of all relevant conformational degrees of freedom. The hybrid quantum mechanical molecular mechanical (QM/MM) framework is the current tool of choice when accurate computations of macromolecular systems are essential. Unfortunately, robust and efficient approaches that employ the high levels of computational theory needed to accurately describe many reactive processes (ie, ab initio, DFT), while also including explicit solvation effects and accounting for extensive conformational sampling are essentially nonexistent. In this chapter, we will give a brief overview of two recently developed methods that mitigate several major challenges associated with QM/MM FES: the QM non-Boltzmann Bennett's acceptance ratio method and the QM nonequilibrium work method. We will also describe usage of these methods to calculate free energies associated with (1) relative properties and (2) along reaction paths, using simple test cases with relevance to enzymes examples. © 2016 Elsevier Inc. All rights reserved.

Adaptive time steps in trajectory surface hopping simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Spörkel, Lasse, E-mail: spoerkel@kofo.mpg.de; Thiel, Walter, E-mail: thiel@kofo.mpg.de

2016-05-21

Trajectory surface hopping (TSH) simulations are often performed in combination with active-space multi-reference configuration interaction (MRCI) treatments. Technical problems may arise in such simulations if active and inactive orbitals strongly mix and switch in some particular regions. We propose to use adaptive time steps when such regions are encountered in TSH simulations. For this purpose, we present a computational protocol that is easy to implement and increases the computational effort only in the critical regions. We test this procedure through TSH simulations of a GFP chromophore model (OHBI) and a light-driven rotary molecular motor (F-NAIBP) on semiempirical MRCI potential energymore » surfaces, by comparing the results from simulations with adaptive time steps to analogous ones with constant time steps. For both test molecules, the number of successful trajectories without technical failures rises significantly, from 53% to 95% for OHBI and from 25% to 96% for F-NAIBP. The computed excited-state lifetime remains essentially the same for OHBI and increases somewhat for F-NAIBP, and there is almost no change in the computed quantum efficiency for internal rotation in F-NAIBP. We recommend the general use of adaptive time steps in TSH simulations with active-space CI methods because this will help to avoid technical problems, increase the overall efficiency and robustness of the simulations, and allow for a more complete sampling.« less

Adaptive time steps in trajectory surface hopping simulations

NASA Astrophysics Data System (ADS)

Spörkel, Lasse; Thiel, Walter

2016-05-01

Trajectory surface hopping (TSH) simulations are often performed in combination with active-space multi-reference configuration interaction (MRCI) treatments. Technical problems may arise in such simulations if active and inactive orbitals strongly mix and switch in some particular regions. We propose to use adaptive time steps when such regions are encountered in TSH simulations. For this purpose, we present a computational protocol that is easy to implement and increases the computational effort only in the critical regions. We test this procedure through TSH simulations of a GFP chromophore model (OHBI) and a light-driven rotary molecular motor (F-NAIBP) on semiempirical MRCI potential energy surfaces, by comparing the results from simulations with adaptive time steps to analogous ones with constant time steps. For both test molecules, the number of successful trajectories without technical failures rises significantly, from 53% to 95% for OHBI and from 25% to 96% for F-NAIBP. The computed excited-state lifetime remains essentially the same for OHBI and increases somewhat for F-NAIBP, and there is almost no change in the computed quantum efficiency for internal rotation in F-NAIBP. We recommend the general use of adaptive time steps in TSH simulations with active-space CI methods because this will help to avoid technical problems, increase the overall efficiency and robustness of the simulations, and allow for a more complete sampling.

Integral Images: Efficient Algorithms for Their Computation and Storage in Resource-Constrained Embedded Vision Systems.

PubMed

Ehsan, Shoaib; Clark, Adrian F; Naveed ur Rehman; McDonald-Maier, Klaus D

2015-07-10

The integral image, an intermediate image representation, has found extensive use in multi-scale local feature detection algorithms, such as Speeded-Up Robust Features (SURF), allowing fast computation of rectangular features at constant speed, independent of filter size. For resource-constrained real-time embedded vision systems, computation and storage of integral image presents several design challenges due to strict timing and hardware limitations. Although calculation of the integral image only consists of simple addition operations, the total number of operations is large owing to the generally large size of image data. Recursive equations allow substantial decrease in the number of operations but require calculation in a serial fashion. This paper presents two new hardware algorithms that are based on the decomposition of these recursive equations, allowing calculation of up to four integral image values in a row-parallel way without significantly increasing the number of operations. An efficient design strategy is also proposed for a parallel integral image computation unit to reduce the size of the required internal memory (nearly 35% for common HD video). Addressing the storage problem of integral image in embedded vision systems, the paper presents two algorithms which allow substantial decrease (at least 44.44%) in the memory requirements. Finally, the paper provides a case study that highlights the utility of the proposed architectures in embedded vision systems.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Versino, Daniele; Bronkhorst, Curt Allan

The computational formulation of a micro-mechanical material model for the dynamic failure of ductile metals is presented in this paper. The statistical nature of porosity initiation is accounted for by introducing an arbitrary probability density function which describes the pores nucleation pressures. Each micropore within the representative volume element is modeled as a thick spherical shell made of plastically incompressible material. The treatment of porosity by a distribution of thick-walled spheres also allows for the inclusion of micro-inertia effects under conditions of shock and dynamic loading. The second order ordinary differential equation governing the microscopic porosity evolution is solved withmore » a robust implicit procedure. A new Chebyshev collocation method is employed to approximate the porosity distribution and remapping is used to optimize memory usage. The adaptive approximation of the porosity distribution leads to a reduction of computational time and memory usage of up to two orders of magnitude. Moreover, the proposed model affords consistent performance: changing the nucleation pressure probability density function and/or the applied strain rate does not reduce accuracy or computational efficiency of the material model. The numerical performance of the model and algorithms presented is tested against three problems for high density tantalum: single void, one-dimensional uniaxial strain, and two-dimensional plate impact. Here, the results using the integration and algorithmic advances suggest a significant improvement in computational efficiency and accuracy over previous treatments for dynamic loading conditions.« less

Democratic population decisions result in robust policy-gradient learning: a parametric study with GPU simulations.

PubMed

Richmond, Paul; Buesing, Lars; Giugliano, Michele; Vasilaki, Eleni

2011-05-04

High performance computing on the Graphics Processing Unit (GPU) is an emerging field driven by the promise of high computational power at a low cost. However, GPU programming is a non-trivial task and moreover architectural limitations raise the question of whether investing effort in this direction may be worthwhile. In this work, we use GPU programming to simulate a two-layer network of Integrate-and-Fire neurons with varying degrees of recurrent connectivity and investigate its ability to learn a simplified navigation task using a policy-gradient learning rule stemming from Reinforcement Learning. The purpose of this paper is twofold. First, we want to support the use of GPUs in the field of Computational Neuroscience. Second, using GPU computing power, we investigate the conditions under which the said architecture and learning rule demonstrate best performance. Our work indicates that networks featuring strong Mexican-Hat-shaped recurrent connections in the top layer, where decision making is governed by the formation of a stable activity bump in the neural population (a "non-democratic" mechanism), achieve mediocre learning results at best. In absence of recurrent connections, where all neurons "vote" independently ("democratic") for a decision via population vector readout, the task is generally learned better and more robustly. Our study would have been extremely difficult on a desktop computer without the use of GPU programming. We present the routines developed for this purpose and show that a speed improvement of 5x up to 42x is provided versus optimised Python code. The higher speed is achieved when we exploit the parallelism of the GPU in the search of learning parameters. This suggests that efficient GPU programming can significantly reduce the time needed for simulating networks of spiking neurons, particularly when multiple parameter configurations are investigated.

Efficient transportation for Vermont : optimal statewide transit networks.

DOT National Transportation Integrated Search

2011-01-01

"Public transit systems are receiving increased attention as viable solutions to problems with : transportation system robustness, energy-efficiency and equity. The over-reliance on a single : mode, the automobile, is a threat to system robustness. I...

Fast Particle Methods for Multiscale Phenomena Simulations

NASA Technical Reports Server (NTRS)

Koumoutsakos, P.; Wray, A.; Shariff, K.; Pohorille, Andrew

2000-01-01

We are developing particle methods oriented at improving computational modeling capabilities of multiscale physical phenomena in : (i) high Reynolds number unsteady vortical flows, (ii) particle laden and interfacial flows, (iii)molecular dynamics studies of nanoscale droplets and studies of the structure, functions, and evolution of the earliest living cell. The unifying computational approach involves particle methods implemented in parallel computer architectures. The inherent adaptivity, robustness and efficiency of particle methods makes them a multidisciplinary computational tool capable of bridging the gap of micro-scale and continuum flow simulations. Using efficient tree data structures, multipole expansion algorithms, and improved particle-grid interpolation, particle methods allow for simulations using millions of computational elements, making possible the resolution of a wide range of length and time scales of these important physical phenomena.The current challenges in these simulations are in : [i] the proper formulation of particle methods in the molecular and continuous level for the discretization of the governing equations [ii] the resolution of the wide range of time and length scales governing the phenomena under investigation. [iii] the minimization of numerical artifacts that may interfere with the physics of the systems under consideration. [iv] the parallelization of processes such as tree traversal and grid-particle interpolations We are conducting simulations using vortex methods, molecular dynamics and smooth particle hydrodynamics, exploiting their unifying concepts such as : the solution of the N-body problem in parallel computers, highly accurate particle-particle and grid-particle interpolations, parallel FFT's and the formulation of processes such as diffusion in the context of particle methods. This approach enables us to transcend among seemingly unrelated areas of research.

Comparison of missing value imputation methods in time series: the case of Turkish meteorological data

NASA Astrophysics Data System (ADS)

Yozgatligil, Ceylan; Aslan, Sipan; Iyigun, Cem; Batmaz, Inci

2013-04-01

This study aims to compare several imputation methods to complete the missing values of spatio-temporal meteorological time series. To this end, six imputation methods are assessed with respect to various criteria including accuracy, robustness, precision, and efficiency for artificially created missing data in monthly total precipitation and mean temperature series obtained from the Turkish State Meteorological Service. Of these methods, simple arithmetic average, normal ratio (NR), and NR weighted with correlations comprise the simple ones, whereas multilayer perceptron type neural network and multiple imputation strategy adopted by Monte Carlo Markov Chain based on expectation-maximization (EM-MCMC) are computationally intensive ones. In addition, we propose a modification on the EM-MCMC method. Besides using a conventional accuracy measure based on squared errors, we also suggest the correlation dimension (CD) technique of nonlinear dynamic time series analysis which takes spatio-temporal dependencies into account for evaluating imputation performances. Depending on the detailed graphical and quantitative analysis, it can be said that although computational methods, particularly EM-MCMC method, are computationally inefficient, they seem favorable for imputation of meteorological time series with respect to different missingness periods considering both measures and both series studied. To conclude, using the EM-MCMC algorithm for imputing missing values before conducting any statistical analyses of meteorological data will definitely decrease the amount of uncertainty and give more robust results. Moreover, the CD measure can be suggested for the performance evaluation of missing data imputation particularly with computational methods since it gives more precise results in meteorological time series.

Computer aided diagnosis based on medical image processing and artificial intelligence methods

NASA Astrophysics Data System (ADS)

Stoitsis, John; Valavanis, Ioannis; Mougiakakou, Stavroula G.; Golemati, Spyretta; Nikita, Alexandra; Nikita, Konstantina S.

2006-12-01

Advances in imaging technology and computer science have greatly enhanced interpretation of medical images, and contributed to early diagnosis. The typical architecture of a Computer Aided Diagnosis (CAD) system includes image pre-processing, definition of region(s) of interest, features extraction and selection, and classification. In this paper, the principles of CAD systems design and development are demonstrated by means of two examples. The first one focuses on the differentiation between symptomatic and asymptomatic carotid atheromatous plaques. For each plaque, a vector of texture and motion features was estimated, which was then reduced to the most robust ones by means of ANalysis of VAriance (ANOVA). Using fuzzy c-means, the features were then clustered into two classes. Clustering performances of 74%, 79%, and 84% were achieved for texture only, motion only, and combinations of texture and motion features, respectively. The second CAD system presented in this paper supports the diagnosis of focal liver lesions and is able to characterize liver tissue from Computed Tomography (CT) images as normal, hepatic cyst, hemangioma, and hepatocellular carcinoma. Five texture feature sets were extracted for each lesion, while a genetic algorithm based feature selection method was applied to identify the most robust features. The selected feature set was fed into an ensemble of neural network classifiers. The achieved classification performance was 100%, 93.75% and 90.63% in the training, validation and testing set, respectively. It is concluded that computerized analysis of medical images in combination with artificial intelligence can be used in clinical practice and may contribute to more efficient diagnosis.

Finite Element Simulation of Articular Contact Mechanics with Quadratic Tetrahedral Elements

PubMed Central

Maas, Steve A.; Ellis, Benjamin J.; Rawlins, David S.; Weiss, Jeffrey A.

2016-01-01

Although it is easier to generate finite element discretizations with tetrahedral elements, trilinear hexahedral (HEX8) elements are more often used in simulations of articular contact mechanics. This is due to numerical shortcomings of linear tetrahedral (TET4) elements, limited availability of quadratic tetrahedron elements in combination with effective contact algorithms, and the perceived increased computational expense of quadratic finite elements. In this study we implemented both ten-node (TET10) and fifteen-node (TET15) quadratic tetrahedral elements in FEBio (www.febio.org) and compared their accuracy, robustness in terms of convergence behavior and computational cost for simulations relevant to articular contact mechanics. Suitable volume integration and surface integration rules were determined by comparing the results of several benchmark contact problems. The results demonstrated that the surface integration rule used to evaluate the contact integrals for quadratic elements affected both convergence behavior and accuracy of predicted stresses. The computational expense and robustness of both quadratic tetrahedral formulations compared favorably to the HEX8 models. Of note, the TET15 element demonstrated superior convergence behavior and lower computational cost than both the TET10 and HEX8 elements for meshes with similar numbers of degrees of freedom in the contact problems that we examined. Finally, the excellent accuracy and relative efficiency of these quadratic tetrahedral elements was illustrated by comparing their predictions with those for a HEX8 mesh for simulation of articular contact in a fully validated model of the hip. These results demonstrate that TET10 and TET15 elements provide viable alternatives to HEX8 elements for simulation of articular contact mechanics. PMID:26900037

Homo heuristicus: why biased minds make better inferences.

PubMed

Gigerenzer, Gerd; Brighton, Henry

2009-01-01

Heuristics are efficient cognitive processes that ignore information. In contrast to the widely held view that less processing reduces accuracy, the study of heuristics shows that less information, computation, and time can in fact improve accuracy. We review the major progress made so far: (a) the discovery of less-is-more effects; (b) the study of the ecological rationality of heuristics, which examines in which environments a given strategy succeeds or fails, and why; (c) an advancement from vague labels to computational models of heuristics; (d) the development of a systematic theory of heuristics that identifies their building blocks and the evolved capacities they exploit, and views the cognitive system as relying on an "adaptive toolbox;" and (e) the development of an empirical methodology that accounts for individual differences, conducts competitive tests, and has provided evidence for people's adaptive use of heuristics. Homo heuristicus has a biased mind and ignores part of the available information, yet a biased mind can handle uncertainty more efficiently and robustly than an unbiased mind relying on more resource-intensive and general-purpose processing strategies. Copyright © 2009 Cognitive Science Society, Inc.

Implementing the Data Center Energy Productivity Metric in a High Performance Computing Data Center

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sego, Landon H.; Marquez, Andres; Rawson, Andrew

2013-06-30

As data centers proliferate in size and number, the improvement of their energy efficiency and productivity has become an economic and environmental imperative. Making these improvements requires metrics that are robust, interpretable, and practical. We discuss the properties of a number of the proposed metrics of energy efficiency and productivity. In particular, we focus on the Data Center Energy Productivity (DCeP) metric, which is the ratio of useful work produced by the data center to the energy consumed performing that work. We describe our approach for using DCeP as the principal outcome of a designed experiment using a highly instrumented,more » high-performance computing data center. We found that DCeP was successful in clearly distinguishing different operational states in the data center, thereby validating its utility as a metric for identifying configurations of hardware and software that would improve energy productivity. We also discuss some of the challenges and benefits associated with implementing the DCeP metric, and we examine the efficacy of the metric in making comparisons within a data center and between data centers.« less

Yarn-dyed fabric defect classification based on convolutional neural network

NASA Astrophysics Data System (ADS)

Jing, Junfeng; Dong, Amei; Li, Pengfei

2017-07-01

Considering that the manual inspection of the yarn-dyed fabric can be time consuming and less efficient, a convolutional neural network (CNN) solution based on the modified AlexNet structure for the classification of the yarn-dyed fabric defect is proposed. CNN has powerful ability of feature extraction and feature fusion which can simulate the learning mechanism of the human brain. In order to enhance computational efficiency and detection accuracy, the local response normalization (LRN) layers in AlexNet are replaced by the batch normalization (BN) layers. In the process of the network training, through several convolution operations, the characteristics of the image are extracted step by step, and the essential features of the image can be obtained from the edge features. And the max pooling layers, the dropout layers, the fully connected layers are also employed in the classification model to reduce the computation cost and acquire more precise features of fabric defect. Finally, the results of the defect classification are predicted by the softmax function. The experimental results show the capability of defect classification via the modified Alexnet model and indicate its robustness.

Comparison of Implicit Schemes for the Incompressible Navier-Stokes Equations

NASA Technical Reports Server (NTRS)

Rogers, Stuart E.

1995-01-01

For a computational flow simulation tool to be useful in a design environment, it must be very robust and efficient. To develop such a tool for incompressible flow applications, a number of different implicit schemes are compared for several two-dimensional flow problems in the current study. The schemes include Point-Jacobi relaxation, Gauss-Seidel line relaxation, incomplete lower-upper decomposition, and the generalized minimum residual method preconditioned with each of the three other schemes. The efficiency of the schemes is measured in terms of the computing time required to obtain a steady-state solution for the laminar flow over a backward-facing step, the flow over a NACA 4412 airfoil, and the flow over a three-element airfoil using overset grids. The flow solver used in the study is the INS2D code that solves the incompressible Navier-Stokes equations using the method of artificial compressibility and upwind differencing of the convective terms. The results show that the generalized minimum residual method preconditioned with the incomplete lower-upper factorization outperforms all other methods by at least a factor of 2.

Curvilinear immersed-boundary method for simulating unsteady flows in shallow natural streams with arbitrarily complex obstacles

NASA Astrophysics Data System (ADS)

Kang, Seokkoo; Borazjani, Iman; Sotiropoulos, Fotis

2008-11-01

Unsteady 3D simulations of flows in natural streams is a challenging task due to the complexity of the bathymetry, the shallowness of the flow, and the presence of multiple nature- and man-made obstacles. This work is motivated by the need to develop a powerful numerical method for simulating such flows using coherent-structure-resolving turbulence models. We employ the curvilinear immersed boundary method of Ge and Sotiropoulos (Journal of Computational Physics, 2007) and address the critical issue of numerical efficiency in large aspect ratio computational domains and grids such as those encountered in long and shallow open channels. We show that the matrix-free Newton-Krylov method for solving the momentum equations coupled with an algebraic multigrid method with incomplete LU preconditioner for solving the Poisson equation yield a robust and efficient procedure for obtaining time-accurate solutions in such problems. We demonstrate the potential of the numerical approach by carrying out a direct numerical simulation of flow in a long and shallow meandering stream with multiple hydraulic structures.

A Gossip-based Energy Efficient Protocol for Robust In-network Aggregation in Wireless Sensor Networks

NASA Astrophysics Data System (ADS)

Fauji, Shantanu

We consider the problem of energy efficient and fault tolerant in--network aggregation for wireless sensor networks (WSNs). In-network aggregation is the process of aggregation while collecting data from sensors to the base station. This process should be energy efficient due to the limited energy at the sensors and tolerant to the high failure rates common in sensor networks. Tree based in--network aggregation protocols, although energy efficient, are not robust to network failures. Multipath routing protocols are robust to failures to a certain degree but are not energy efficient due to the overhead in the maintenance of multiple paths. We propose a new protocol for in-network aggregation in WSNs, which is energy efficient, achieves high lifetime, and is robust to the changes in the network topology. Our protocol, gossip--based protocol for in-network aggregation (GPIA) is based on the spreading of information via gossip. GPIA is not only adaptive to failures and changes in the network topology, but is also energy efficient. Energy efficiency of GPIA comes from all the nodes being capable of selective message reception and detecting convergence of the aggregation early. We experimentally show that GPIA provides significant improvement over some other competitors like the Ridesharing, Synopsis Diffusion and the pure version of gossip. GPIA shows ten fold, five fold and two fold improvement over the pure gossip, the synopsis diffusion and Ridesharing protocols in terms of network lifetime, respectively. Further, GPIA retains gossip's robustness to failures and improves upon the accuracy of synopsis diffusion and Ridesharing.

Robust Classification and Segmentation of Planar and Linear Features for Construction Site Progress Monitoring and Structural Dimension Compliance Control

NASA Astrophysics Data System (ADS)

Maalek, R.; Lichti, D. D.; Ruwanpura, J.

2015-08-01

The application of terrestrial laser scanners (TLSs) on construction sites for automating construction progress monitoring and controlling structural dimension compliance is growing markedly. However, current research in construction management relies on the planned building information model (BIM) to assign the accumulated point clouds to their corresponding structural elements, which may not be reliable in cases where the dimensions of the as-built structure differ from those of the planned model and/or the planned model is not available with sufficient detail. In addition outliers exist in construction site datasets due to data artefacts caused by moving objects, occlusions and dust. In order to overcome the aforementioned limitations, a novel method for robust classification and segmentation of planar and linear features is proposed to reduce the effects of outliers present in the LiDAR data collected from construction sites. First, coplanar and collinear points are classified through a robust principal components analysis procedure. The classified points are then grouped using a robust clustering method. A method is also proposed to robustly extract the points belonging to the flat-slab floors and/or ceilings without performing the aforementioned stages in order to preserve computational efficiency. The applicability of the proposed method is investigated in two scenarios, namely, a laboratory with 30 million points and an actual construction site with over 150 million points. The results obtained by the two experiments validate the suitability of the proposed method for robust segmentation of planar and linear features in contaminated datasets, such as those collected from construction sites.

Application of the 3D slicer chest imaging platform segmentation algorithm for large lung nodule delineation

PubMed Central

Parmar, Chintan; Blezek, Daniel; Estepar, Raul San Jose; Pieper, Steve; Kim, John; Aerts, Hugo J. W. L.

2017-01-01

Purpose Accurate segmentation of lung nodules is crucial in the development of imaging biomarkers for predicting malignancy of the nodules. Manual segmentation is time consuming and affected by inter-observer variability. We evaluated the robustness and accuracy of a publically available semiautomatic segmentation algorithm that is implemented in the 3D Slicer Chest Imaging Platform (CIP) and compared it with the performance of manual segmentation. Methods CT images of 354 manually segmented nodules were downloaded from the LIDC database. Four radiologists performed the manual segmentation and assessed various nodule characteristics. The semiautomatic CIP segmentation was initialized using the centroid of the manual segmentations, thereby generating four contours for each nodule. The robustness of both segmentation methods was assessed using the region of uncertainty (δ) and Dice similarity index (DSI). The robustness of the segmentation methods was compared using the Wilcoxon-signed rank test (pWilcoxon<0.05). The Dice similarity index (DSIAgree) between the manual and CIP segmentations was computed to estimate the accuracy of the semiautomatic contours. Results The median computational time of the CIP segmentation was 10 s. The median CIP and manually segmented volumes were 477 ml and 309 ml, respectively. CIP segmentations were significantly more robust than manual segmentations (median δCIP = 14ml, median dsiCIP = 99% vs. median δmanual = 222ml, median dsimanual = 82%) with pWilcoxon~10−16. The agreement between CIP and manual segmentations had a median DSIAgree of 60%. While 13% (47/354) of the nodules did not require any manual adjustment, minor to substantial manual adjustments were needed for 87% (305/354) of the nodules. CIP segmentations were observed to perform poorly (median DSIAgree≈50%) for non-/sub-solid nodules with subtle appearances and poorly defined boundaries. Conclusion Semi-automatic CIP segmentation can potentially reduce the physician workload for 13% of nodules owing to its computational efficiency and superior stability compared to manual segmentation. Although manual adjustment is needed for many cases, CIP segmentation provides a preliminary contour for physicians as a starting point. PMID:28594880

Flaws in current human training protocols for spontaneous Brain-Computer Interfaces: lessons learned from instructional design

PubMed Central

Lotte, Fabien; Larrue, Florian; Mühl, Christian

2013-01-01

While recent research on Brain-Computer Interfaces (BCI) has highlighted their potential for many applications, they remain barely used outside laboratories. The main reason is their lack of robustness. Indeed, with current BCI, mental state recognition is usually slow and often incorrect. Spontaneous BCI (i.e., mental imagery-based BCI) often rely on mutual learning efforts by the user and the machine, with BCI users learning to produce stable ElectroEncephaloGraphy (EEG) patterns (spontaneous BCI control being widely acknowledged as a skill) while the computer learns to automatically recognize these EEG patterns, using signal processing. Most research so far was focused on signal processing, mostly neglecting the human in the loop. However, how well the user masters the BCI skill is also a key element explaining BCI robustness. Indeed, if the user is not able to produce stable and distinct EEG patterns, then no signal processing algorithm would be able to recognize them. Unfortunately, despite the importance of BCI training protocols, they have been scarcely studied so far, and used mostly unchanged for years. In this paper, we advocate that current human training approaches for spontaneous BCI are most likely inappropriate. We notably study instructional design literature in order to identify the key requirements and guidelines for a successful training procedure that promotes a good and efficient skill learning. This literature study highlights that current spontaneous BCI user training procedures satisfy very few of these requirements and hence are likely to be suboptimal. We therefore identify the flaws in BCI training protocols according to instructional design principles, at several levels: in the instructions provided to the user, in the tasks he/she has to perform, and in the feedback provided. For each level, we propose new research directions that are theoretically expected to address some of these flaws and to help users learn the BCI skill more efficiently. PMID:24062669

Quantification of uncertainty in first-principles predicted mechanical properties of solids: Application to solid ion conductors

NASA Astrophysics Data System (ADS)

Ahmad, Zeeshan; Viswanathan, Venkatasubramanian

2016-08-01

Computationally-guided material discovery is being increasingly employed using a descriptor-based screening through the calculation of a few properties of interest. A precise understanding of the uncertainty associated with first-principles density functional theory calculated property values is important for the success of descriptor-based screening. The Bayesian error estimation approach has been built in to several recently developed exchange-correlation functionals, which allows an estimate of the uncertainty associated with properties related to the ground state energy, for example, adsorption energies. Here, we propose a robust and computationally efficient method for quantifying uncertainty in mechanical properties, which depend on the derivatives of the energy. The procedure involves calculating energies around the equilibrium cell volume with different strains and fitting the obtained energies to the corresponding energy-strain relationship. At each strain, we use instead of a single energy, an ensemble of energies, giving us an ensemble of fits and thereby, an ensemble of mechanical properties associated with each fit, whose spread can be used to quantify its uncertainty. The generation of ensemble of energies is only a post-processing step involving a perturbation of parameters of the exchange-correlation functional and solving for the energy non-self-consistently. The proposed method is computationally efficient and provides a more robust uncertainty estimate compared to the approach of self-consistent calculations employing several different exchange-correlation functionals. We demonstrate the method by calculating the uncertainty bounds for several materials belonging to different classes and having different structures using the developed method. We show that the calculated uncertainty bounds the property values obtained using three different GGA functionals: PBE, PBEsol, and RPBE. Finally, we apply the approach to calculate the uncertainty associated with the DFT-calculated elastic properties of solid state Li-ion and Na-ion conductors.

RNAdualPF: software to compute the dual partition function with sample applications in molecular evolution theory.

PubMed

Garcia-Martin, Juan Antonio; Bayegan, Amir H; Dotu, Ivan; Clote, Peter

2016-10-19

RNA inverse folding is the problem of finding one or more sequences that fold into a user-specified target structure s 0 , i.e. whose minimum free energy secondary structure is identical to the target s 0 . Here we consider the ensemble of all RNA sequences that have low free energy with respect to a given target s 0 . We introduce the program RNAdualPF, which computes the dual partition function Z ∗ , defined as the sum of Boltzmann factors exp(-E(a,s 0 )/RT) of all RNA nucleotide sequences a compatible with target structure s 0 . Using RNAdualPF, we efficiently sample RNA sequences that approximately fold into s 0 , where additionally the user can specify IUPAC sequence constraints at certain positions, and whether to include dangles (energy terms for stacked, single-stranded nucleotides). Moreover, since we also compute the dual partition function Z ∗ (k) over all sequences having GC-content k, the user can require that all sampled sequences have a precise, specified GC-content. Using Z ∗ , we compute the dual expected energy 〈E ∗ 〉, and use it to show that natural RNAs from the Rfam 12.0 database have higher minimum free energy than expected, thus suggesting that functional RNAs are under evolutionary pressure to be only marginally thermodynamically stable. We show that C. elegans precursor microRNA (pre-miRNA) is significantly non-robust with respect to mutations, by comparing the robustness of each wild type pre-miRNA sequence with 2000 [resp. 500] sequences of the same GC-content generated by RNAdualPF, which approximately [resp. exactly] fold into the wild type target structure. We confirm and strengthen earlier findings that precursor microRNAs and bacterial small noncoding RNAs display plasticity, a measure of structural diversity. We describe RNAdualPF, which rapidly computes the dual partition function Z ∗ and samples sequences having low energy with respect to a target structure, allowing sequence constraints and specified GC-content. Using different inverse folding software, another group had earlier shown that pre-miRNA is mutationally robust, even controlling for compositional bias. Our opposite conclusion suggests a cautionary note that computationally based insights into molecular evolution may heavily depend on the software used. C/C++-software for RNAdualPF is available at http://bioinformatics.bc.edu/clotelab/RNAdualPF .

Efficient encoding of motion is mediated by gap junctions in the fly visual system.

PubMed

Wang, Siwei; Borst, Alexander; Zaslavsky, Noga; Tishby, Naftali; Segev, Idan

2017-12-01

Understanding the computational implications of specific synaptic connectivity patterns is a fundamental goal in neuroscience. In particular, the computational role of ubiquitous electrical synapses operating via gap junctions remains elusive. In the fly visual system, the cells in the vertical-system network, which play a key role in visual processing, primarily connect to each other via axonal gap junctions. This network therefore provides a unique opportunity to explore the functional role of gap junctions in sensory information processing. Our information theoretical analysis of a realistic VS network model shows that within 10 ms following the onset of the visual input, the presence of axonal gap junctions enables the VS system to efficiently encode the axis of rotation, θ, of the fly's ego motion. This encoding efficiency, measured in bits, is near-optimal with respect to the physical limits of performance determined by the statistical structure of the visual input itself. The VS network is known to be connected to downstream pathways via a subset of triplets of the vertical system cells; we found that because of the axonal gap junctions, the efficiency of this subpopulation in encoding θ is superior to that of the whole vertical system network and is robust to a wide range of signal to noise ratios. We further demonstrate that this efficient encoding of motion by this subpopulation is necessary for the fly's visually guided behavior, such as banked turns in evasive maneuvers. Because gap junctions are formed among the axons of the vertical system cells, they only impact the system's readout, while maintaining the dendritic input intact, suggesting that the computational principles implemented by neural circuitries may be much richer than previously appreciated based on point neuron models. Our study provides new insights as to how specific network connectivity leads to efficient encoding of sensory stimuli.

Evaluating metrics of local topographic position for multiscale geomorphometric analysis

NASA Astrophysics Data System (ADS)

Newman, D. R.; Lindsay, J. B.; Cockburn, J. M. H.

2018-07-01

The field of geomorphometry has increasingly moved towards the use of multiscale analytical techniques, due to the availability of fine-resolution digital elevation models (DEMs) and the inherent scale-dependency of many DEM-derived attributes such as local topographic position (LTP). LTP is useful for landform and soils mapping and numerous other environmental applications. Multiple LTP metrics have been proposed and applied in the literature; however, elevation percentile (EP) is notable for its robustness to elevation error and applicability to non-Gaussian local elevation distributions, both of which are common characteristics of DEM data sets. Multiscale LTP analysis involves the estimation of spatial patterns using a range of neighborhood sizes, traditionally achieved by applying spatial filtering techniques with varying kernel sizes. While EP can be demonstrated to provide accurate estimates of LTP, the computationally intensive method of its calculation makes it unsuited to multiscale LTP analysis, particularly at large neighborhood sizes or with fine-resolution DEMs. This research assessed the suitability of three LTP metrics for multiscale terrain characterization by quantifying their computational efficiency and by comparing their ability to approximate EP spatial patterns under varying topographic conditions. The tested LTP metrics included: deviation from mean elevation (DEV), percent elevation range (PER), and the novel relative topographic position (RTP) index. The results demonstrated that DEV, calculated using the integral image technique, offers fast and scale-invariant computation. DEV spatial patterns were strongly correlated with EP (r2 range of 0.699 to 0.967) under all tested topographic conditions. RTP was also a strong predictor of EP (r2 range of 0.594 to 0.917). PER was the weakest predictor of EP (r2 range of 0.031 to 0.801) without offering a substantial improvement in computational efficiency over RTP. PER was therefore determined to be unsuitable for most multiscale applications. It was concluded that the scale-invariant property offered by the integral image used by the DEV method counters the minor losses in robustness compared to EP, making DEV the optimal LTP metric for multiscale applications.

Towards scalable quantum communication and computation: Novel approaches and realizations

NASA Astrophysics Data System (ADS)

Jiang, Liang

Quantum information science involves exploration of fundamental laws of quantum mechanics for information processing tasks. This thesis presents several new approaches towards scalable quantum information processing. First, we consider a hybrid approach to scalable quantum computation, based on an optically connected network of few-qubit quantum registers. Specifically, we develop a novel scheme for scalable quantum computation that is robust against various imperfections. To justify that nitrogen-vacancy (NV) color centers in diamond can be a promising realization of the few-qubit quantum register, we show how to isolate a few proximal nuclear spins from the rest of the environment and use them for the quantum register. We also demonstrate experimentally that the nuclear spin coherence is only weakly perturbed under optical illumination, which allows us to implement quantum logical operations that use the nuclear spins to assist the repetitive-readout of the electronic spin. Using this technique, we demonstrate more than two-fold improvement in signal-to-noise ratio. Apart from direct application to enhance the sensitivity of the NV-based nano-magnetometer, this experiment represents an important step towards the realization of robust quantum information processors using electronic and nuclear spin qubits. We then study realizations of quantum repeaters for long distance quantum communication. Specifically, we develop an efficient scheme for quantum repeaters based on atomic ensembles. We use dynamic programming to optimize various quantum repeater protocols. In addition, we propose a new protocol of quantum repeater with encoding, which efficiently uses local resources (about 100 qubits) to identify and correct errors, to achieve fast one-way quantum communication over long distances. Finally, we explore quantum systems with topological order. Such systems can exhibit remarkable phenomena such as quasiparticles with anyonic statistics and have been proposed as candidates for naturally error-free quantum computation. We propose a scheme to unambiguously detect the anyonic statistics in spin lattice realizations using ultra-cold atoms in an optical lattice. We show how to reliably read and write topologically protected quantum memory using an atomic or photonic qubit.

Computationally efficient video restoration for Nyquist sampled imaging sensors combining an affine-motion-based temporal Kalman filter and adaptive Wiener filter.

PubMed

Rucci, Michael; Hardie, Russell C; Barnard, Kenneth J

2014-05-01

In this paper, we present a computationally efficient video restoration algorithm to address both blur and noise for a Nyquist sampled imaging system. The proposed method utilizes a temporal Kalman filter followed by a correlation-model based spatial adaptive Wiener filter (AWF). The Kalman filter employs an affine background motion model and novel process-noise variance estimate. We also propose and demonstrate a new multidelay temporal Kalman filter designed to more robustly treat local motion. The AWF is a spatial operation that performs deconvolution and adapts to the spatially varying residual noise left in the Kalman filter stage. In image areas where the temporal Kalman filter is able to provide significant noise reduction, the AWF can be aggressive in its deconvolution. In other areas, where less noise reduction is achieved with the Kalman filter, the AWF balances the deconvolution with spatial noise reduction. In this way, the Kalman filter and AWF work together effectively, but without the computational burden of full joint spatiotemporal processing. We also propose a novel hybrid system that combines a temporal Kalman filter and BM3D processing. To illustrate the efficacy of the proposed methods, we test the algorithms on both simulated imagery and video collected with a visible camera.

Sparse deconvolution for the large-scale ill-posed inverse problem of impact force reconstruction

NASA Astrophysics Data System (ADS)

Qiao, Baijie; Zhang, Xingwu; Gao, Jiawei; Liu, Ruonan; Chen, Xuefeng

2017-01-01

Most previous regularization methods for solving the inverse problem of force reconstruction are to minimize the l2-norm of the desired force. However, these traditional regularization methods such as Tikhonov regularization and truncated singular value decomposition, commonly fail to solve the large-scale ill-posed inverse problem in moderate computational cost. In this paper, taking into account the sparse characteristic of impact force, the idea of sparse deconvolution is first introduced to the field of impact force reconstruction and a general sparse deconvolution model of impact force is constructed. Second, a novel impact force reconstruction method based on the primal-dual interior point method (PDIPM) is proposed to solve such a large-scale sparse deconvolution model, where minimizing the l2-norm is replaced by minimizing the l1-norm. Meanwhile, the preconditioned conjugate gradient algorithm is used to compute the search direction of PDIPM with high computational efficiency. Finally, two experiments including the small-scale or medium-scale single impact force reconstruction and the relatively large-scale consecutive impact force reconstruction are conducted on a composite wind turbine blade and a shell structure to illustrate the advantage of PDIPM. Compared with Tikhonov regularization, PDIPM is more efficient, accurate and robust whether in the single impact force reconstruction or in the consecutive impact force reconstruction.

IAServ: an intelligent home care web services platform in a cloud for aging-in-place.

PubMed

Su, Chuan-Jun; Chiang, Chang-Yu

2013-11-12

As the elderly population has been rapidly expanding and the core tax-paying population has been shrinking, the need for adequate elderly health and housing services continues to grow while the resources to provide such services are becoming increasingly scarce. Thus, increasing the efficiency of the delivery of healthcare services through the use of modern technology is a pressing issue. The seamless integration of such enabling technologies as ontology, intelligent agents, web services, and cloud computing is transforming healthcare from hospital-based treatments to home-based self-care and preventive care. A ubiquitous healthcare platform based on this technological integration, which synergizes service providers with patients' needs to be developed to provide personalized healthcare services at the right time, in the right place, and the right manner. This paper presents the development and overall architecture of IAServ (the Intelligent Aging-in-place Home care Web Services Platform) to provide personalized healthcare service ubiquitously in a cloud computing setting to support the most desirable and cost-efficient method of care for the aged-aging in place. The IAServ is expected to offer intelligent, pervasive, accurate and contextually-aware personal care services. Architecturally the implemented IAServ leverages web services and cloud computing to provide economic, scalable, and robust healthcare services over the Internet.

G‐LoSA: An efficient computational tool for local structure‐centric biological studies and drug design

PubMed Central

2016-01-01

Abstract Molecular recognition by protein mostly occurs in a local region on the protein surface. Thus, an efficient computational method for accurate characterization of protein local structural conservation is necessary to better understand biology and drug design. We present a novel local structure alignment tool, G‐LoSA. G‐LoSA aligns protein local structures in a sequence order independent way and provides a GA‐score, a chemical feature‐based and size‐independent structure similarity score. Our benchmark validation shows the robust performance of G‐LoSA to the local structures of diverse sizes and characteristics, demonstrating its universal applicability to local structure‐centric comparative biology studies. In particular, G‐LoSA is highly effective in detecting conserved local regions on the entire surface of a given protein. In addition, the applications of G‐LoSA to identifying template ligands and predicting ligand and protein binding sites illustrate its strong potential for computer‐aided drug design. We hope that G‐LoSA can be a useful computational method for exploring interesting biological problems through large‐scale comparison of protein local structures and facilitating drug discovery research and development. G‐LoSA is freely available to academic users at http://im.compbio.ku.edu/GLoSA/. PMID:26813336

Optical studies of current-induced magnetization switching and photonic quantum states

NASA Astrophysics Data System (ADS)

Lorenz, Virginia

2017-04-01

The ever-decreasing size of electronic components is leading to a fundamental change in the way computers operate, as at the few-nanometer scale, resistive heating and quantum mechanics prohibit efficient and stable operation. One of the most promising next-generation computing paradigms is Spintronics, which uses the spin of the electron to manipulate and store information in the form of magnetic thin films. I will present our optical studies of the fundamental mechanisms by which we can efficiently manipulate magnetization using electrical current. Although electron spin is a quantum-mechanical property, Spintronics relies on macroscopic magnetization and thus does not take advantage of quantum mechanics in the algorithms used to encode and transmit information. For the second part of my talk, I will present our work under the umbrella of new computing and communication technologies based on the quantum mechanical properties of photons. Quantum technologies often require the carriers of information, or qubits, to have specific properties. Photonic quantum states are good information carriers because they travel fast and are robust to environmental fluctuations, but characterizing and controlling photonic sources so the photons have just the right properties is still a challenge. I will describe our work towards enabling quantum-physics-based secure long-distance communication using photons.

IAServ: An Intelligent Home Care Web Services Platform in a Cloud for Aging-in-Place

PubMed Central

Su, Chuan-Jun; Chiang, Chang-Yu

2013-01-01

As the elderly population has been rapidly expanding and the core tax-paying population has been shrinking, the need for adequate elderly health and housing services continues to grow while the resources to provide such services are becoming increasingly scarce. Thus, increasing the efficiency of the delivery of healthcare services through the use of modern technology is a pressing issue. The seamless integration of such enabling technologies as ontology, intelligent agents, web services, and cloud computing is transforming healthcare from hospital-based treatments to home-based self-care and preventive care. A ubiquitous healthcare platform based on this technological integration, which synergizes service providers with patients’ needs to be developed to provide personalized healthcare services at the right time, in the right place, and the right manner. This paper presents the development and overall architecture of IAServ (the Intelligent Aging-in-place Home care Web Services Platform) to provide personalized healthcare service ubiquitously in a cloud computing setting to support the most desirable and cost-efficient method of care for the aged-aging in place. The IAServ is expected to offer intelligent, pervasive, accurate and contextually-aware personal care services. Architecturally the implemented IAServ leverages web services and cloud computing to provide economic, scalable, and robust healthcare services over the Internet. PMID:24225647

A direct method for nonlinear ill-posed problems

NASA Astrophysics Data System (ADS)

Lakhal, A.

2018-02-01

We propose a direct method for solving nonlinear ill-posed problems in Banach-spaces. The method is based on a stable inversion formula we explicitly compute by applying techniques for analytic functions. Furthermore, we investigate the convergence and stability of the method and prove that the derived noniterative algorithm is a regularization. The inversion formula provides a systematic sensitivity analysis. The approach is applicable to a wide range of nonlinear ill-posed problems. We test the algorithm on a nonlinear problem of travel-time inversion in seismic tomography. Numerical results illustrate the robustness and efficiency of the algorithm.

Inverse halftoning via robust nonlinear filtering

NASA Astrophysics Data System (ADS)

Shen, Mei-Yin; Kuo, C.-C. Jay

1999-10-01

A new blind inverse halftoning algorithm based on a nonlinear filtering technique of low computational complexity and low memory requirement is proposed in this research. It is called blind since we do not require the knowledge of the halftone kernel. The proposed scheme performs nonlinear filtering in conjunction with edge enhancement to improve the quality of an inverse halftoned image. Distinct features of the proposed approach include: efficiently smoothing halftone patterns in large homogeneous areas, additional edge enhancement capability to recover the edge quality and an excellent PSNR performance with only local integer operations and a small memory buffer.

Protein Simulation Data in the Relational Model.

PubMed

Simms, Andrew M; Daggett, Valerie

2012-10-01

High performance computing is leading to unprecedented volumes of data. Relational databases offer a robust and scalable model for storing and analyzing scientific data. However, these features do not come without a cost-significant design effort is required to build a functional and efficient repository. Modeling protein simulation data in a relational database presents several challenges: the data captured from individual simulations are large, multi-dimensional, and must integrate with both simulation software and external data sites. Here we present the dimensional design and relational implementation of a comprehensive data warehouse for storing and analyzing molecular dynamics simulations using SQL Server.

Protein Simulation Data in the Relational Model

PubMed Central

Simms, Andrew M.; Daggett, Valerie

2011-01-01

High performance computing is leading to unprecedented volumes of data. Relational databases offer a robust and scalable model for storing and analyzing scientific data. However, these features do not come without a cost—significant design effort is required to build a functional and efficient repository. Modeling protein simulation data in a relational database presents several challenges: the data captured from individual simulations are large, multi-dimensional, and must integrate with both simulation software and external data sites. Here we present the dimensional design and relational implementation of a comprehensive data warehouse for storing and analyzing molecular dynamics simulations using SQL Server. PMID:23204646

Craniofacial Reconstruction Using Rational Cubic Ball Curves

PubMed Central

Majeed, Abdul; Mt Piah, Abd Rahni; Gobithaasan, R. U.; Yahya, Zainor Ridzuan

2015-01-01

This paper proposes the reconstruction of craniofacial fracture using rational cubic Ball curve. The idea of choosing Ball curve is based on its robustness of computing efficiency over Bezier curve. The main steps are conversion of Digital Imaging and Communications in Medicine (Dicom) images to binary images, boundary extraction and corner point detection, Ball curve fitting with genetic algorithm and final solution conversion to Dicom format. The last section illustrates a real case of craniofacial reconstruction using the proposed method which clearly indicates the applicability of this method. A Graphical User Interface (GUI) has also been developed for practical application. PMID:25880632

Registration of segmented histological images using thin plate splines and belief propagation

NASA Astrophysics Data System (ADS)

Kybic, Jan

2014-03-01

We register images based on their multiclass segmentations, for cases when correspondence of local features cannot be established. A discrete mutual information is used as a similarity criterion. It is evaluated at a sparse set of location on the interfaces between classes. A thin-plate spline regularization is approximated by pairwise interactions. The problem is cast into a discrete setting and solved efficiently by belief propagation. Further speedup and robustness is provided by a multiresolution framework. Preliminary experiments suggest that our method can provide similar registration quality to standard methods at a fraction of the computational cost.

Development of homotopy algorithms for fixed-order mixed H2/H(infinity) controller synthesis

NASA Technical Reports Server (NTRS)

Whorton, M.; Buschek, H.; Calise, A. J.

1994-01-01

A major difficulty associated with H-infinity and mu-synthesis methods is the order of the resulting compensator. Whereas model and/or controller reduction techniques are sometimes applied, performance and robustness properties are not preserved. By directly constraining compensator order during the optimization process, these properties are better preserved, albeit at the expense of computational complexity. This paper presents a novel homotopy algorithm to synthesize fixed-order mixed H2/H-infinity compensators. Numerical results are presented for a four-disk flexible structure to evaluate the efficiency of the algorithm.

Uncertainty propagation of p-boxes using sparse polynomial chaos expansions

NASA Astrophysics Data System (ADS)

Schöbi, Roland; Sudret, Bruno

2017-06-01

In modern engineering, physical processes are modelled and analysed using advanced computer simulations, such as finite element models. Furthermore, concepts of reliability analysis and robust design are becoming popular, hence, making efficient quantification and propagation of uncertainties an important aspect. In this context, a typical workflow includes the characterization of the uncertainty in the input variables. In this paper, input variables are modelled by probability-boxes (p-boxes), accounting for both aleatory and epistemic uncertainty. The propagation of p-boxes leads to p-boxes of the output of the computational model. A two-level meta-modelling approach is proposed using non-intrusive sparse polynomial chaos expansions to surrogate the exact computational model and, hence, to facilitate the uncertainty quantification analysis. The capabilities of the proposed approach are illustrated through applications using a benchmark analytical function and two realistic engineering problem settings. They show that the proposed two-level approach allows for an accurate estimation of the statistics of the response quantity of interest using a small number of evaluations of the exact computational model. This is crucial in cases where the computational costs are dominated by the runs of high-fidelity computational models.

Uncertainty propagation of p-boxes using sparse polynomial chaos expansions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schöbi, Roland, E-mail: schoebi@ibk.baug.ethz.ch; Sudret, Bruno, E-mail: sudret@ibk.baug.ethz.ch

2017-06-15

In modern engineering, physical processes are modelled and analysed using advanced computer simulations, such as finite element models. Furthermore, concepts of reliability analysis and robust design are becoming popular, hence, making efficient quantification and propagation of uncertainties an important aspect. In this context, a typical workflow includes the characterization of the uncertainty in the input variables. In this paper, input variables are modelled by probability-boxes (p-boxes), accounting for both aleatory and epistemic uncertainty. The propagation of p-boxes leads to p-boxes of the output of the computational model. A two-level meta-modelling approach is proposed using non-intrusive sparse polynomial chaos expansions tomore » surrogate the exact computational model and, hence, to facilitate the uncertainty quantification analysis. The capabilities of the proposed approach are illustrated through applications using a benchmark analytical function and two realistic engineering problem settings. They show that the proposed two-level approach allows for an accurate estimation of the statistics of the response quantity of interest using a small number of evaluations of the exact computational model. This is crucial in cases where the computational costs are dominated by the runs of high-fidelity computational models.« less

Enhanced H-filter based on Fåhræus-Lindqvist effect for efficient and robust dialysis without membrane

PubMed Central

Zheng, Wei-Chao; Xie, Rui; He, Li-Qun; Xi, Yue-Heng; Liu, Ying-Mei; Meng, Zhi-Jun; Wang, Wei; Ju, Xiao-Jie; Chen, Gang; Chu, Liang-Yin

2015-01-01

A novel microfluidic device for highly efficient and robust dialysis without membrane is highly desired for the development of portable or wearable microdialyzer. Here we report an enhanced H-filter with pillar array based on Fåhræus-Lindqvist effect (F-L effect) for highly efficient and robust membraneless dialysis of simplified blood for the first time. The H-filter employs two fluids laminarly flowing in the microchannel for continuously membraneless dialysis. With pillar array in the microchannel, the two laminar flows, with one containing blood cells and small molecules and another containing dialyzate solution, can form a cell-free layer at the interface as selective zones for separation. This provides enhanced mixing yet extremely low shear for extraction of small molecules from the blood-cell-containing flow into the dialyzate flow, resulting in robust separation with reduced cell loss and improved efficiency. We demonstrate this by first using Chlorella pyrenoidosa as model cells to quantitatively study the separation performances, and then using simplified human blood for dialysis. The advanced H-filter, with highly efficient and robust performance for membraneless dialysis, shows great potential as promising candidate for rapid blood analysis/separation, and as fundamental structure for portable dialyzer. PMID:26339313

Enhanced H-filter based on Fåhræus-Lindqvist effect for efficient and robust dialysis without membrane.

PubMed

Zheng, Wei-Chao; Xie, Rui; He, Li-Qun; Xi, Yue-Heng; Liu, Ying-Mei; Meng, Zhi-Jun; Wang, Wei; Ju, Xiao-Jie; Chen, Gang; Chu, Liang-Yin

2015-07-01

A novel microfluidic device for highly efficient and robust dialysis without membrane is highly desired for the development of portable or wearable microdialyzer. Here we report an enhanced H-filter with pillar array based on Fåhræus-Lindqvist effect (F-L effect) for highly efficient and robust membraneless dialysis of simplified blood for the first time. The H-filter employs two fluids laminarly flowing in the microchannel for continuously membraneless dialysis. With pillar array in the microchannel, the two laminar flows, with one containing blood cells and small molecules and another containing dialyzate solution, can form a cell-free layer at the interface as selective zones for separation. This provides enhanced mixing yet extremely low shear for extraction of small molecules from the blood-cell-containing flow into the dialyzate flow, resulting in robust separation with reduced cell loss and improved efficiency. We demonstrate this by first using Chlorella pyrenoidosa as model cells to quantitatively study the separation performances, and then using simplified human blood for dialysis. The advanced H-filter, with highly efficient and robust performance for membraneless dialysis, shows great potential as promising candidate for rapid blood analysis/separation, and as fundamental structure for portable dialyzer.

Endovascular Electrodes for Electrical Stimulation of Blood Vessels for Vasoconstriction - a Finite Element Simulation Study

NASA Astrophysics Data System (ADS)

Kezurer, Noa; Farah, Nairouz; Mandel, Yossi

2016-08-01

Hemorrhagic shock accounts for 30-40 percent of trauma mortality, as bleeding may sometimes be hard to control. Application of short electrical pulses on blood vessels was recently shown to elicit robust vasoconstriction and reduction of blood loss following vascular injury. In this study we present a novel approach for vasoconstriction based on endovascular application of electrical pulses for situations where access to the vessel is limited. In addition to ease of access, we hypothesize that this novel approach will result in a localized and efficient vasoconstriction. Using computer modeling (COMSOL Multiphysics, Electric Currents Module), we studied the effect of endovascular pulsed electrical treatment on abdominal aorta of pigs, and compared the efficiency of different electrodes configurations on the electric field amplitude, homogeneity and locality when applied on a blood vessel wall. Results reveal that the optimal configuration is the endovascular approach where four electrodes are used, spaced 13 mm apart. Furthermore, computer based temperature investigations (bio-heat model, COMSOL Multiphysics) show that the maximum expected temperature rise is of 1.2 degrees; highlighting the safety of the four endovascular electrodes configuration. These results can aid in planning the application of endovascular pulsed electrical treatment as an efficient and safe vasoconstriction approach.

Shape and Stress Sensing of Multilayered Composite and Sandwich Structures Using an Inverse Finite Element Method

NASA Technical Reports Server (NTRS)

Cerracchio, Priscilla; Gherlone, Marco; Di Sciuva, Marco; Tessler, Alexander

2013-01-01

The marked increase in the use of composite and sandwich material systems in aerospace, civil, and marine structures leads to the need for integrated Structural Health Management systems. A key capability to enable such systems is the real-time reconstruction of structural deformations, stresses, and failure criteria that are inferred from in-situ, discrete-location strain measurements. This technology is commonly referred to as shape- and stress-sensing. Presented herein is a computationally efficient shape- and stress-sensing methodology that is ideally suited for applications to laminated composite and sandwich structures. The new approach employs the inverse Finite Element Method (iFEM) as a general framework and the Refined Zigzag Theory (RZT) as the underlying plate theory. A three-node inverse plate finite element is formulated. The element formulation enables robust and efficient modeling of plate structures instrumented with strain sensors that have arbitrary positions. The methodology leads to a set of linear algebraic equations that are solved efficiently for the unknown nodal displacements. These displacements are then used at the finite element level to compute full-field strains, stresses, and failure criteria that are in turn used to assess structural integrity. Numerical results for multilayered, highly heterogeneous laminates demonstrate the unique capability of this new formulation for shape- and stress-sensing.

A fast and automatic fusion algorithm for unregistered multi-exposure image sequence

NASA Astrophysics Data System (ADS)

Liu, Yan; Yu, Feihong

2014-09-01

Human visual system (HVS) can visualize all the brightness levels of the scene through visual adaptation. However, the dynamic range of most commercial digital cameras and display devices are smaller than the dynamic range of human eye. This implies low dynamic range (LDR) images captured by normal digital camera may lose image details. We propose an efficient approach to high dynamic (HDR) image fusion that copes with image displacement and image blur degradation in a computationally efficient manner, which is suitable for implementation on mobile devices. The various image registration algorithms proposed in the previous literatures are unable to meet the efficiency and performance requirements in the application of mobile devices. In this paper, we selected Oriented Brief (ORB) detector to extract local image structures. The descriptor selected in multi-exposure image fusion algorithm has to be fast and robust to illumination variations and geometric deformations. ORB descriptor is the best candidate in our algorithm. Further, we perform an improved RANdom Sample Consensus (RANSAC) algorithm to reject incorrect matches. For the fusion of images, a new approach based on Stationary Wavelet Transform (SWT) is used. The experimental results demonstrate that the proposed algorithm generates high quality images at low computational cost. Comparisons with a number of other feature matching methods show that our method gets better performance.

Endovascular Electrodes for Electrical Stimulation of Blood Vessels for Vasoconstriction – a Finite Element Simulation Study

PubMed Central

Kezurer, Noa; Farah, Nairouz; Mandel, Yossi

2016-01-01

Hemorrhagic shock accounts for 30–40 percent of trauma mortality, as bleeding may sometimes be hard to control. Application of short electrical pulses on blood vessels was recently shown to elicit robust vasoconstriction and reduction of blood loss following vascular injury. In this study we present a novel approach for vasoconstriction based on endovascular application of electrical pulses for situations where access to the vessel is limited. In addition to ease of access, we hypothesize that this novel approach will result in a localized and efficient vasoconstriction. Using computer modeling (COMSOL Multiphysics, Electric Currents Module), we studied the effect of endovascular pulsed electrical treatment on abdominal aorta of pigs, and compared the efficiency of different electrodes configurations on the electric field amplitude, homogeneity and locality when applied on a blood vessel wall. Results reveal that the optimal configuration is the endovascular approach where four electrodes are used, spaced 13 mm apart. Furthermore, computer based temperature investigations (bio-heat model, COMSOL Multiphysics) show that the maximum expected temperature rise is of 1.2 degrees; highlighting the safety of the four endovascular electrodes configuration. These results can aid in planning the application of endovascular pulsed electrical treatment as an efficient and safe vasoconstriction approach. PMID:27534438

Robust guaranteed-cost adaptive quantum phase estimation

NASA Astrophysics Data System (ADS)

Roy, Shibdas; Berry, Dominic W.; Petersen, Ian R.; Huntington, Elanor H.

2017-05-01

Quantum parameter estimation plays a key role in many fields like quantum computation, communication, and metrology. Optimal estimation allows one to achieve the most precise parameter estimates, but requires accurate knowledge of the model. Any inevitable uncertainty in the model parameters may heavily degrade the quality of the estimate. It is therefore desired to make the estimation process robust to such uncertainties. Robust estimation was previously studied for a varying phase, where the goal was to estimate the phase at some time in the past, using the measurement results from both before and after that time within a fixed time interval up to current time. Here, we consider a robust guaranteed-cost filter yielding robust estimates of a varying phase in real time, where the current phase is estimated using only past measurements. Our filter minimizes the largest (worst-case) variance in the allowable range of the uncertain model parameter(s) and this determines its guaranteed cost. It outperforms in the worst case the optimal Kalman filter designed for the model with no uncertainty, which corresponds to the center of the possible range of the uncertain parameter(s). Moreover, unlike the Kalman filter, our filter in the worst case always performs better than the best achievable variance for heterodyne measurements, which we consider as the tolerable threshold for our system. Furthermore, we consider effective quantum efficiency and effective noise power, and show that our filter provides the best results by these measures in the worst case.

Efficient simulation and model reformulation of two-dimensional electrochemical thermal behavior of lithium-ion batteries

DOE PAGES

Northrop, Paul W. C.; Pathak, Manan; Rife, Derek; ...

2015-03-09

Lithium-ion batteries are an important technology to facilitate efficient energy storage and enable a shift from petroleum based energy to more environmentally benign sources. Such systems can be utilized most efficiently if good understanding of performance can be achieved for a range of operating conditions. Mathematical models can be useful to predict battery behavior to allow for optimization of design and control. An analytical solution is ideally preferred to solve the equations of a mathematical model, as it eliminates the error that arises when using numerical techniques and is usually computationally cheap. An analytical solution provides insight into the behaviormore » of the system and also explicitly shows the effects of different parameters on the behavior. However, most engineering models, including the majority of battery models, cannot be solved analytically due to non-linearities in the equations and state dependent transport and kinetic parameters. The numerical method used to solve the system of equations describing a battery operation can have a significant impact on the computational cost of the simulation. In this paper, a model reformulation of the porous electrode pseudo three dimensional (P3D) which significantly reduces the computational cost of lithium ion battery simulation, while maintaining high accuracy, is discussed. This reformulation enables the use of the P3D model into applications that would otherwise be too computationally expensive to justify its use, such as online control, optimization, and parameter estimation. Furthermore, the P3D model has proven to be robust enough to allow for the inclusion of additional physical phenomena as understanding improves. In this study, the reformulated model is used to allow for more complicated physical phenomena to be considered for study, including thermal effects.« less

Covalently bound DNA on naked iron oxide nanoparticles: Intelligent colloidal nano-vector for cell transfection.

PubMed

Magro, Massimiliano; Martinello, Tiziana; Bonaiuto, Emanuela; Gomiero, Chiara; Baratella, Davide; Zoppellaro, Giorgio; Cozza, Giorgio; Patruno, Marco; Zboril, Radek; Vianello, Fabio

2017-11-01

Conversely to common coated iron oxide nanoparticles, novel naked surface active maghemite nanoparticles (SAMNs) can covalently bind DNA. Plasmid (pDNA) harboring the coding gene for GFP was directly chemisorbed onto SAMNs, leading to a novel DNA nanovector (SAMN@pDNA). The spontaneous internalization of SAMN@pDNA into cells was compared with an extensively studied fluorescent SAMN derivative (SAMN@RITC). Moreover, the transfection efficiency of SAMN@pDNA was evaluated and explained by computational model. SAMN@pDNA was prepared and characterized by spectroscopic and computational methods, and molecular dynamic simulation. The size and hydrodynamic properties of SAMN@pDNA and SAMN@RITC were studied by electron transmission microscopy, light scattering and zeta-potential. The two nanomaterials were tested by confocal scanning microscopy on equine peripheral blood-derived mesenchymal stem cells (ePB-MSCs) and GFP expression by SAMN@pDNA was determined. Nanomaterials characterized by similar hydrodynamic properties were successfully internalized and stored into mesenchymal stem cells. Transfection by SAMN@pDNA occurred and GFP expression was higher than lipofectamine procedure, even in the absence of an external magnetic field. A computational model clarified that transfection efficiency can be ascribed to DNA availability inside cells. Direct covalent binding of DNA on naked magnetic nanoparticles led to an extremely robust gene delivery tool. Hydrodynamic and chemical-physical properties of SAMN@pDNA were responsible of the successful uptake by cells and of the efficiency of GFP gene transfection. SAMNs are characterized by colloidal stability, excellent cell uptake, persistence in the host cells, low toxicity and are proposed as novel intelligent DNA nanovectors for efficient cell transfection. Copyright © 2017 Elsevier B.V. All rights reserved.

TARCMO: Theory and Algorithms for Robust, Combinatorial, Multicriteria Optimization

DTIC Science & Technology

2016-11-28

objective 9 4.6 On The Recoverable Robust Traveling Salesman Problem . . . . . 11 4.7 A Bicriteria Approach to Robust Optimization...be found. 4.6 On The Recoverable Robust Traveling Salesman Problem The traveling salesman problem (TSP) is a well-known combinatorial optimiza- tion...procedure for the robust traveling salesman problem . While this iterative algorithms results in an optimal solution to the robust TSP, computation

Rapid Global Fitting of Large Fluorescence Lifetime Imaging Microscopy Datasets

PubMed Central

Warren, Sean C.; Margineanu, Anca; Alibhai, Dominic; Kelly, Douglas J.; Talbot, Clifford; Alexandrov, Yuriy; Munro, Ian; Katan, Matilda

2013-01-01

Fluorescence lifetime imaging (FLIM) is widely applied to obtain quantitative information from fluorescence signals, particularly using Förster Resonant Energy Transfer (FRET) measurements to map, for example, protein-protein interactions. Extracting FRET efficiencies or population fractions typically entails fitting data to complex fluorescence decay models but such experiments are frequently photon constrained, particularly for live cell or in vivo imaging, and this leads to unacceptable errors when analysing data on a pixel-wise basis. Lifetimes and population fractions may, however, be more robustly extracted using global analysis to simultaneously fit the fluorescence decay data of all pixels in an image or dataset to a multi-exponential model under the assumption that the lifetime components are invariant across the image (dataset). This approach is often considered to be prohibitively slow and/or computationally expensive but we present here a computationally efficient global analysis algorithm for the analysis of time-correlated single photon counting (TCSPC) or time-gated FLIM data based on variable projection. It makes efficient use of both computer processor and memory resources, requiring less than a minute to analyse time series and multiwell plate datasets with hundreds of FLIM images on standard personal computers. This lifetime analysis takes account of repetitive excitation, including fluorescence photons excited by earlier pulses contributing to the fit, and is able to accommodate time-varying backgrounds and instrument response functions. We demonstrate that this global approach allows us to readily fit time-resolved fluorescence data to complex models including a four-exponential model of a FRET system, for which the FRET efficiencies of the two species of a bi-exponential donor are linked, and polarisation-resolved lifetime data, where a fluorescence intensity and bi-exponential anisotropy decay model is applied to the analysis of live cell homo-FRET data. A software package implementing this algorithm, FLIMfit, is available under an open source licence through the Open Microscopy Environment. PMID:23940626

A Comparative Theoretical and Computational Study on Robust Counterpart Optimization: II. Probabilistic Guarantees on Constraint Satisfaction

PubMed Central

Li, Zukui; Floudas, Christodoulos A.

2012-01-01

Probabilistic guarantees on constraint satisfaction for robust counterpart optimization are studied in this paper. The robust counterpart optimization formulations studied are derived from box, ellipsoidal, polyhedral, “interval+ellipsoidal” and “interval+polyhedral” uncertainty sets (Li, Z., Ding, R., and Floudas, C.A., A Comparative Theoretical and Computational Study on Robust Counterpart Optimization: I. Robust Linear and Robust Mixed Integer Linear Optimization, Ind. Eng. Chem. Res, 2011, 50, 10567). For those robust counterpart optimization formulations, their corresponding probability bounds on constraint satisfaction are derived for different types of uncertainty characteristic (i.e., bounded or unbounded uncertainty, with or without detailed probability distribution information). The findings of this work extend the results in the literature and provide greater flexibility for robust optimization practitioners in choosing tighter probability bounds so as to find less conservative robust solutions. Extensive numerical studies are performed to compare the tightness of the different probability bounds and the conservatism of different robust counterpart optimization formulations. Guiding rules for the selection of robust counterpart optimization models and for the determination of the size of the uncertainty set are discussed. Applications in production planning and process scheduling problems are presented. PMID:23329868

A Robust Cooperated Control Method with Reinforcement Learning and Adaptive H∞ Control

NASA Astrophysics Data System (ADS)

Obayashi, Masanao; Uchiyama, Shogo; Kuremoto, Takashi; Kobayashi, Kunikazu

This study proposes a robust cooperated control method combining reinforcement learning with robust control to control the system. A remarkable characteristic of the reinforcement learning is that it doesn't require model formula, however, it doesn't guarantee the stability of the system. On the other hand, robust control system guarantees stability and robustness, however, it requires model formula. We employ both the actor-critic method which is a kind of reinforcement learning with minimal amount of computation to control continuous valued actions and the traditional robust control, that is, H∞ control. The proposed system was compared method with the conventional control method, that is, the actor-critic only used, through the computer simulation of controlling the angle and the position of a crane system, and the simulation result showed the effectiveness of the proposed method.

3D shape recovery from image focus using gray level co-occurrence matrix

NASA Astrophysics Data System (ADS)

Mahmood, Fahad; Munir, Umair; Mehmood, Fahad; Iqbal, Javaid

2018-04-01

Recovering a precise and accurate 3-D shape of the target object utilizing robust 3-D shape recovery algorithm is an ultimate objective of computer vision community. Focus measure algorithm plays an important role in this architecture which convert the color values of each pixel of the acquired 2-D image dataset into corresponding focus values. After convolving the focus measure filter with the input 2-D image dataset, a 3-D shape recovery approach is applied which will recover the depth map. In this document, we are concerned with proposing Gray Level Co-occurrence Matrix along with its statistical features for computing the focus information of the image dataset. The Gray Level Co-occurrence Matrix quantifies the texture present in the image using statistical features and then applies joint probability distributive function of the gray level pairs of the input image. Finally, we quantify the focus value of the input image using Gaussian Mixture Model. Due to its little computational complexity, sharp focus measure curve, robust to random noise sources and accuracy, it is considered as superior alternative to most of recently proposed 3-D shape recovery approaches. This algorithm is deeply investigated on real image sequences and synthetic image dataset. The efficiency of the proposed scheme is also compared with the state of art 3-D shape recovery approaches. Finally, by means of two global statistical measures, root mean square error and correlation, we claim that this approach -in spite of simplicity generates accurate results.

Learning-based image preprocessing for robust computer-aided detection

NASA Astrophysics Data System (ADS)

Raghupathi, Laks; Devarakota, Pandu R.; Wolf, Matthias

2013-03-01

Recent studies have shown that low dose computed tomography (LDCT) can be an effective screening tool to reduce lung cancer mortality. Computer-aided detection (CAD) would be a beneficial second reader for radiologists in such cases. Studies demonstrate that while iterative reconstructions (IR) improve LDCT diagnostic quality, it however degrades CAD performance significantly (increased false positives) when applied directly. For improving CAD performance, solutions such as retraining with newer data or applying a standard preprocessing technique may not be suffice due to high prevalence of CT scanners and non-uniform acquisition protocols. Here, we present a learning-based framework that can adaptively transform a wide variety of input data to boost an existing CAD performance. This not only enhances their robustness but also their applicability in clinical workflows. Our solution consists of applying a suitable pre-processing filter automatically on the given image based on its characteristics. This requires the preparation of ground truth (GT) of choosing an appropriate filter resulting in improved CAD performance. Accordingly, we propose an efficient consolidation process with a novel metric. Using key anatomical landmarks, we then derive consistent feature descriptors for the classification scheme that then uses a priority mechanism to automatically choose an optimal preprocessing filter. We demonstrate CAD prototype∗ performance improvement using hospital-scale datasets acquired from North America, Europe and Asia. Though we demonstrated our results for a lung nodule CAD, this scheme is straightforward to extend to other post-processing tools dedicated to other organs and modalities.

Joint estimation of motion and illumination change in a sequence of images

NASA Astrophysics Data System (ADS)

Koo, Ja-Keoung; Kim, Hyo-Hun; Hong, Byung-Woo

2015-09-01

We present an algorithm that simultaneously computes optical flow and estimates illumination change from an image sequence in a unified framework. We propose an energy functional consisting of conventional optical flow energy based on Horn-Schunck method and an additional constraint that is designed to compensate for illumination changes. Any undesirable illumination change that occurs in the imaging procedure in a sequence while the optical flow is being computed is considered a nuisance factor. In contrast to the conventional optical flow algorithm based on Horn-Schunck functional, which assumes the brightness constancy constraint, our algorithm is shown to be robust with respect to temporal illumination changes in the computation of optical flows. An efficient conjugate gradient descent technique is used in the optimization procedure as a numerical scheme. The experimental results obtained from the Middlebury benchmark dataset demonstrate the robustness and the effectiveness of our algorithm. In addition, comparative analysis of our algorithm and Horn-Schunck algorithm is performed on the additional test dataset that is constructed by applying a variety of synthetic bias fields to the original image sequences in the Middlebury benchmark dataset in order to demonstrate that our algorithm outperforms the Horn-Schunck algorithm. The superior performance of the proposed method is observed in terms of both qualitative visualizations and quantitative accuracy errors when compared to Horn-Schunck optical flow algorithm that easily yields poor results in the presence of small illumination changes leading to violation of the brightness constancy constraint.

Inducer analysis/pump model development

NASA Astrophysics Data System (ADS)

Cheng, Gary C.

1994-03-01

Current design of high performance turbopumps for rocket engines requires effective and robust analytical tools to provide design information in a productive manner. The main goal of this study was to develop a robust and effective computational fluid dynamics (CFD) pump model for general turbopump design and analysis applications. A finite difference Navier-Stokes flow solver, FDNS, which includes an extended k-epsilon turbulence model and appropriate moving zonal interface boundary conditions, was developed to analyze turbulent flows in turbomachinery devices. In the present study, three key components of the turbopump, the inducer, impeller, and diffuser, were investigated by the proposed pump model, and the numerical results were benchmarked by the experimental data provided by Rocketdyne. For the numerical calculation of inducer flows with tip clearance, the turbulence model and grid spacing are very important. Meanwhile, the development of the cross-stream secondary flow, generated by curved blade passage and the flow through tip leakage, has a strong effect on the inducer flow. Hence, the prediction of the inducer performance critically depends on whether the numerical scheme of the pump model can simulate the secondary flow pattern accurately or not. The impeller and diffuser, however, are dominated by pressure-driven flows such that the effects of turbulence model and grid spacing (except near leading and trailing edges of blades) are less sensitive. The present CFD pump model has been proved to be an efficient and robust analytical tool for pump design due to its very compact numerical structure (requiring small memory), fast turnaround computing time, and versatility for different geometries.

Inducer analysis/pump model development

NASA Technical Reports Server (NTRS)

Cheng, Gary C.

1994-01-01

Current design of high performance turbopumps for rocket engines requires effective and robust analytical tools to provide design information in a productive manner. The main goal of this study was to develop a robust and effective computational fluid dynamics (CFD) pump model for general turbopump design and analysis applications. A finite difference Navier-Stokes flow solver, FDNS, which includes an extended k-epsilon turbulence model and appropriate moving zonal interface boundary conditions, was developed to analyze turbulent flows in turbomachinery devices. In the present study, three key components of the turbopump, the inducer, impeller, and diffuser, were investigated by the proposed pump model, and the numerical results were benchmarked by the experimental data provided by Rocketdyne. For the numerical calculation of inducer flows with tip clearance, the turbulence model and grid spacing are very important. Meanwhile, the development of the cross-stream secondary flow, generated by curved blade passage and the flow through tip leakage, has a strong effect on the inducer flow. Hence, the prediction of the inducer performance critically depends on whether the numerical scheme of the pump model can simulate the secondary flow pattern accurately or not. The impeller and diffuser, however, are dominated by pressure-driven flows such that the effects of turbulence model and grid spacing (except near leading and trailing edges of blades) are less sensitive. The present CFD pump model has been proved to be an efficient and robust analytical tool for pump design due to its very compact numerical structure (requiring small memory), fast turnaround computing time, and versatility for different geometries.

Worst-Case Flutter Margins from F/A-18 Aircraft Aeroelastic Data

NASA Technical Reports Server (NTRS)

Lind, Rick; Brenner, Marty

1997-01-01

An approach for computing worst-case flutter margins has been formulated in a robust stability framework. Uncertainty operators are included with a linear model to describe modeling errors and flight variations. The structured singular value, micron, computes a stability margin which directly accounts for these uncertainties. This approach introduces a new method of computing flutter margins and an associated new parameter for describing these margins. The micron margins are robust margins which indicate worst-case stability estimates with respect to the defined uncertainty. Worst-case flutter margins are computed for the F/A-18 SRA using uncertainty sets generated by flight data analysis. The robust margins demonstrate flight conditions for flutter may lie closer to the flight envelope than previously estimated by p-k analysis.

Phase-unwrapping algorithm by a rounding-least-squares approach

NASA Astrophysics Data System (ADS)

Juarez-Salazar, Rigoberto; Robledo-Sanchez, Carlos; Guerrero-Sanchez, Fermin

2014-02-01

A simple and efficient phase-unwrapping algorithm based on a rounding procedure and a global least-squares minimization is proposed. Instead of processing the gradient of the wrapped phase, this algorithm operates over the gradient of the phase jumps by a robust and noniterative scheme. Thus, the residue-spreading and over-smoothing effects are reduced. The algorithm's performance is compared with four well-known phase-unwrapping methods: minimum cost network flow (MCNF), fast Fourier transform (FFT), quality-guided, and branch-cut. A computer simulation and experimental results show that the proposed algorithm reaches a high-accuracy level than the MCNF method by a low-computing time similar to the FFT phase-unwrapping method. Moreover, since the proposed algorithm is simple, fast, and user-free, it could be used in metrological interferometric and fringe-projection automatic real-time applications.

The development of a virtual camera system for astronaut-rover planetary exploration.

PubMed

Platt, Donald W; Boy, Guy A

2012-01-01

A virtual assistant is being developed for use by astronauts as they use rovers to explore the surface of other planets. This interactive database, called the Virtual Camera (VC), is an interactive database that allows the user to have better situational awareness for exploration. It can be used for training, data analysis and augmentation of actual surface exploration. This paper describes the development efforts and Human-Computer Interaction considerations for implementing a first-generation VC on a tablet mobile computer device. Scenarios for use will be presented. Evaluation and success criteria such as efficiency in terms of processing time and precision situational awareness, learnability, usability, and robustness will also be presented. Initial testing and the impact of HCI design considerations of manipulation and improvement in situational awareness using a prototype VC will be discussed.

Computational methods for yeast prion curing curves.

PubMed

Ridout, Martin S

2008-10-01

If the chemical guanidine hydrochloride is added to a dividing culture of yeast cells in which some of the protein Sup35p is in its prion form, the proportion of cells that carry replicating units of the prion, termed propagons, decreases gradually over time. Stochastic models to describe this process of 'curing' have been developed in earlier work. The present paper investigates the use of numerical methods of Laplace transform inversion to calculate curing curves and contrasts this with an alternative, more direct, approach that involves numerical integration. Transform inversion is found to provide a much more efficient computational approach that allows different models to be investigated with minimal programming effort. The method is used to investigate the robustness of the curing curve to changes in the assumed distribution of cell generation times. Matlab code is available for carrying out the calculations.

Machine learning bandgaps of double perovskites

DOE PAGES

Pilania, G.; Mannodi-Kanakkithodi, A.; Uberuaga, B. P.; ...

2016-01-19

The ability to make rapid and accurate predictions on bandgaps of double perovskites is of much practical interest for a range of applications. While quantum mechanical computations for high-fidelity bandgaps are enormously computation-time intensive and thus impractical in high throughput studies, informatics-based statistical learning approaches can be a promising alternative. Here we demonstrate a systematic feature-engineering approach and a robust learning framework for efficient and accurate predictions of electronic bandgaps of double perovskites. After evaluating a set of more than 1.2 million features, we identify lowest occupied Kohn-Sham levels and elemental electronegativities of the constituent atomic species as the mostmore » crucial and relevant predictors. As a result, the developed models are validated and tested using the best practices of data science and further analyzed to rationalize their prediction performance.« less

A robust and scalable neuromorphic communication system by combining synaptic time multiplexing and MIMO-OFDM.

PubMed

Srinivasa, Narayan; Zhang, Deying; Grigorian, Beayna

2014-03-01

This paper describes a novel architecture for enabling robust and efficient neuromorphic communication. The architecture combines two concepts: 1) synaptic time multiplexing (STM) that trades space for speed of processing to create an intragroup communication approach that is firing rate independent and offers more flexibility in connectivity than cross-bar architectures and 2) a wired multiple input multiple output (MIMO) communication with orthogonal frequency division multiplexing (OFDM) techniques to enable a robust and efficient intergroup communication for neuromorphic systems. The MIMO-OFDM concept for the proposed architecture was analyzed by simulating large-scale spiking neural network architecture. Analysis shows that the neuromorphic system with MIMO-OFDM exhibits robust and efficient communication while operating in real time with a high bit rate. Through combining STM with MIMO-OFDM techniques, the resulting system offers a flexible and scalable connectivity as well as a power and area efficient solution for the implementation of very large-scale spiking neural architectures in hardware.

The Problem of Size in Robust Design

NASA Technical Reports Server (NTRS)

Koch, Patrick N.; Allen, Janet K.; Mistree, Farrokh; Mavris, Dimitri

1997-01-01

To facilitate the effective solution of multidisciplinary, multiobjective complex design problems, a departure from the traditional parametric design analysis and single objective optimization approaches is necessary in the preliminary stages of design. A necessary tradeoff becomes one of efficiency vs. accuracy as approximate models are sought to allow fast analysis and effective exploration of a preliminary design space. In this paper we apply a general robust design approach for efficient and comprehensive preliminary design to a large complex system: a high speed civil transport (HSCT) aircraft. Specifically, we investigate the HSCT wing configuration design, incorporating life cycle economic uncertainties to identify economically robust solutions. The approach is built on the foundation of statistical experimentation and modeling techniques and robust design principles, and is specialized through incorporation of the compromise Decision Support Problem for multiobjective design. For large problems however, as in the HSCT example, this robust design approach developed for efficient and comprehensive design breaks down with the problem of size - combinatorial explosion in experimentation and model building with number of variables -and both efficiency and accuracy are sacrificed. Our focus in this paper is on identifying and discussing the implications and open issues associated with the problem of size for the preliminary design of large complex systems.

On fair, effective and efficient REDD mechanism design

PubMed Central

2009-01-01

The issues surrounding 'Reduced Emissions from Deforestation and Forest Degradation' (REDD) have become a major component of continuing negotiations under the United Nations Framework Convention on Climate Change (UNFCCC). This paper aims to address two key requirements of any potential REDD mechanism: first, the generation of measurable, reportable and verifiable (MRV) REDD credits; and secondly, the sustainable and efficient provision of emission reductions under a robust financing regime. To ensure the supply of MRV credits, we advocate the establishment of an 'International Emission Reference Scenario Coordination Centre' (IERSCC). The IERSCC would act as a global clearing house for harmonized data to be used in implementing reference level methodologies. It would be tasked with the collection, reporting and subsequent processing of earth observation, deforestation- and degradation driver information in a globally consistent manner. The IERSCC would also assist, coordinate and supervise the computation of national reference scenarios according to rules negotiated under the UNFCCC. To overcome the threats of "market flooding" on the one hand and insufficient economic incentives for REDD on the other hand, we suggest an 'International Investment Reserve' (IIR) as REDD financing framework. In order to distribute the resources of the IIR we propose adopting an auctioning mechanism. Auctioning not only reveals the true emission reduction costs, but might also allow for incentivizing the protection of biodiversity and socio-economic values. The introduced concepts will be vital to ensure robustness, environmental integrity and economic efficiency of the future REDD mechanism. PMID:19943927

Efficient structure from motion on large scenes using UAV with position and pose information

NASA Astrophysics Data System (ADS)

Teng, Xichao; Yu, Qifeng; Shang, Yang; Luo, Jing; Wang, Gang

2018-04-01

In this paper, we exploit prior information from global positioning systems and inertial measurement units to speed up the process of large scene reconstruction from images acquired by Unmanned Aerial Vehicles. We utilize weak pose information and intrinsic parameter to obtain the projection matrix for each view. As compared to unmanned aerial vehicles' flight altitude, topographic relief can usually be ignored, we assume that the scene is flat and use weak perspective camera to get projective transformations between two views. Furthermore, we propose an overlap criterion and select potentially matching view pairs between projective transformed views. A robust global structure from motion method is used for image based reconstruction. Our real world experiments show that the approach is accurate, scalable and computationally efficient. Moreover, projective transformations between views can also be used to eliminate false matching.

Redundant interferometric calibration as a complex optimization problem

NASA Astrophysics Data System (ADS)

Grobler, T. L.; Bernardi, G.; Kenyon, J. S.; Parsons, A. R.; Smirnov, O. M.

2018-05-01

Observations of the redshifted 21 cm line from the epoch of reionization have recently motivated the construction of low-frequency radio arrays with highly redundant configurations. These configurations provide an alternative calibration strategy - `redundant calibration' - and boost sensitivity on specific spatial scales. In this paper, we formulate calibration of redundant interferometric arrays as a complex optimization problem. We solve this optimization problem via the Levenberg-Marquardt algorithm. This calibration approach is more robust to initial conditions than current algorithms and, by leveraging an approximate matrix inversion, allows for further optimization and an efficient implementation (`redundant STEFCAL'). We also investigated using the preconditioned conjugate gradient method as an alternative to the approximate matrix inverse, but found that its computational performance is not competitive with respect to `redundant STEFCAL'. The efficient implementation of this new algorithm is made publicly available.

Optical properties reconstruction using the adjoint method based on the radiative transfer equation

NASA Astrophysics Data System (ADS)

Addoum, Ahmad; Farges, Olivier; Asllanaj, Fatmir

2018-01-01

An efficient algorithm is proposed to reconstruct the spatial distribution of optical properties in heterogeneous media like biological tissues. The light transport through such media is accurately described by the radiative transfer equation in the frequency-domain. The adjoint method is used to efficiently compute the objective function gradient with respect to optical parameters. Numerical tests show that the algorithm is accurate and robust to retrieve simultaneously the absorption μa and scattering μs coefficients for lowly and highly absorbing medium. Moreover, the simultaneous reconstruction of μs and the anisotropy factor g of the Henyey-Greenstein phase function is achieved with a reasonable accuracy. The main novelty in this work is the reconstruction of g which might open the possibility to image this parameter in tissues as an additional contrast agent in optical tomography.

Modeling of an intelligent pressure sensor using functional link artificial neural networks.

PubMed

Patra, J C; van den Bos, A

2000-01-01

A capacitor pressure sensor (CPS) is modeled for accurate readout of applied pressure using a novel artificial neural network (ANN). The proposed functional link ANN (FLANN) is a computationally efficient nonlinear network and is capable of complex nonlinear mapping between its input and output pattern space. The nonlinearity is introduced into the FLANN by passing the input pattern through a functional expansion unit. Three different polynomials such as, Chebyschev, Legendre and power series have been employed in the FLANN. The FLANN offers computational advantage over a multilayer perceptron (MLP) for similar performance in modeling of the CPS. The prime aim of the present paper is to develop an intelligent model of the CPS involving less computational complexity, so that its implementation can be economical and robust. It is shown that, over a wide temperature variation ranging from -50 to 150 degrees C, the maximum error of estimation of pressure remains within +/- 3%. With the help of computer simulation, the performance of the three types of FLANN models has been compared to that of an MLP based model.

Probabilistic inference in discrete spaces can be implemented into networks of LIF neurons.

PubMed

Probst, Dimitri; Petrovici, Mihai A; Bytschok, Ilja; Bill, Johannes; Pecevski, Dejan; Schemmel, Johannes; Meier, Karlheinz

2015-01-01

The means by which cortical neural networks are able to efficiently solve inference problems remains an open question in computational neuroscience. Recently, abstract models of Bayesian computation in neural circuits have been proposed, but they lack a mechanistic interpretation at the single-cell level. In this article, we describe a complete theoretical framework for building networks of leaky integrate-and-fire neurons that can sample from arbitrary probability distributions over binary random variables. We test our framework for a model inference task based on a psychophysical phenomenon (the Knill-Kersten optical illusion) and further assess its performance when applied to randomly generated distributions. As the local computations performed by the network strongly depend on the interaction between neurons, we compare several types of couplings mediated by either single synapses or interneuron chains. Due to its robustness to substrate imperfections such as parameter noise and background noise correlations, our model is particularly interesting for implementation on novel, neuro-inspired computing architectures, which can thereby serve as a fast, low-power substrate for solving real-world inference problems.

Algorithms for the computation of solutions of the Ornstein-Zernike equation.

PubMed

Peplow, A T; Beardmore, R E; Bresme, F

2006-10-01

We introduce a robust and efficient methodology to solve the Ornstein-Zernike integral equation using the pseudoarc length (PAL) continuation method that reformulates the integral equation in an equivalent but nonstandard form. This enables the computation of solutions in regions where the compressibility experiences large changes or where the existence of multiple solutions and so-called branch points prevents Newton's method from converging. We illustrate the use of the algorithm with a difficult problem that arises in the numerical solution of integral equations, namely the evaluation of the so-called no-solution line of the Ornstein-Zernike hypernetted chain (HNC) integral equation for the Lennard-Jones potential. We are able to use the PAL algorithm to solve the integral equation along this line and to connect physical and nonphysical solution branches (both isotherms and isochores) where appropriate. We also show that PAL continuation can compute solutions within the no-solution region that cannot be computed when Newton and Picard methods are applied directly to the integral equation. While many solutions that we find are new, some correspond to states with negative compressibility and consequently are not physical.