Fractal Dimension Analysis of Transient Visual Evoked Potentials: Optimisation and Applications.

PubMed

Boon, Mei Ying; Henry, Bruce Ian; Chu, Byoung Sun; Basahi, Nour; Suttle, Catherine May; Luu, Chi; Leung, Harry; Hing, Stephen

2016-01-01

The visual evoked potential (VEP) provides a time series signal response to an external visual stimulus at the location of the visual cortex. The major VEP signal components, peak latency and amplitude, may be affected by disease processes. Additionally, the VEP contains fine detailed and non-periodic structure, of presently unclear relevance to normal function, which may be quantified using the fractal dimension. The purpose of this study is to provide a systematic investigation of the key parameters in the measurement of the fractal dimension of VEPs, to develop an optimal analysis protocol for application. VEP time series were mathematically transformed using delay time, τ, and embedding dimension, m, parameters. The fractal dimension of the transformed data was obtained from a scaling analysis based on straight line fits to the numbers of pairs of points with separation less than r versus log(r) in the transformed space. Optimal τ, m, and scaling analysis were obtained by comparing the consistency of results using different sampling frequencies. The optimised method was then piloted on samples of normal and abnormal VEPs. Consistent fractal dimension estimates were obtained using τ = 4 ms, designating the fractal dimension = D2 of the time series based on embedding dimension m = 7 (for 3606 Hz and 5000 Hz), m = 6 (for 1803 Hz) and m = 5 (for 1000Hz), and estimating D2 for each embedding dimension as the steepest slope of the linear scaling region in the plot of log(C(r)) vs log(r) provided the scaling region occurred within the middle third of the plot. Piloting revealed that fractal dimensions were higher from the sampled abnormal than normal achromatic VEPs in adults (p = 0.02). Variances of fractal dimension were higher from the abnormal than normal chromatic VEPs in children (p = 0.01). A useful analysis protocol to assess the fractal dimension of transformed VEPs has been developed.

Time-dependent vibrational spectral analysis of first principles trajectory of methylamine with wavelet transform.

PubMed

Biswas, Sohag; Mallik, Bhabani S

2017-04-12

The fluctuation dynamics of amine stretching frequencies, hydrogen bonds, dangling N-D bonds, and the orientation profile of the amine group of methylamine (MA) were investigated under ambient conditions by means of dispersion-corrected density functional theory-based first principles molecular dynamics (FPMD) simulations. Along with the dynamical properties, various equilibrium properties such as radial distribution function, spatial distribution function, combined radial and angular distribution functions and hydrogen bonding were also calculated. The instantaneous stretching frequencies of amine groups were obtained by wavelet transform of the trajectory obtained from FPMD simulations. The frequency-structure correlation reveals that the amine stretching frequency is weakly correlated with the nearest nitrogen-deuterium distance. The frequency-frequency correlation function has a short time scale of around 110 fs and a longer time scale of about 1.15 ps. It was found that the short time scale originates from the underdamped motion of intact hydrogen bonds of MA pairs. However, the long time scale of the vibrational spectral diffusion of N-D modes is determined by the overall dynamics of hydrogen bonds as well as the dangling ND groups and the inertial rotation of the amine group of the molecule.

Quantifying team cooperation through intrinsic multi-scale measures: respiratory and cardiac synchronization in choir singers and surgical teams.

PubMed

Hemakom, Apit; Powezka, Katarzyna; Goverdovsky, Valentin; Jaffer, Usman; Mandic, Danilo P

2017-12-01

A highly localized data-association measure, termed intrinsic synchrosqueezing transform (ISC), is proposed for the analysis of coupled nonlinear and non-stationary multivariate signals. This is achieved based on a combination of noise-assisted multivariate empirical mode decomposition and short-time Fourier transform-based univariate and multivariate synchrosqueezing transforms. It is shown that the ISC outperforms six other combinations of algorithms in estimating degrees of synchrony in synthetic linear and nonlinear bivariate signals. Its advantage is further illustrated in the precise identification of the synchronized respiratory and heart rate variability frequencies among a subset of bass singers of a professional choir, where it distinctly exhibits better performance than the continuous wavelet transform-based ISC. We also introduce an extension to the intrinsic phase synchrony (IPS) measure, referred to as nested intrinsic phase synchrony (N-IPS), for the empirical quantification of physically meaningful and straightforward-to-interpret trends in phase synchrony. The N-IPS is employed to reveal physically meaningful variations in the levels of cooperation in choir singing and performing a surgical procedure. Both the proposed techniques successfully reveal degrees of synchronization of the physiological signals in two different aspects: (i) precise localization of synchrony in time and frequency (ISC), and (ii) large-scale analysis for the empirical quantification of physically meaningful trends in synchrony (N-IPS).

Mobile robot motion estimation using Hough transform

NASA Astrophysics Data System (ADS)

Aldoshkin, D. N.; Yamskikh, T. N.; Tsarev, R. Yu

2018-05-01

This paper proposes an algorithm for estimation of mobile robot motion. The geometry of surrounding space is described with range scans (samples of distance measurements) taken by the mobile robot’s range sensors. A similar sample of space geometry in any arbitrary preceding moment of time or the environment map can be used as a reference. The suggested algorithm is invariant to isotropic scaling of samples or map that allows using samples measured in different units and maps made at different scales. The algorithm is based on Hough transform: it maps from measurement space to a straight-line parameters space. In the straight-line parameters, space the problems of estimating rotation, scaling and translation are solved separately breaking down a problem of estimating mobile robot localization into three smaller independent problems. The specific feature of the algorithm presented is its robustness to noise and outliers inherited from Hough transform. The prototype of the system of mobile robot orientation is described.

Optimal Control Modification Adaptive Law for Time-Scale Separated Systems

NASA Technical Reports Server (NTRS)

Nguyen, Nhan T.

2010-01-01

Recently a new optimal control modification has been introduced that can achieve robust adaptation with a large adaptive gain without incurring high-frequency oscillations as with the standard model-reference adaptive control. This modification is based on an optimal control formulation to minimize the L2 norm of the tracking error. The optimal control modification adaptive law results in a stable adaptation in the presence of a large adaptive gain. This study examines the optimal control modification adaptive law in the context of a system with a time scale separation resulting from a fast plant with a slow actuator. A singular perturbation analysis is performed to derive a modification to the adaptive law by transforming the original system into a reduced-order system in slow time. A model matching conditions in the transformed time coordinate results in an increase in the actuator command that effectively compensate for the slow actuator dynamics. Simulations demonstrate effectiveness of the method.

Optimal Control Modification for Time-Scale Separated Systems

NASA Technical Reports Server (NTRS)

Nguyen, Nhan T.

2012-01-01

Recently a new optimal control modification has been introduced that can achieve robust adaptation with a large adaptive gain without incurring high-frequency oscillations as with the standard model-reference adaptive control. This modification is based on an optimal control formulation to minimize the L2 norm of the tracking error. The optimal control modification adaptive law results in a stable adaptation in the presence of a large adaptive gain. This study examines the optimal control modification adaptive law in the context of a system with a time scale separation resulting from a fast plant with a slow actuator. A singular perturbation analysis is performed to derive a modification to the adaptive law by transforming the original system into a reduced-order system in slow time. A model matching conditions in the transformed time coordinate results in an increase in the actuator command that effectively compensate for the slow actuator dynamics. Simulations demonstrate effectiveness of the method.

Modelling accelerated degradation data using Wiener diffusion with a time scale transformation.

PubMed

Whitmore, G A; Schenkelberg, F

1997-01-01

Engineering degradation tests allow industry to assess the potential life span of long-life products that do not fail readily under accelerated conditions in life tests. A general statistical model is presented here for performance degradation of an item of equipment. The degradation process in the model is taken to be a Wiener diffusion process with a time scale transformation. The model incorporates Arrhenius extrapolation for high stress testing. The lifetime of an item is defined as the time until performance deteriorates to a specified failure threshold. The model can be used to predict the lifetime of an item or the extent of degradation of an item at a specified future time. Inference methods for the model parameters, based on accelerated degradation test data, are presented. The model and inference methods are illustrated with a case application involving self-regulating heating cables. The paper also discusses a number of practical issues encountered in applications.

Investigation of aquifer-estuary interaction using wavelet analysis of fiber-optic temperature data

USGS Publications Warehouse

Henderson, R.D.; Day-Lewis, Frederick D.; Harvey, Charles F.

2009-01-01

Fiber-optic distributed temperature sensing (FODTS) provides sub-minute temporal and meter-scale spatial resolution over kilometer-long cables. Compared to conventional thermistor or thermocouple-based technologies, which measure temperature at discrete (and commonly sparse) locations, FODTS offers nearly continuous spatial coverage, thus providing hydrologic information at spatiotemporal scales previously impossible. Large and information-rich FODTS datasets, however, pose challenges for data exploration and analysis. To date, FODTS analyses have focused on time-series variance as the means to discriminate between hydrologic phenomena. Here, we demonstrate the continuous wavelet transform (CWT) and cross-wavelet transform (XWT) to analyze FODTS in the context of related hydrologic time series. We apply the CWT and XWT to data from Waquoit Bay, Massachusetts to identify the location and timing of tidal pumping of submarine groundwater.

On Stabilizing the Variance of Dynamic Functional Brain Connectivity Time Series

PubMed Central

Fransson, Peter

2016-01-01

Abstract Assessment of dynamic functional brain connectivity based on functional magnetic resonance imaging (fMRI) data is an increasingly popular strategy to investigate temporal dynamics of the brain's large-scale network architecture. Current practice when deriving connectivity estimates over time is to use the Fisher transformation, which aims to stabilize the variance of correlation values that fluctuate around varying true correlation values. It is, however, unclear how well the stabilization of signal variance performed by the Fisher transformation works for each connectivity time series, when the true correlation is assumed to be fluctuating. This is of importance because many subsequent analyses either assume or perform better when the time series have stable variance or adheres to an approximate Gaussian distribution. In this article, using simulations and analysis of resting-state fMRI data, we analyze the effect of applying different variance stabilization strategies on connectivity time series. We focus our investigation on the Fisher transformation, the Box–Cox (BC) transformation and an approach that combines both transformations. Our results show that, if the intention of stabilizing the variance is to use metrics on the time series, where stable variance or a Gaussian distribution is desired (e.g., clustering), the Fisher transformation is not optimal and may even skew connectivity time series away from being Gaussian. Furthermore, we show that the suboptimal performance of the Fisher transformation can be substantially improved by including an additional BC transformation after the dynamic functional connectivity time series has been Fisher transformed. PMID:27784176

On Stabilizing the Variance of Dynamic Functional Brain Connectivity Time Series.

PubMed

Thompson, William Hedley; Fransson, Peter

2016-12-01

Assessment of dynamic functional brain connectivity based on functional magnetic resonance imaging (fMRI) data is an increasingly popular strategy to investigate temporal dynamics of the brain's large-scale network architecture. Current practice when deriving connectivity estimates over time is to use the Fisher transformation, which aims to stabilize the variance of correlation values that fluctuate around varying true correlation values. It is, however, unclear how well the stabilization of signal variance performed by the Fisher transformation works for each connectivity time series, when the true correlation is assumed to be fluctuating. This is of importance because many subsequent analyses either assume or perform better when the time series have stable variance or adheres to an approximate Gaussian distribution. In this article, using simulations and analysis of resting-state fMRI data, we analyze the effect of applying different variance stabilization strategies on connectivity time series. We focus our investigation on the Fisher transformation, the Box-Cox (BC) transformation and an approach that combines both transformations. Our results show that, if the intention of stabilizing the variance is to use metrics on the time series, where stable variance or a Gaussian distribution is desired (e.g., clustering), the Fisher transformation is not optimal and may even skew connectivity time series away from being Gaussian. Furthermore, we show that the suboptimal performance of the Fisher transformation can be substantially improved by including an additional BC transformation after the dynamic functional connectivity time series has been Fisher transformed.

Use of the Morlet mother wavelet in the frequency-scale domain decomposition technique for the modal identification of ambient vibration responses

NASA Astrophysics Data System (ADS)

Le, Thien-Phu

2017-10-01

The frequency-scale domain decomposition technique has recently been proposed for operational modal analysis. The technique is based on the Cauchy mother wavelet. In this paper, the approach is extended to the Morlet mother wavelet, which is very popular in signal processing due to its superior time-frequency localization. Based on the regressive form and an appropriate norm of the Morlet mother wavelet, the continuous wavelet transform of the power spectral density of ambient responses enables modes in the frequency-scale domain to be highlighted. Analytical developments first demonstrate the link between modal parameters and the local maxima of the continuous wavelet transform modulus. The link formula is then used as the foundation of the proposed modal identification method. Its practical procedure, combined with the singular value decomposition algorithm, is presented step by step. The proposition is finally verified using numerical examples and a laboratory test.

Stationary Wavelet-based Two-directional Two-dimensional Principal Component Analysis for EMG Signal Classification

NASA Astrophysics Data System (ADS)

Ji, Yi; Sun, Shanlin; Xie, Hong-Bo

2017-06-01

Discrete wavelet transform (WT) followed by principal component analysis (PCA) has been a powerful approach for the analysis of biomedical signals. Wavelet coefficients at various scales and channels were usually transformed into a one-dimensional array, causing issues such as the curse of dimensionality dilemma and small sample size problem. In addition, lack of time-shift invariance of WT coefficients can be modeled as noise and degrades the classifier performance. In this study, we present a stationary wavelet-based two-directional two-dimensional principal component analysis (SW2D2PCA) method for the efficient and effective extraction of essential feature information from signals. Time-invariant multi-scale matrices are constructed in the first step. The two-directional two-dimensional principal component analysis then operates on the multi-scale matrices to reduce the dimension, rather than vectors in conventional PCA. Results are presented from an experiment to classify eight hand motions using 4-channel electromyographic (EMG) signals recorded in healthy subjects and amputees, which illustrates the efficiency and effectiveness of the proposed method for biomedical signal analysis.

Statistical geometric affinity in human brain electric activity

NASA Astrophysics Data System (ADS)

Chornet-Lurbe, A.; Oteo, J. A.; Ros, J.

2007-05-01

The representation of the human electroencephalogram (EEG) records by neurophysiologists demands standardized time-amplitude scales for their correct conventional interpretation. In a suite of graphical experiments involving scaling affine transformations we have been able to convert electroencephalogram samples corresponding to any particular sleep phase and relaxed wakefulness into each other. We propound a statistical explanation for that finding in terms of data collapse. As a sequel, we determine characteristic time and amplitude scales and outline a possible physical interpretation. An analysis for characteristic times based on lacunarity is also carried out as well as a study of the synchrony between left and right EEG channels.

n-SIFT: n-dimensional scale invariant feature transform.

PubMed

Cheung, Warren; Hamarneh, Ghassan

2009-09-01

We propose the n-dimensional scale invariant feature transform (n-SIFT) method for extracting and matching salient features from scalar images of arbitrary dimensionality, and compare this method's performance to other related features. The proposed features extend the concepts used for 2-D scalar images in the computer vision SIFT technique for extracting and matching distinctive scale invariant features. We apply the features to images of arbitrary dimensionality through the use of hyperspherical coordinates for gradients and multidimensional histograms to create the feature vectors. We analyze the performance of a fully automated multimodal medical image matching technique based on these features, and successfully apply the technique to determine accurate feature point correspondence between pairs of 3-D MRI images and dynamic 3D + time CT data.

An improved KCF tracking algorithm based on multi-feature and multi-scale

NASA Astrophysics Data System (ADS)

Wu, Wei; Wang, Ding; Luo, Xin; Su, Yang; Tian, Weiye

2018-02-01

The purpose of visual tracking is to associate the target object in a continuous video frame. In recent years, the method based on the kernel correlation filter has become the research hotspot. However, the algorithm still has some problems such as video capture equipment fast jitter, tracking scale transformation. In order to improve the ability of scale transformation and feature description, this paper has carried an innovative algorithm based on the multi feature fusion and multi-scale transform. The experimental results show that our method solves the problem that the target model update when is blocked or its scale transforms. The accuracy of the evaluation (OPE) is 77.0%, 75.4% and the success rate is 69.7%, 66.4% on the VOT and OTB datasets. Compared with the optimal one of the existing target-based tracking algorithms, the accuracy of the algorithm is improved by 6.7% and 6.3% respectively. The success rates are improved by 13.7% and 14.2% respectively.

Non-stationary dynamics in the bouncing ball: A wavelet perspective

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

Behera, Abhinna K., E-mail: abhinna@iiserkol.ac.in; Panigrahi, Prasanta K., E-mail: pprasanta@iiserkol.ac.in; Sekar Iyengar, A. N., E-mail: ansekar.iyengar@saha.ac.in

2014-12-01

The non-stationary dynamics of a bouncing ball, comprising both periodic as well as chaotic behavior, is studied through wavelet transform. The multi-scale characterization of the time series displays clear signatures of self-similarity, complex scaling behavior, and periodicity. Self-similar behavior is quantified by the generalized Hurst exponent, obtained through both wavelet based multi-fractal detrended fluctuation analysis and Fourier methods. The scale dependent variable window size of the wavelets aptly captures both the transients and non-stationary periodic behavior, including the phase synchronization of different modes. The optimal time-frequency localization of the continuous Morlet wavelet is found to delineate the scales corresponding tomore » neutral turbulence, viscous dissipation regions, and different time varying periodic modulations.« less

Properties of the Magnitude Terms of Orthogonal Scaling Functions.

PubMed

Tay, Peter C; Havlicek, Joseph P; Acton, Scott T; Hossack, John A

2010-09-01

The spectrum of the convolution of two continuous functions can be determined as the continuous Fourier transform of the cross-correlation function. The same can be said about the spectrum of the convolution of two infinite discrete sequences, which can be determined as the discrete time Fourier transform of the cross-correlation function of the two sequences. In current digital signal processing, the spectrum of the contiuous Fourier transform and the discrete time Fourier transform are approximately determined by numerical integration or by densely taking the discrete Fourier transform. It has been shown that all three transforms share many analogous properties. In this paper we will show another useful property of determining the spectrum terms of the convolution of two finite length sequences by determining the discrete Fourier transform of the modified cross-correlation function. In addition, two properties of the magnitude terms of orthogonal wavelet scaling functions are developed. These properties are used as constraints for an exhaustive search to determine an robust lower bound on conjoint localization of orthogonal scaling functions.

Note: Tesla based pulse generator for electrical breakdown study of liquid dielectrics

NASA Astrophysics Data System (ADS)

Veda Prakash, G.; Kumar, R.; Patel, J.; Saurabh, K.; Shyam, A.

2013-12-01

In the process of studying charge holding capability and delay time for breakdown in liquids under nanosecond (ns) time scales, a Tesla based pulse generator has been developed. Pulse generator is a combination of Tesla transformer, pulse forming line, a fast closing switch, and test chamber. Use of Tesla transformer over conventional Marx generators makes the pulse generator very compact, cost effective, and requires less maintenance. The system has been designed and developed to deliver maximum output voltage of 300 kV and rise time of the order of tens of nanoseconds. The paper deals with the system design parameters, breakdown test procedure, and various experimental results. To validate the pulse generator performance, experimental results have been compared with PSPICE simulation software and are in good agreement with simulation results.

A real-time KLT implementation for radio-SETI applications

NASA Astrophysics Data System (ADS)

Melis, Andrea; Concu, Raimondo; Pari, Pierpaolo; Maccone, Claudio; Montebugnoli, Stelio; Possenti, Andrea; Valente, Giuseppe; Antonietti, Nicoló; Perrodin, Delphine; Migoni, Carlo; Murgia, Matteo; Trois, Alessio; Barbaro, Massimo; Bocchinu, Alessandro; Casu, Silvia; Lunesu, Maria Ilaria; Monari, Jader; Navarrini, Alessandro; Pisanu, Tonino; Schilliró, Francesco; Vacca, Valentina

2016-07-01

SETI, the Search for ExtraTerrestrial Intelligence, is the search for radio signals emitted by alien civilizations living in the Galaxy. Narrow-band FFT-based approaches have been preferred in SETI, since their computation time only grows like N*lnN, where N is the number of time samples. On the contrary, a wide-band approach based on the Kahrunen-Lo`eve Transform (KLT) algorithm would be preferable, but it would scale like N*N. In this paper, we describe a hardware-software infrastructure based on FPGA boards and GPU-based PCs that circumvents this computation-time problem allowing for a real-time KLT.

Identification of varying time scales in sediment transport using the Hilbert-Huang Transform method

NASA Astrophysics Data System (ADS)

Kuai, Ken Z.; Tsai, Christina W.

2012-02-01

SummarySediment transport processes vary at a variety of time scales - from seconds, hours, days to months and years. Multiple time scales exist in the system of flow, sediment transport and bed elevation change processes. As such, identification and selection of appropriate time scales for flow and sediment processes can assist in formulating a system of flow and sediment governing equations representative of the dynamic interaction of flow and particles at the desired details. Recognizing the importance of different varying time scales in the fluvial processes of sediment transport, we introduce the Hilbert-Huang Transform method (HHT) to the field of sediment transport for the time scale analysis. The HHT uses the Empirical Mode Decomposition (EMD) method to decompose a time series into a collection of the Intrinsic Mode Functions (IMFs), and uses the Hilbert Spectral Analysis (HSA) to obtain instantaneous frequency data. The EMD extracts the variability of data with different time scales, and improves the analysis of data series. The HSA can display the succession of time varying time scales, which cannot be captured by the often-used Fast Fourier Transform (FFT) method. This study is one of the earlier attempts to introduce the state-of-the-art technique for the multiple time sales analysis of sediment transport processes. Three practical applications of the HHT method for data analysis of both suspended sediment and bedload transport time series are presented. The analysis results show the strong impact of flood waves on the variations of flow and sediment time scales at a large sampling time scale, as well as the impact of flow turbulence on those time scales at a smaller sampling time scale. Our analysis reveals that the existence of multiple time scales in sediment transport processes may be attributed to the fractal nature in sediment transport. It can be demonstrated by the HHT analysis that the bedload motion time scale is better represented by the ratio of the water depth to the settling velocity, h/ w. In the final part, HHT results are compared with an available time scale formula in literature.

Integrated fringe projection 3D scanning system for large-scale metrology based on laser tracker

NASA Astrophysics Data System (ADS)

Du, Hui; Chen, Xiaobo; Zhou, Dan; Guo, Gen; Xi, Juntong

2017-10-01

Large scale components exist widely in advance manufacturing industry,3D profilometry plays a pivotal role for the quality control. This paper proposes a flexible, robust large-scale 3D scanning system by integrating a robot with a binocular structured light scanner and a laser tracker. The measurement principle and system construction of the integrated system are introduced. And a mathematical model is established for the global data fusion. Subsequently, a flexible and robust method and mechanism is introduced for the establishment of the end coordination system. Based on this method, a virtual robot noumenon is constructed for hand-eye calibration. And then the transformation matrix between end coordination system and world coordination system is solved. Validation experiment is implemented for verifying the proposed algorithms. Firstly, hand-eye transformation matrix is solved. Then a car body rear is measured for 16 times for the global data fusion algorithm verification. And the 3D shape of the rear is reconstructed successfully.

A Wide-Swath Spaceborne TOPS SAR Image Formation Algorithm Based on Chirp Scaling and Chirp-Z Transform

PubMed Central

Yang, Wei; Chen, Jie; Zeng, Hong Cheng; Wang, Peng Bo; Liu, Wei

2016-01-01

Based on the terrain observation by progressive scans (TOPS) mode, an efficient full-aperture image formation algorithm for focusing wide-swath spaceborne TOPS data is proposed. First, to overcome the Doppler frequency spectrum aliasing caused by azimuth antenna steering, the range-independent derotation operation is adopted, and the signal properties after derotation are derived in detail. Then, the azimuth deramp operation is performed to resolve image folding in azimuth. The traditional dermap function will introduce a time shift, resulting in appearance of ghost targets and azimuth resolution reduction at the scene edge, especially in the wide-swath coverage case. To avoid this, a novel solution is provided using a modified range-dependent deramp function combined with the chirp-z transform. Moreover, range scaling and azimuth scaling are performed to provide the same azimuth and range sampling interval for all sub-swaths, instead of the interpolation operation for the sub-swath image mosaic. Simulation results are provided to validate the proposed algorithm. PMID:27941706

Shift-and-invert parallel spectral transformation eigensolver: Massively parallel performance for density-functional based tight-binding

DOE PAGES

Zhang, Hong; Zapol, Peter; Dixon, David A.; ...

2015-11-17

The Shift-and-invert parallel spectral transformations (SIPs), a computational approach to solve sparse eigenvalue problems, is developed for massively parallel architectures with exceptional parallel scalability and robustness. The capabilities of SIPs are demonstrated by diagonalization of density-functional based tight-binding (DFTB) Hamiltonian and overlap matrices for single-wall metallic carbon nanotubes, diamond nanowires, and bulk diamond crystals. The largest (smallest) example studied is a 128,000 (2000) atom nanotube for which ~330,000 (~5600) eigenvalues and eigenfunctions are obtained in ~190 (~5) seconds when parallelized over 266,144 (16,384) Blue Gene/Q cores. Weak scaling and strong scaling of SIPs are analyzed and the performance of SIPsmore » is compared with other novel methods. Different matrix ordering methods are investigated to reduce the cost of the factorization step, which dominates the time-to-solution at the strong scaling limit. As a result, a parallel implementation of assembling the density matrix from the distributed eigenvectors is demonstrated.« less

Shift-and-invert parallel spectral transformation eigensolver: Massively parallel performance for density-functional based tight-binding

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

Zhang, Hong; Zapol, Peter; Dixon, David A.

The Shift-and-invert parallel spectral transformations (SIPs), a computational approach to solve sparse eigenvalue problems, is developed for massively parallel architectures with exceptional parallel scalability and robustness. The capabilities of SIPs are demonstrated by diagonalization of density-functional based tight-binding (DFTB) Hamiltonian and overlap matrices for single-wall metallic carbon nanotubes, diamond nanowires, and bulk diamond crystals. The largest (smallest) example studied is a 128,000 (2000) atom nanotube for which ~330,000 (~5600) eigenvalues and eigenfunctions are obtained in ~190 (~5) seconds when parallelized over 266,144 (16,384) Blue Gene/Q cores. Weak scaling and strong scaling of SIPs are analyzed and the performance of SIPsmore » is compared with other novel methods. Different matrix ordering methods are investigated to reduce the cost of the factorization step, which dominates the time-to-solution at the strong scaling limit. As a result, a parallel implementation of assembling the density matrix from the distributed eigenvectors is demonstrated.« less

Fast Poisson noise removal by biorthogonal Haar domain hypothesis testing

NASA Astrophysics Data System (ADS)

Zhang, B.; Fadili, M. J.; Starck, J.-L.; Digel, S. W.

2008-07-01

Methods based on hypothesis tests (HTs) in the Haar domain are widely used to denoise Poisson count data. Facing large datasets or real-time applications, Haar-based denoisers have to use the decimated transform to meet limited-memory or computation-time constraints. Unfortunately, for regular underlying intensities, decimation yields discontinuous estimates and strong “staircase” artifacts. In this paper, we propose to combine the HT framework with the decimated biorthogonal Haar (Bi-Haar) transform instead of the classical Haar. The Bi-Haar filter bank is normalized such that the p-values of Bi-Haar coefficients (p) provide good approximation to those of Haar (pH) for high-intensity settings or large scales; for low-intensity settings and small scales, we show that p are essentially upper-bounded by pH. Thus, we may apply the Haar-based HTs to Bi-Haar coefficients to control a prefixed false positive rate. By doing so, we benefit from the regular Bi-Haar filter bank to gain a smooth estimate while always maintaining a low computational complexity. A Fisher-approximation-based threshold implementing the HTs is also established. The efficiency of this method is illustrated on an example of hyperspectral-source-flux estimation.

Retinal identification based on an Improved Circular Gabor Filter and Scale Invariant Feature Transform.

PubMed

Meng, Xianjing; Yin, Yilong; Yang, Gongping; Xi, Xiaoming

2013-07-18

Retinal identification based on retinal vasculatures in the retina provides the most secure and accurate means of authentication among biometrics and has primarily been used in combination with access control systems at high security facilities. Recently, there has been much interest in retina identification. As digital retina images always suffer from deformations, the Scale Invariant Feature Transform (SIFT), which is known for its distinctiveness and invariance for scale and rotation, has been introduced to retinal based identification. However, some shortcomings like the difficulty of feature extraction and mismatching exist in SIFT-based identification. To solve these problems, a novel preprocessing method based on the Improved Circular Gabor Transform (ICGF) is proposed. After further processing by the iterated spatial anisotropic smooth method, the number of uninformative SIFT keypoints is decreased dramatically. Tested on the VARIA and eight simulated retina databases combining rotation and scaling, the developed method presents promising results and shows robustness to rotations and scale changes.

Retinal Identification Based on an Improved Circular Gabor Filter and Scale Invariant Feature Transform

PubMed Central

Meng, Xianjing; Yin, Yilong; Yang, Gongping; Xi, Xiaoming

2013-01-01

Retinal identification based on retinal vasculatures in the retina provides the most secure and accurate means of authentication among biometrics and has primarily been used in combination with access control systems at high security facilities. Recently, there has been much interest in retina identification. As digital retina images always suffer from deformations, the Scale Invariant Feature Transform (SIFT), which is known for its distinctiveness and invariance for scale and rotation, has been introduced to retinal based identification. However, some shortcomings like the difficulty of feature extraction and mismatching exist in SIFT-based identification. To solve these problems, a novel preprocessing method based on the Improved Circular Gabor Transform (ICGF) is proposed. After further processing by the iterated spatial anisotropic smooth method, the number of uninformative SIFT keypoints is decreased dramatically. Tested on the VARIA and eight simulated retina databases combining rotation and scaling, the developed method presents promising results and shows robustness to rotations and scale changes. PMID:23873409

Analysis of pulse thermography using similarities between wave and diffusion propagation

NASA Astrophysics Data System (ADS)

Gershenson, M.

2017-05-01

Pulse thermography or thermal wave imaging are commonly used as nondestructive evaluation (NDE) method. While the technical aspect has evolve with time, theoretical interpretation is lagging. Interpretation is still using curved fitting on a log log scale. A new approach based directly on the governing differential equation is introduced. By using relationships between wave propagation and the diffusive propagation of thermal excitation, it is shown that one can transform from solutions in one type of propagation to the other. The method is based on the similarities between the Laplace transforms of the diffusion equation and the wave equation. For diffusive propagation we have the Laplace variable s to the first power, while for the wave propagation similar equations occur with s2. For discrete time the transformation between the domains is performed by multiplying the temperature data vector by a matrix. The transform is local. The performance of the techniques is tested on synthetic data. The application of common back projection techniques used in the processing of wave data is also demonstrated. The combined use of the transform and back projection makes it possible to improve both depth and lateral resolution of transient thermography.

Fast Atomic-Scale Chemical Imaging of Crystalline Materials and Dynamic Phase Transformations

DOE PAGES

Lu, Ping; Yuan, Ren Liang; Ihlefeld, Jon F.; ...

2016-03-04

Chemical imaging at the atomic-scale provides a useful real-space approach to chemically investigate solid crystal structures, and has been recently demonstrated in aberration corrected scanning transmission electron microscopy (STEM). Atomic-scale chemical imaging by STEM using energy-dispersive X-ray spectroscopy (EDS) offers easy data interpretation with a one-to-one correspondence between image and structure but has a severe shortcoming due to the poor efficiency of X-ray generation and collection. As a result, it requires a long acquisition time of typical > few 100 seconds, limiting its potential applications. Here we describe the development of an atomic-scale STEM EDS chemical imaging technique that cutsmore » the acquisition time to one or a few seconds, efficiently reducing the acquisition time by more than 100 times. This method was demonstrated using LaAlO 3 (LAO) as a model crystal. Applying this method to the study of phase transformation induced by electron-beam radiation in a layered lithium transition-metal (TM) oxide, i.e., Li[Li 0.2Ni 0.2Mn 0.6]O 2 (LNMO), a cathode materials for lithium-ion batteries, we obtained a time-series of the atomic-scale chemical imaging, showing the transformation progressing by preferably jumping of Ni atoms from the TM layers into the Li-layers. The new capability offers an opportunity for temporal, atomic-scale chemical mapping of crystal structures for the investigation of materials susceptible to electron irradiation as well as phase transformation and dynamics at the atomic-scale.« less

Combining points and lines in rectifying satellite images

NASA Astrophysics Data System (ADS)

Elaksher, Ahmed F.

2017-09-01

The quick advance in remote sensing technologies established the potential to gather accurate and reliable information about the Earth surface using high resolution satellite images. Remote sensing satellite images of less than one-meter pixel size are currently used in large-scale mapping. Rigorous photogrammetric equations are usually used to describe the relationship between the image coordinates and ground coordinates. These equations require the knowledge of the exterior and interior orientation parameters of the image that might not be available. On the other hand, the parallel projection transformation could be used to represent the mathematical relationship between the image-space and objectspace coordinate systems and provides the required accuracy for large-scale mapping using fewer ground control features. This article investigates the differences between point-based and line-based parallel projection transformation models in rectifying satellite images with different resolutions. The point-based parallel projection transformation model and its extended form are presented and the corresponding line-based forms are developed. Results showed that the RMS computed using the point- or line-based transformation models are equivalent and satisfy the requirement for large-scale mapping. The differences between the transformation parameters computed using the point- and line-based transformation models are insignificant. The results showed high correlation between the differences in the ground elevation and the RMS.

Multiscale analysis of the intensity fluctuation in a time series of dynamic speckle patterns.

PubMed

Federico, Alejandro; Kaufmann, Guillermo H

2007-04-10

We propose the application of a method based on the discrete wavelet transform to detect, identify, and measure scaling behavior in dynamic speckle. The multiscale phenomena presented by a sample and displayed by its speckle activity are analyzed by processing the time series of dynamic speckle patterns. The scaling analysis is applied to the temporal fluctuation of the speckle intensity and also to the two derived data sets generated by its magnitude and sign. The application of the method is illustrated by analyzing paint-drying processes and bruising in apples. The results are discussed taking into account the different time organizations obtained for the scaling behavior of the magnitude and the sign of the intensity fluctuation.

IAU resolutions on reference systems and time scales in practice

NASA Astrophysics Data System (ADS)

Brumberg, V. A.; Groten, E.

2001-03-01

To be consistent with IAU/IUGG (1991) resolutions ICRS and ITRS should be treated as four-dimensional reference systems with TCB and TCG time scales, respectively, interrelated by a four-dimensional general relativistic transformation. This two-way transformation is given in the form adapted for actual application. The use of TB and TT instead of TCB and TCG, respectively, involves scaling factors complicating the use of this transformation in practice. New IAU B1 (2000) resolution is commented taking in mind some points of possible confusion in its practical application. The problem of the relationship of the theory of reference systems with the parameters of common relevance to astronomy, geodesy and geodynamics is briefly outlined.

A user-friendly tool to transform large scale administrative data into wide table format using a MapReduce program with a Pig Latin based script.

PubMed

Horiguchi, Hiromasa; Yasunaga, Hideo; Hashimoto, Hideki; Ohe, Kazuhiko

2012-12-22

Secondary use of large scale administrative data is increasingly popular in health services and clinical research, where a user-friendly tool for data management is in great demand. MapReduce technology such as Hadoop is a promising tool for this purpose, though its use has been limited by the lack of user-friendly functions for transforming large scale data into wide table format, where each subject is represented by one row, for use in health services and clinical research. Since the original specification of Pig provides very few functions for column field management, we have developed a novel system called GroupFilterFormat to handle the definition of field and data content based on a Pig Latin script. We have also developed, as an open-source project, several user-defined functions to transform the table format using GroupFilterFormat and to deal with processing that considers date conditions. Having prepared dummy discharge summary data for 2.3 million inpatients and medical activity log data for 950 million events, we used the Elastic Compute Cloud environment provided by Amazon Inc. to execute processing speed and scaling benchmarks. In the speed benchmark test, the response time was significantly reduced and a linear relationship was observed between the quantity of data and processing time in both a small and a very large dataset. The scaling benchmark test showed clear scalability. In our system, doubling the number of nodes resulted in a 47% decrease in processing time. Our newly developed system is widely accessible as an open resource. This system is very simple and easy to use for researchers who are accustomed to using declarative command syntax for commercial statistical software and Structured Query Language. Although our system needs further sophistication to allow more flexibility in scripts and to improve efficiency in data processing, it shows promise in facilitating the application of MapReduce technology to efficient data processing with large scale administrative data in health services and clinical research.

Fast Atomic-Scale Chemical Imaging of Crystalline Materials and Dynamic Phase Transformations.

PubMed

Lu, Ping; Yuan, Ren Liang; Ihlefeld, Jon F; Spoerke, Erik David; Pan, Wei; Zuo, Jian Min

2016-04-13

Atomic-scale phenomena fundamentally influence materials form and function that makes the ability to locally probe and study these processes critical to advancing our understanding and development of materials. Atomic-scale chemical imaging by scanning transmission electron microscopy (STEM) using energy-dispersive X-ray spectroscopy (EDS) is a powerful approach to investigate solid crystal structures. Inefficient X-ray emission and collection, however, require long acquisition times (typically hundreds of seconds), making the technique incompatible with electron-beam sensitive materials and study of dynamic material phenomena. Here we describe an atomic-scale STEM-EDS chemical imaging technique that decreases the acquisition time to as little as one second, a reduction of more than 100 times. We demonstrate this new approach using LaAlO3 single crystal and study dynamic phase transformation in beam-sensitive Li[Li0.2Ni0.2Mn0.6]O2 (LNMO) lithium ion battery cathode material. By capturing a series of time-lapsed chemical maps, we show for the first time clear atomic-scale evidence of preferred Ni-mobility in LNMO transformation, revealing new kinetic mechanisms. These examples highlight the potential of this approach toward temporal, atomic-scale mapping of crystal structure and chemistry for investigating dynamic material phenomena.

Nonlinear Prediction Model for Hydrologic Time Series Based on Wavelet Decomposition

NASA Astrophysics Data System (ADS)

Kwon, H.; Khalil, A.; Brown, C.; Lall, U.; Ahn, H.; Moon, Y.

2005-12-01

Traditionally forecasting and characterizations of hydrologic systems is performed utilizing many techniques. Stochastic linear methods such as AR and ARIMA and nonlinear ones such as statistical learning theory based tools have been extensively used. The common difficulty to all methods is the determination of sufficient and necessary information and predictors for a successful prediction. Relationships between hydrologic variables are often highly nonlinear and interrelated across the temporal scale. A new hybrid approach is proposed for the simulation of hydrologic time series combining both the wavelet transform and the nonlinear model. The present model employs some merits of wavelet transform and nonlinear time series model. The Wavelet Transform is adopted to decompose a hydrologic nonlinear process into a set of mono-component signals, which are simulated by nonlinear model. The hybrid methodology is formulated in a manner to improve the accuracy of a long term forecasting. The proposed hybrid model yields much better results in terms of capturing and reproducing the time-frequency properties of the system at hand. Prediction results are promising when compared to traditional univariate time series models. An application of the plausibility of the proposed methodology is provided and the results conclude that wavelet based time series model can be utilized for simulating and forecasting of hydrologic variable reasonably well. This will ultimately serve the purpose of integrated water resources planning and management.

Lagrangian single-particle turbulent statistics through the Hilbert-Huang transform.

PubMed

Huang, Yongxiang; Biferale, Luca; Calzavarini, Enrico; Sun, Chao; Toschi, Federico

2013-04-01

The Hilbert-Huang transform is applied to analyze single-particle Lagrangian velocity data from numerical simulations of hydrodynamic turbulence. The velocity trajectory is described in terms of a set of intrinsic mode functions C(i)(t) and of their instantaneous frequency ω(i)(t). On the basis of this decomposition we define the ω-conditioned statistical moments of the C(i) modes, named q-order Hilbert spectra (HS). We show that such quantities have enhanced scaling properties as compared to traditional Fourier transform- or correlation-based (structure functions) statistical indicators, thus providing better insights into the turbulent energy transfer process. We present clear empirical evidence that the energylike quantity, i.e., the second-order HS, displays a linear scaling in time in the inertial range, as expected from a dimensional analysis. We also measure high-order moment scaling exponents in a direct way, without resorting to the extended self-similarity procedure. This leads to an estimate of the Lagrangian structure function exponents which are consistent with the multifractal prediction in the Lagrangian frame as proposed by Biferale et al. [Phys. Rev. Lett. 93, 064502 (2004)].

From Networks to Time Series

NASA Astrophysics Data System (ADS)

Shimada, Yutaka; Ikeguchi, Tohru; Shigehara, Takaomi

2012-10-01

In this Letter, we propose a framework to transform a complex network to a time series. The transformation from complex networks to time series is realized by the classical multidimensional scaling. Applying the transformation method to a model proposed by Watts and Strogatz [Nature (London) 393, 440 (1998)], we show that ring lattices are transformed to periodic time series, small-world networks to noisy periodic time series, and random networks to random time series. We also show that these relationships are analytically held by using the circulant-matrix theory and the perturbation theory of linear operators. The results are generalized to several high-dimensional lattices.

Approximation of the ruin probability using the scaled Laplace transform inversion

PubMed Central

Mnatsakanov, Robert M.; Sarkisian, Khachatur; Hakobyan, Artak

2015-01-01

The problem of recovering the ruin probability in the classical risk model based on the scaled Laplace transform inversion is studied. It is shown how to overcome the problem of evaluating the ruin probability at large values of an initial surplus process. Comparisons of proposed approximations with the ones based on the Laplace transform inversions using a fixed Talbot algorithm as well as on the ones using the Trefethen–Weideman–Schmelzer and maximum entropy methods are presented via a simulation study. PMID:26752796

Real-time object tracking based on scale-invariant features employing bio-inspired hardware.

PubMed

Yasukawa, Shinsuke; Okuno, Hirotsugu; Ishii, Kazuo; Yagi, Tetsuya

2016-09-01

We developed a vision sensor system that performs a scale-invariant feature transform (SIFT) in real time. To apply the SIFT algorithm efficiently, we focus on a two-fold process performed by the visual system: whole-image parallel filtering and frequency-band parallel processing. The vision sensor system comprises an active pixel sensor, a metal-oxide semiconductor (MOS)-based resistive network, a field-programmable gate array (FPGA), and a digital computer. We employed the MOS-based resistive network for instantaneous spatial filtering and a configurable filter size. The FPGA is used to pipeline process the frequency-band signals. The proposed system was evaluated by tracking the feature points detected on an object in a video. Copyright © 2016 Elsevier Ltd. All rights reserved.

Poisson denoising on the sphere

NASA Astrophysics Data System (ADS)

Schmitt, J.; Starck, J. L.; Fadili, J.; Grenier, I.; Casandjian, J. M.

2009-08-01

In the scope of the Fermi mission, Poisson noise removal should improve data quality and make source detection easier. This paper presents a method for Poisson data denoising on sphere, called Multi-Scale Variance Stabilizing Transform on Sphere (MS-VSTS). This method is based on a Variance Stabilizing Transform (VST), a transform which aims to stabilize a Poisson data set such that each stabilized sample has an (asymptotically) constant variance. In addition, for the VST used in the method, the transformed data are asymptotically Gaussian. Thus, MS-VSTS consists in decomposing the data into a sparse multi-scale dictionary (wavelets, curvelets, ridgelets...), and then applying a VST on the coefficients in order to get quasi-Gaussian stabilized coefficients. In this present article, the used multi-scale transform is the Isotropic Undecimated Wavelet Transform. Then, hypothesis tests are made to detect significant coefficients, and the denoised image is reconstructed with an iterative method based on Hybrid Steepest Descent (HST). The method is tested on simulated Fermi data.

Transformation-aware perceptual image metric

NASA Astrophysics Data System (ADS)

Kellnhofer, Petr; Ritschel, Tobias; Myszkowski, Karol; Seidel, Hans-Peter

2016-09-01

Predicting human visual perception has several applications such as compression, rendering, editing, and retargeting. Current approaches, however, ignore the fact that the human visual system compensates for geometric transformations, e.g., we see that an image and a rotated copy are identical. Instead, they will report a large, false-positive difference. At the same time, if the transformations become too strong or too spatially incoherent, comparing two images gets increasingly difficult. Between these two extrema, we propose a system to quantify the effect of transformations, not only on the perception of image differences but also on saliency and motion parallax. To this end, we first fit local homographies to a given optical flow field, and then convert this field into a field of elementary transformations, such as translation, rotation, scaling, and perspective. We conduct a perceptual experiment quantifying the increase of difficulty when compensating for elementary transformations. Transformation entropy is proposed as a measure of complexity in a flow field. This representation is then used for applications, such as comparison of nonaligned images, where transformations cause threshold elevation, detection of salient transformations, and a model of perceived motion parallax. Applications of our approach are a perceptual level-of-detail for real-time rendering and viewpoint selection based on perceived motion parallax.

A downscaling method for the assessment of local climate change

NASA Astrophysics Data System (ADS)

Bruno, E.; Portoghese, I.; Vurro, M.

2009-04-01

The use of complimentary models is necessary to study the impact of climate change scenarios on the hydrological response at different space-time scales. However, the structure of GCMs is such that their space resolution (hundreds of kilometres) is too coarse and not adequate to describe the variability of extreme events at basin scale (Burlando and Rosso, 2002). To bridge the space-time gap between the climate scenarios and the usual scale of the inputs for hydrological prediction models is a fundamental requisite for the evaluation of climate change impacts on water resources. Since models operate a simplification of a complex reality, their results cannot be expected to fit with climate observations. Identifying local climate scenarios for impact analysis implies the definition of more detailed local scenario by downscaling GCMs or RCMs results. Among the output correction methods we consider the statistical approach by Déqué (2007) reported as a ‘Variable correction method' in which the correction of model outputs is obtained by a function build with the observation dataset and operating a quantile-quantile transformation (Q-Q transform). However, in the case of daily precipitation fields the Q-Q transform is not able to correct the temporal property of the model output concerning the dry-wet lacunarity process. An alternative correction method is proposed based on a stochastic description of the arrival-duration-intensity processes in coherence with the Poissonian Rectangular Pulse scheme (PRP) (Eagleson, 1972). In this proposed approach, the Q-Q transform is applied to the PRP variables derived from the daily rainfall datasets. Consequently the corrected PRP parameters are used for the synthetic generation of statistically homogeneous rainfall time series that mimic the persistency of daily observations for the reference period. Then the PRP parameters are forced through the GCM scenarios to generate local scale rainfall records for the 21st century. The statistical parameters characterizing daily storm occurrence, storm intensity and duration needed to apply the PRP scheme are considered among STARDEX collection of extreme indices.

Evaluation of Hydrologic and Meteorological Impacts on Dengue Fever Incidences in Southern Taiwan using Time- Frequency Method

NASA Astrophysics Data System (ADS)

Tsai, Christina; Yeh, Ting-Gu

2017-04-01

Extreme weather events are occurring more frequently as a result of climate change. Recently dengue fever has become a serious issue in southern Taiwan. It may have characteristic temporal scales that can be identified. Some researchers have hypothesized that dengue fever incidences are related to climate change. This study applies time-frequency analysis to time series data concerning dengue fever and hydrologic and meteorological variables. Results of three time-frequency analytical methods - the Hilbert Huang transform (HHT), the Wavelet Transform (WT) and the Short Time Fourier Transform (STFT) are compared and discussed. A more effective time-frequency analysis method will be identified to analyze relevant time series data. The most influential time scales of hydrologic and meteorological variables that are associated with dengue fever are determined. Finally, the linkage between hydrologic/meteorological factors and dengue fever incidences can be established.

Multiresolution forecasting for futures trading using wavelet decompositions.

PubMed

Zhang, B L; Coggins, R; Jabri, M A; Dersch, D; Flower, B

2001-01-01

We investigate the effectiveness of a financial time-series forecasting strategy which exploits the multiresolution property of the wavelet transform. A financial series is decomposed into an over complete, shift invariant scale-related representation. In transform space, each individual wavelet series is modeled by a separate multilayer perceptron (MLP). We apply the Bayesian method of automatic relevance determination to choose short past windows (short-term history) for the inputs to the MLPs at lower scales and long past windows (long-term history) at higher scales. To form the overall forecast, the individual forecasts are then recombined by the linear reconstruction property of the inverse transform with the chosen autocorrelation shell representation, or by another perceptron which learns the weight of each scale in the prediction of the original time series. The forecast results are then passed to a money management system to generate trades.

Percentile-Based Journal Impact Factors: A Neglected Collection Development Metric

ERIC Educational Resources Information Center

Wagner, A. Ben

2009-01-01

Various normalization techniques to transform journal impact factors (JIFs) into a standard scale or range of values have been reported a number of times in the literature, but have seldom been part of collection development librarians' tool kits. In this paper, JIFs as reported in the Journal Citation Reports (JCR) database are converted to…

Precipitation of coherent Ni{sub 2}(Cr, W) superlattice in an Ni–Cr–W superalloy

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

Gao, Xiangyu; Hu, Rui, E-mail: rhu@nwpu.edu.cn; Zhang, Tiebang

2016-01-15

It is demonstrated that a nanometer-sized Ni{sub 2}(Cr, W) superlattice with a Pt{sub 2}Mo-type structure can precipitate in an Ni–Cr–W alloy by means of a simple aging treatment at 550 °C. The dark-field image of short-range order domains has been found for the first time experimentally. The mechanism of short-range order to long-range order transformation has been revealed based on transmission electron microscopy result and static concentration waves theory and found to be continuous ordering. The randomness of the transformation of static concentration waves leads to equiprobable occurrence of the different variants. The transformation of short-range order to long-range ordermore » gives rise to the Pt{sub 2}Mo-type Ni{sub 2}(Cr, W) superlattice. The interfaces between Ni{sub 2}(Cr, W) and Ni-based matrix and the different variants of Ni{sub 2}(Cr, W) have been investigated by high resolution transmission electron microscopy. The results reveal that the interfaces between Ni{sub 2}(Cr, W) and surrounding matrix are coherent at the atomic scale. - Highlights: • The DF image of SRO cluster has been found for the first time experimentally. • The transformation of SRO to LRO gives rise to the Pt{sub 2}Mo-type Ni{sub 2}(Cr, W). • Variants of Ni{sub 2}(Cr, W) occur equiprobably. • The interfaces between Ni{sub 2}(Cr, W) and matrix are coherent at the atomic scale.« less

Element analysis: a wavelet-based method for analysing time-localized events in noisy time series.

PubMed

Lilly, Jonathan M

2017-04-01

A method is derived for the quantitative analysis of signals that are composed of superpositions of isolated, time-localized 'events'. Here, these events are taken to be well represented as rescaled and phase-rotated versions of generalized Morse wavelets, a broad family of continuous analytic functions. Analysing a signal composed of replicates of such a function using another Morse wavelet allows one to directly estimate the properties of events from the values of the wavelet transform at its own maxima. The distribution of events in general power-law noise is determined in order to establish significance based on an expected false detection rate. Finally, an expression for an event's 'region of influence' within the wavelet transform permits the formation of a criterion for rejecting spurious maxima due to numerical artefacts or other unsuitable events. Signals can then be reconstructed based on a small number of isolated points on the time/scale plane. This method, termed element analysis , is applied to the identification of long-lived eddy structures in ocean currents as observed by along-track measurements of sea surface elevation from satellite altimetry.

Wavelet-based multiscale performance analysis: An approach to assess and improve hydrological models

NASA Astrophysics Data System (ADS)

Rathinasamy, Maheswaran; Khosa, Rakesh; Adamowski, Jan; ch, Sudheer; Partheepan, G.; Anand, Jatin; Narsimlu, Boini

2014-12-01

The temporal dynamics of hydrological processes are spread across different time scales and, as such, the performance of hydrological models cannot be estimated reliably from global performance measures that assign a single number to the fit of a simulated time series to an observed reference series. Accordingly, it is important to analyze model performance at different time scales. Wavelets have been used extensively in the area of hydrological modeling for multiscale analysis, and have been shown to be very reliable and useful in understanding dynamics across time scales and as these evolve in time. In this paper, a wavelet-based multiscale performance measure for hydrological models is proposed and tested (i.e., Multiscale Nash-Sutcliffe Criteria and Multiscale Normalized Root Mean Square Error). The main advantage of this method is that it provides a quantitative measure of model performance across different time scales. In the proposed approach, model and observed time series are decomposed using the Discrete Wavelet Transform (known as the à trous wavelet transform), and performance measures of the model are obtained at each time scale. The applicability of the proposed method was explored using various case studies-both real as well as synthetic. The synthetic case studies included various kinds of errors (e.g., timing error, under and over prediction of high and low flows) in outputs from a hydrologic model. The real time case studies investigated in this study included simulation results of both the process-based Soil Water Assessment Tool (SWAT) model, as well as statistical models, namely the Coupled Wavelet-Volterra (WVC), Artificial Neural Network (ANN), and Auto Regressive Moving Average (ARMA) methods. For the SWAT model, data from Wainganga and Sind Basin (India) were used, while for the Wavelet Volterra, ANN and ARMA models, data from the Cauvery River Basin (India) and Fraser River (Canada) were used. The study also explored the effect of the choice of the wavelets in multiscale model evaluation. It was found that the proposed wavelet-based performance measures, namely the MNSC (Multiscale Nash-Sutcliffe Criteria) and MNRMSE (Multiscale Normalized Root Mean Square Error), are a more reliable measure than traditional performance measures such as the Nash-Sutcliffe Criteria (NSC), Root Mean Square Error (RMSE), and Normalized Root Mean Square Error (NRMSE). Further, the proposed methodology can be used to: i) compare different hydrological models (both physical and statistical models), and ii) help in model calibration.

Analysis and test for space shuttle propellant dynamics (1/10th scale model test results). Volume 2: 1/10th scale model test data

NASA Technical Reports Server (NTRS)

Berry, R. L.; Tegart, J. R.; Demchak, L. J.

1979-01-01

Thirty sets of test data selected from the 89 low-g aircraft tests flown by NASA KC-135 zero-g aircraft are listed in tables with their accompanying test conditions. The data for each test consists of the time history plots of digitalized data (in engineering units) and the time history plots of the load cell data transformed to the tank axis system. The transformed load cell data was developed for future analytical comparisons; therefore, these data were transformed and plotted from the time at which the aircraft Z axis acceleration passed through l-g. There are 14 time history plots per test condition. The contents of each plot is shown in a table.

Gray-scale transform and evaluation for digital x-ray chest images on CRT monitor

NASA Astrophysics Data System (ADS)

Furukawa, Isao; Suzuki, Junji; Ono, Sadayasu; Kitamura, Masayuki; Ando, Yutaka

1997-04-01

In this paper, an experimental evaluation of a super high definition (SHD) imaging system for digital x-ray chest images is presented. The SHD imaging system is proposed as a platform for integrating conventional image media. We are involved in the use of SHD images in the total digitizing of medical records that include chest x-rays and pathological microscopic images, both which demand the highest level of quality among the various types of medical images. SHD images use progressive scanning and have a spatial resolution of 2000 by 2000 pixels or more and a temporal resolution (frame rate) of 60 frames/sec or more. For displaying medical x-ray images on a CRT, we derived gray scale transform characteristics based on radiologists' comments during the experiment, and elucidated the relationship between that gray scale transform and the linearization transform for maintaining the linear relationship with the luminance of film on a light box (luminance linear transform). We then carried out viewing experiments based on a five-stage evaluation. Nine radiologists participated in our experiment, and the ten cases evaluated included pulmonary fibrosis, lung cancer, and pneumonia. The experimental results indicated that conventional film images and those on super high definition CRT monitors have nearly the same quality. They also show that the gray scale transform for CRT images decided according to radiologists' comments agrees with the luminance linear transform in the high luminance region. And in the low luminance region, it was found that the gray scale transform had the characteristics of level expansion to increase the number of levels that can be expressed.

Inverse Transformation: Unleashing Spatially Heterogeneous Dynamics with an Alternative Approach to XPCS Data Analysis.

PubMed

Andrews, Ross N; Narayanan, Suresh; Zhang, Fan; Kuzmenko, Ivan; Ilavsky, Jan

2018-02-01

X-ray photon correlation spectroscopy (XPCS), an extension of dynamic light scattering (DLS) in the X-ray regime, detects temporal intensity fluctuations of coherent speckles and provides scattering vector-dependent sample dynamics at length scales smaller than DLS. The penetrating power of X-rays enables probing dynamics in a broad array of materials with XPCS, including polymers, glasses and metal alloys, where attempts to describe the dynamics with a simple exponential fit usually fails. In these cases, the prevailing XPCS data analysis approach employs stretched or compressed exponential decay functions (Kohlrausch functions), which implicitly assume homogeneous dynamics. In this paper, we propose an alternative analysis scheme based upon inverse Laplace or Gaussian transformation for elucidating heterogeneous distributions of dynamic time scales in XPCS, an approach analogous to the CONTIN algorithm widely accepted in the analysis of DLS from polydisperse and multimodal systems. Using XPCS data measured from colloidal gels, we demonstrate the inverse transform approach reveals hidden multimodal dynamics in materials, unleashing the full potential of XPCS.

Inverse Transformation: Unleashing Spatially Heterogeneous Dynamics with an Alternative Approach to XPCS Data Analysis

PubMed Central

Andrews, Ross N.; Narayanan, Suresh; Zhang, Fan; Kuzmenko, Ivan; Ilavsky, Jan

2018-01-01

X-ray photon correlation spectroscopy (XPCS), an extension of dynamic light scattering (DLS) in the X-ray regime, detects temporal intensity fluctuations of coherent speckles and provides scattering vector-dependent sample dynamics at length scales smaller than DLS. The penetrating power of X-rays enables probing dynamics in a broad array of materials with XPCS, including polymers, glasses and metal alloys, where attempts to describe the dynamics with a simple exponential fit usually fails. In these cases, the prevailing XPCS data analysis approach employs stretched or compressed exponential decay functions (Kohlrausch functions), which implicitly assume homogeneous dynamics. In this paper, we propose an alternative analysis scheme based upon inverse Laplace or Gaussian transformation for elucidating heterogeneous distributions of dynamic time scales in XPCS, an approach analogous to the CONTIN algorithm widely accepted in the analysis of DLS from polydisperse and multimodal systems. Using XPCS data measured from colloidal gels, we demonstrate the inverse transform approach reveals hidden multimodal dynamics in materials, unleashing the full potential of XPCS. PMID:29875506

Computer implemented empirical mode decomposition method apparatus, and article of manufacture utilizing curvature extrema

NASA Technical Reports Server (NTRS)

Shen, Zheng (Inventor); Huang, Norden Eh (Inventor)

2003-01-01

A computer implemented physical signal analysis method is includes two essential steps and the associated presentation techniques of the results. All the steps exist only in a computer: there are no analytic expressions resulting from the method. The first step is a computer implemented Empirical Mode Decomposition to extract a collection of Intrinsic Mode Functions (IMF) from nonlinear, nonstationary physical signals based on local extrema and curvature extrema. The decomposition is based on the direct extraction of the energy associated with various intrinsic time scales in the physical signal. Expressed in the IMF's, they have well-behaved Hilbert Transforms from which instantaneous frequencies can be calculated. The second step is the Hilbert Transform. The final result is the Hilbert Spectrum. Thus, the invention can localize any event on the time as well as the frequency axis. The decomposition can also be viewed as an expansion of the data in terms of the IMF's. Then, these IMF's, based on and derived from the data, can serve as the basis of that expansion. The local energy and the instantaneous frequency derived from the IMF's through the Hilbert transform give a full energy-frequency-time distribution of the data which is designated as the Hilbert Spectrum.

Time-frequency analysis of electric motors

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

Bentley, C.L.; Dunn, M.E.; Mattingly, J.K.

1995-12-31

Physical signals such as the current of an electric motor become nonstationary as a consequence of degraded operation and broken parts. In this instance, their power spectral densities become time dependent, and time-frequency analysis techniques become the appropriate tools for signal analysis. The first among these techniques, generally called the short-time Fourier transform (STFT) method, is the Gabor transform 2 (GT) of a signal S(t), which decomposes the signal into time-local frequency modes: where the window function, {Phi}(t-{tau}), is a normalized Gaussian. Alternatively, one can decompose the signal into its multi-resolution representation at different levels of magnification. This representation ismore » achieved by the continuous wavelet transform (CWT) where the function g(t) is a kernel of zero average belonging to a family of scaled and shifted wavelet kernels. The CWT can be interpreted as the action of a microscope that locates the signal by the shift parameter b and adjusts its magnification by changing the scale parameter a. The Fourier-transformed CWT, W,{sub g}(a, {omega}), acts as a filter that places the high-frequency content of a signal into the lower end of the scale spectrum and vice versa for the low frequencies. Signals from a motor in three different states were analyzed.« less

Coordinated control of active and reactive power of distribution network with distributed PV cluster via model predictive control

NASA Astrophysics Data System (ADS)

Ji, Yu; Sheng, Wanxing; Jin, Wei; Wu, Ming; Liu, Haitao; Chen, Feng

2018-02-01

A coordinated optimal control method of active and reactive power of distribution network with distributed PV cluster based on model predictive control is proposed in this paper. The method divides the control process into long-time scale optimal control and short-time scale optimal control with multi-step optimization. The models are transformed into a second-order cone programming problem due to the non-convex and nonlinear of the optimal models which are hard to be solved. An improved IEEE 33-bus distribution network system is used to analyse the feasibility and the effectiveness of the proposed control method

Plasticity-mediated collapse and recrystallization in hollow copper nanowires: a molecular dynamics simulation.

PubMed

Dutta, Amlan; Raychaudhuri, Arup Kumar; Saha-Dasgupta, Tanusri

2016-01-01

We study the thermal stability of hollow copper nanowires using molecular dynamics simulation. We find that the plasticity-mediated structural evolution leads to transformation of the initial hollow structure to a solid wire. The process involves three distinct stages, namely, collapse, recrystallization and slow recovery. We calculate the time scales associated with different stages of the evolution process. Our findings suggest a plasticity-mediated mechanism of collapse and recrystallization. This contradicts the prevailing notion of diffusion driven transport of vacancies from the interior to outer surface being responsible for collapse, which would involve much longer time scales as compared to the plasticity-based mechanism.

Analytical and phenomenological studies of rotating turbulence

NASA Technical Reports Server (NTRS)

Mahalov, Alex; Zhou, YE

1995-01-01

A framework, which combines mathematical analysis, closure theory, and phenomenological treatment, is developed to study the spectral transfer process and reduction of dimensionality in turbulent flows that are subject to rotation. First, we outline a mathematical procedure that is particularly appropriate for problems with two disparate time scales. The approach which is based on the Green's method leads to the Poincare velocity variables and the Poincare transformation when applied to rotating turbulence. The effects of the rotation are now reflected in the modifications to the convolution of a nonlinear term. The Poincare transformed equations are used to obtain a time-dependent analog of the Taylor-Proudman theorem valid in the asymptotic limit when the non-dimensional parameter mu is identical to Omega(t) approaches infinity (Omega is the rotation rate and t is the time). The 'split' of the energy transfer in both direct and inverse directions is established. Secondly, we apply the Eddy-Damped-Quasinormal-Markovian (EDQNM) closure to the Poincare transformed Euler/Navier-Stokes equations. This closure leads to expressions for the spectral energy transfer. In particular, an unique triple velocity decorrelation time is derived with an explicit dependence on the rotation rate. This provides an important input for applying the phenomenological treatment of Zhou. In order to characterize the relative strength of rotation, another non-dimensional number, a spectral Rossby number, which is defined as the ratio of rotation and turbulence time scales, is introduced. Finally, the energy spectrum and the spectral eddy viscosity are deduced.

Computer implemented empirical mode decomposition method, apparatus and article of manufacture

NASA Technical Reports Server (NTRS)

Huang, Norden E. (Inventor)

1999-01-01

A computer implemented physical signal analysis method is invented. This method includes two essential steps and the associated presentation techniques of the results. All the steps exist only in a computer: there are no analytic expressions resulting from the method. The first step is a computer implemented Empirical Mode Decomposition to extract a collection of Intrinsic Mode Functions (IMF) from nonlinear, nonstationary physical signals. The decomposition is based on the direct extraction of the energy associated with various intrinsic time scales in the physical signal. Expressed in the IMF's, they have well-behaved Hilbert Transforms from which instantaneous frequencies can be calculated. The second step is the Hilbert Transform. The final result is the Hilbert Spectrum. Thus, the invention can localize any event on the time as well as the frequency axis. The decomposition can also be viewed as an expansion of the data in terms of the IMF's. Then, these IMF's, based on and derived from the data, can serve as the basis of that expansion. The local energy and the instantaneous frequency derived from the IMF's through the Hilbert transform give a full energy-frequency-time distribution of the data which is designated as the Hilbert Spectrum.

Experimental phase synchronization detection in non-phase coherent chaotic systems by using the discrete complex wavelet approach

NASA Astrophysics Data System (ADS)

Ferreira, Maria Teodora; Follmann, Rosangela; Domingues, Margarete O.; Macau, Elbert E. N.; Kiss, István Z.

2017-08-01

Phase synchronization may emerge from mutually interacting non-linear oscillators, even under weak coupling, when phase differences are bounded, while amplitudes remain uncorrelated. However, the detection of this phenomenon can be a challenging problem to tackle. In this work, we apply the Discrete Complex Wavelet Approach (DCWA) for phase assignment, considering signals from coupled chaotic systems and experimental data. The DCWA is based on the Dual-Tree Complex Wavelet Transform (DT-CWT), which is a discrete transformation. Due to its multi-scale properties in the context of phase characterization, it is possible to obtain very good results from scalar time series, even with non-phase-coherent chaotic systems without state space reconstruction or pre-processing. The method correctly predicts the phase synchronization for a chemical experiment with three locally coupled, non-phase-coherent chaotic processes. The impact of different time-scales is demonstrated on the synchronization process that outlines the advantages of DCWA for analysis of experimental data.

Subauditory Speech Recognition based on EMG/EPG Signals

NASA Technical Reports Server (NTRS)

Jorgensen, Charles; Lee, Diana Dee; Agabon, Shane; Lau, Sonie (Technical Monitor)

2003-01-01

Sub-vocal electromyogram/electro palatogram (EMG/EPG) signal classification is demonstrated as a method for silent speech recognition. Recorded electrode signals from the larynx and sublingual areas below the jaw are noise filtered and transformed into features using complex dual quad tree wavelet transforms. Feature sets for six sub-vocally pronounced words are trained using a trust region scaled conjugate gradient neural network. Real time signals for previously unseen patterns are classified into categories suitable for primitive control of graphic objects. Feature construction, recognition accuracy and an approach for extension of the technique to a variety of real world application areas are presented.

A Coarse-to-Fine Geometric Scale-Invariant Feature Transform for Large Size High Resolution Satellite Image Registration

PubMed Central

Chang, Xueli; Du, Siliang; Li, Yingying; Fang, Shenghui

2018-01-01

Large size high resolution (HR) satellite image matching is a challenging task due to local distortion, repetitive structures, intensity changes and low efficiency. In this paper, a novel matching approach is proposed for the large size HR satellite image registration, which is based on coarse-to-fine strategy and geometric scale-invariant feature transform (SIFT). In the coarse matching step, a robust matching method scale restrict (SR) SIFT is implemented at low resolution level. The matching results provide geometric constraints which are then used to guide block division and geometric SIFT in the fine matching step. The block matching method can overcome the memory problem. In geometric SIFT, with area constraints, it is beneficial for validating the candidate matches and decreasing searching complexity. To further improve the matching efficiency, the proposed matching method is parallelized using OpenMP. Finally, the sensing image is rectified to the coordinate of reference image via Triangulated Irregular Network (TIN) transformation. Experiments are designed to test the performance of the proposed matching method. The experimental results show that the proposed method can decrease the matching time and increase the number of matching points while maintaining high registration accuracy. PMID:29702589

Bearing faults identification and resonant band demodulation based on wavelet de-noising methods and envelope analysis

NASA Astrophysics Data System (ADS)

Abdelrhman, Ahmed M.; Sei Kien, Yong; Salman Leong, M.; Meng Hee, Lim; Al-Obaidi, Salah M. Ali

2017-07-01

The vibration signals produced by rotating machinery contain useful information for condition monitoring and fault diagnosis. Fault severities assessment is a challenging task. Wavelet Transform (WT) as a multivariate analysis tool is able to compromise between the time and frequency information in the signals and served as a de-noising method. The CWT scaling function gives different resolutions to the discretely signals such as very fine resolution at lower scale but coarser resolution at a higher scale. However, the computational cost increased as it needs to produce different signal resolutions. DWT has better low computation cost as the dilation function allowed the signals to be decomposed through a tree of low and high pass filters and no further analysing the high-frequency components. In this paper, a method for bearing faults identification is presented by combing Continuous Wavelet Transform (CWT) and Discrete Wavelet Transform (DWT) with envelope analysis for bearing fault diagnosis. The experimental data was sampled by Case Western Reserve University. The analysis result showed that the proposed method is effective in bearing faults detection, identify the exact fault’s location and severity assessment especially for the inner race and outer race faults.

The morphing of geographical features by Fourier transformation.

PubMed

Li, Jingzhong; Liu, Pengcheng; Yu, Wenhao; Cheng, Xiaoqiang

2018-01-01

This paper presents a morphing model of vector geographical data based on Fourier transformation. This model involves three main steps. They are conversion from vector data to Fourier series, generation of intermediate function by combination of the two Fourier series concerning a large scale and a small scale, and reverse conversion from combination function to vector data. By mirror processing, the model can also be used for morphing of linear features. Experimental results show that this method is sensitive to scale variations and it can be used for vector map features' continuous scale transformation. The efficiency of this model is linearly related to the point number of shape boundary and the interceptive value n of Fourier expansion. The effect of morphing by Fourier transformation is plausible and the efficiency of the algorithm is acceptable.

Temporal Characterization of Aircraft Noise Sources

NASA Technical Reports Server (NTRS)

Grosveld, Ferdinand W.; Sullivan, Brenda M.; Rizzi, Stephen A.

2004-01-01

Current aircraft source noise prediction tools yield time-independent frequency spectra as functions of directivity angle. Realistic evaluation and human assessment of aircraft fly-over noise require the temporal characteristics of the noise signature. The purpose of the current study is to analyze empirical data from broadband jet and tonal fan noise sources and to provide the temporal information required for prediction-based synthesis. Noise sources included a one-tenth-scale engine exhaust nozzle and a one-fifth scale scale turbofan engine. A methodology was developed to characterize the low frequency fluctuations employing the Short Time Fourier Transform in a MATLAB computing environment. It was shown that a trade-off is necessary between frequency and time resolution in the acoustic spectrogram. The procedure requires careful evaluation and selection of the data analysis parameters, including the data sampling frequency, Fourier Transform window size, associated time period and frequency resolution, and time period window overlap. Low frequency fluctuations were applied to the synthesis of broadband noise with the resulting records sounding virtually indistinguishable from the measured data in initial subjective evaluations. Amplitude fluctuations of blade passage frequency (BPF) harmonics were successfully characterized for conditions equivalent to take-off and approach. Data demonstrated that the fifth harmonic of the BPF varied more in frequency than the BPF itself and exhibited larger amplitude fluctuations over the duration of the time record. Frequency fluctuations were found to be not perceptible in the current characterization of tonal components.

Multiscale characterization and prediction of monsoon rainfall in India using Hilbert-Huang transform and time-dependent intrinsic correlation analysis

NASA Astrophysics Data System (ADS)

Adarsh, S.; Reddy, M. Janga

2017-07-01

In this paper, the Hilbert-Huang transform (HHT) approach is used for the multiscale characterization of All India Summer Monsoon Rainfall (AISMR) time series and monsoon rainfall time series from five homogeneous regions in India. The study employs the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) for multiscale decomposition of monsoon rainfall in India and uses the Normalized Hilbert Transform and Direct Quadrature (NHT-DQ) scheme for the time-frequency characterization. The cross-correlation analysis between orthogonal modes of All India monthly monsoon rainfall time series and that of five climate indices such as Quasi Biennial Oscillation (QBO), El Niño Southern Oscillation (ENSO), Sunspot Number (SN), Atlantic Multi Decadal Oscillation (AMO), and Equatorial Indian Ocean Oscillation (EQUINOO) in the time domain showed that the links of different climate indices with monsoon rainfall are expressed well only for few low-frequency modes and for the trend component. Furthermore, this paper investigated the hydro-climatic teleconnection of ISMR in multiple time scales using the HHT-based running correlation analysis technique called time-dependent intrinsic correlation (TDIC). The results showed that both the strength and nature of association between different climate indices and ISMR vary with time scale. Stemming from this finding, a methodology employing Multivariate extension of EMD and Stepwise Linear Regression (MEMD-SLR) is proposed for prediction of monsoon rainfall in India. The proposed MEMD-SLR method clearly exhibited superior performance over the IMD operational forecast, M5 Model Tree (MT), and multiple linear regression methods in ISMR predictions and displayed excellent predictive skill during 1989-2012 including the four extreme events that have occurred during this period.

Scaling and scale invariance of conservation laws in Reynolds transport theorem framework

NASA Astrophysics Data System (ADS)

Haltas, Ismail; Ulusoy, Suleyman

2015-07-01

Scale invariance is the case where the solution of a physical process at a specified time-space scale can be linearly related to the solution of the processes at another time-space scale. Recent studies investigated the scale invariance conditions of hydrodynamic processes by applying the one-parameter Lie scaling transformations to the governing equations of the processes. Scale invariance of a physical process is usually achieved under certain conditions on the scaling ratios of the variables and parameters involved in the process. The foundational axioms of hydrodynamics are the conservation laws, namely, conservation of mass, conservation of linear momentum, and conservation of energy from continuum mechanics. They are formulated using the Reynolds transport theorem. Conventionally, Reynolds transport theorem formulates the conservation equations in integral form. Yet, differential form of the conservation equations can also be derived for an infinitesimal control volume. In the formulation of the governing equation of a process, one or more than one of the conservation laws and, some times, a constitutive relation are combined together. Differential forms of the conservation equations are used in the governing partial differential equation of the processes. Therefore, differential conservation equations constitute the fundamentals of the governing equations of the hydrodynamic processes. Applying the one-parameter Lie scaling transformation to the conservation laws in the Reynolds transport theorem framework instead of applying to the governing partial differential equations may lead to more fundamental conclusions on the scaling and scale invariance of the hydrodynamic processes. This study will investigate the scaling behavior and scale invariance conditions of the hydrodynamic processes by applying the one-parameter Lie scaling transformation to the conservation laws in the Reynolds transport theorem framework.

The Application of Continuous Wavelet Transform Based Foreground Subtraction Method in 21 cm Sky Surveys

NASA Astrophysics Data System (ADS)

Gu, Junhua; Xu, Haiguang; Wang, Jingying; An, Tao; Chen, Wen

2013-08-01

We propose a continuous wavelet transform based non-parametric foreground subtraction method for the detection of redshifted 21 cm signal from the epoch of reionization. This method works based on the assumption that the foreground spectra are smooth in frequency domain, while the 21 cm signal spectrum is full of saw-tooth-like structures, thus their characteristic scales are significantly different. We can distinguish them in the wavelet coefficient space easily and perform the foreground subtraction. Compared with the traditional spectral fitting based method, our method is more tolerant to complex foregrounds. Furthermore, we also find that when the instrument has uncorrected response error, our method can also work significantly better than the spectral fitting based method. Our method can obtain similar results with the Wp smoothing method, which is also a non-parametric method, but our method consumes much less computing time.

Singular perturbation and time scale approaches in discrete control systems

NASA Technical Reports Server (NTRS)

Naidu, D. S.; Price, D. B.

1988-01-01

After considering a singularly perturbed discrete control system, a singular perturbation approach is used to obtain outer and correction subsystems. A time scale approach is then applied via block diagonalization transformations to decouple the system into slow and fast subsystems. To a zeroth-order approximation, the singular perturbation and time-scale approaches are found to yield equivalent results.

Coalescence computations for large samples drawn from populations of time-varying sizes

PubMed Central

Polanski, Andrzej; Szczesna, Agnieszka; Garbulowski, Mateusz; Kimmel, Marek

2017-01-01

We present new results concerning probability distributions of times in the coalescence tree and expected allele frequencies for coalescent with large sample size. The obtained results are based on computational methodologies, which involve combining coalescence time scale changes with techniques of integral transformations and using analytical formulae for infinite products. We show applications of the proposed methodologies for computing probability distributions of times in the coalescence tree and their limits, for evaluation of accuracy of approximate expressions for times in the coalescence tree and expected allele frequencies, and for analysis of large human mitochondrial DNA dataset. PMID:28170404

Estimation of surface heat and moisture fluxes over a prairie grassland. II - Two-dimensional time filtering and site variability

NASA Technical Reports Server (NTRS)

Crosson, William L.; Smith, Eric A.

1992-01-01

The behavior of in situ measurements of surface fluxes obtained during FIFE 1987 is examined by using correlative and spectral techniques in order to assess the significance of fluctuations on various time scales, from subdiurnal up to synoptic, intraseasonal, and annual scales. The objectives of this analysis are: (1) to determine which temporal scales have a significant impact on areal averaged fluxes and (2) to design a procedure for filtering an extended flux time series that preserves the basic diurnal features and longer time scales while removing high frequency noise that cannot be attributed to site-induced variation. These objectives are accomplished through the use of a two-dimensional cross-time Fourier transform, which serves to separate processes inherently related to diurnal and subdiurnal variability from those which impact flux variations on the longer time scales. A filtering procedure is desirable before the measurements are utilized as input with an experimental biosphere model, to insure that model based intercomparisons at multiple sites are uncontaminated by input variance not related to true site behavior. Analysis of the spectral decomposition indicates that subdiurnal time scales having periods shorter than 6 hours have little site-to-site consistency and therefore little impact on areal integrated fluxes.

Groundwater similarity across a watershed derived from time-warped and flow-corrected time series

NASA Astrophysics Data System (ADS)

Rinderer, M.; McGlynn, B. L.; van Meerveld, H. J.

2017-05-01

Information about catchment-scale groundwater dynamics is necessary to understand how catchments store and release water and why water quantity and quality varies in streams. However, groundwater level monitoring is often restricted to a limited number of sites. Knowledge of the factors that determine similarity between monitoring sites can be used to predict catchment-scale groundwater storage and connectivity of different runoff source areas. We used distance-based and correlation-based similarity measures to quantify the spatial and temporal differences in shallow groundwater similarity for 51 monitoring sites in a Swiss prealpine catchment. The 41 months long time series were preprocessed using Dynamic Time-Warping and a Flow-corrected Time Transformation to account for small timing differences and bias toward low-flow periods. The mean distance-based groundwater similarity was correlated to topographic indices, such as upslope contributing area, topographic wetness index, and local slope. Correlation-based similarity was less related to landscape position but instead revealed differences between seasons. Analysis of variance and partial Mantel tests showed that landscape position, represented by the topographic wetness index, explained 52% of the variability in mean distance-based groundwater similarity, while spatial distance, represented by the Euclidean distance, explained only 5%. The variability in distance-based similarity and correlation-based similarity between groundwater and streamflow time series was significantly larger for midslope locations than for other landscape positions. This suggests that groundwater dynamics at these midslope sites, which are important to understand runoff source areas and hydrological connectivity at the catchment scale, are most difficult to predict.

The efficiency of parameter estimation of latent path analysis using summated rating scale (SRS) and method of successive interval (MSI) for transformation of score to scale

NASA Astrophysics Data System (ADS)

Solimun, Fernandes, Adji Achmad Rinaldo; Arisoesilaningsih, Endang

2017-12-01

Research in various fields generally investigates systems and involves latent variables. One method to analyze the model representing the system is path analysis. The data of latent variables measured using questionnaires by applying attitude scale model yields data in the form of score, before analyzed should be transformation so that it becomes data of scale. Path coefficient, is parameter estimator, calculated from scale data using method of successive interval (MSI) and summated rating scale (SRS). In this research will be identifying which data transformation method is better. Path coefficients have smaller varieties are said to be more efficient. The transformation method that produces scaled data and used in path analysis capable of producing path coefficients (parameter estimators) with smaller varieties is said to be better. The result of analysis using real data shows that on the influence of Attitude variable to Intention Entrepreneurship, has relative efficiency (ER) = 1, where it shows that the result of analysis using data transformation of MSI and SRS as efficient. On the other hand, for simulation data, at high correlation between items (0.7-0.9), MSI method is more efficient 1.3 times better than SRS method.

Spanning the scales of mechanical metamaterials using time domain simulations in transformed crystals, graphene flakes and structured soils

NASA Astrophysics Data System (ADS)

Aznavourian, Ronald; Puvirajesinghe, Tania M.; Brûlé, Stéphane; Enoch, Stefan; Guenneau, Sébastien

2017-11-01

We begin with a brief historical survey of discoveries of quasi-crystals and graphene, and then introduce the concept of transformation crystallography, which consists of the application of geometric transforms to periodic structures. We consider motifs with three-fold, four-fold and six-fold symmetries according to the crystallographic restriction theorem. Furthermore, we define motifs with five-fold symmetry such as quasi-crystals generated by a cut-and-projection method from periodic structures in higher-dimensional space. We analyze elastic wave propagation in the transformed crystals and (Penrose-type) quasi-crystals with the finite difference time domain freeware SimSonic. We consider geometric transforms underpinning the design of seismic cloaks with square, circular, elliptical and peanut shapes in the context of honeycomb crystals that can be viewed as scaled-up versions of graphene. Interestingly, the use of morphing techniques leads to the design of cloaks with interpolated geometries reminiscent of Victor Vasarely’s artwork. Employing the case of transformed graphene-like (honeycomb) structures allows one to draw useful analogies between large-scale seismic metamaterials such as soils structured with columns of concrete or grout with soil and nanoscale biochemical metamaterials. We further identify similarities in designs of cloaks for elastodynamic and hydrodynamic waves and cloaks for diffusion (heat or mass) processes, as these are underpinned by geometric transforms. Experimental data extracted from field test analysis of soil structured with boreholes demonstrates the application of crystallography to large scale phononic crystals, coined as seismic metamaterials, as they might exhibit low frequency stop bands. This brings us to the outlook of mechanical metamaterials, with control of phonon emission in graphene through extreme anisotropy, attenuation of vibrations of suspension bridges via low frequency stop bands and the concept of transformed meta-cities. We conclude that these novel materials hold strong applications spanning different disciplines or across different scales from biophysics to geophysics.

Characterization of large-scale fluctuations and short-term variability of Seine river daily streamflow (France) over the period 1950-2008 by empirical mode decomposition and the Hilbert-Huang transform

NASA Astrophysics Data System (ADS)

Massei, N.; Fournier, M.

2010-12-01

Daily Seine river flow from 1950 to 2008 was analyzed using Hilbert-Huang Tranform (HHT). For the last ten years, this method which combines the so-called Empirical Mode Decomposition (EMD) multiresolution analysis and the Hilbert transform has proven its efficiency for the analysis of transient oscillatory signals, although the mathematical definition of the EMD is not totally established yet. HHT also provides an interesting alternative to other time-frequency or time-scale analysis of non-stationary signals, the most famous of which being wavelet-based approaches. In this application of HHT to the analysis of the hydrological variability of the Seine river, we seek to characterize the interannual patterns of daily flow, differenciate them from the short-term dynamics and eventually interpret them in the context of regional climate regime fluctuations. In this aim, HHT is also applied to the North-Atlantic Oscillation (NAO) through the annual winter-months NAO index time series. For both hydrological and climatic signals, dominant variability scales are extracted and their temporal variations analyzed by determination of the intantaneous frequency of each component. When compared to previous ones obtained from continuous wavelet transform (CWT) on the same data, HHT results highlighted the same scales and somewhat the same internal components for each signal. However, HHT allowed the identification and extraction of much more similar features during the 1950-2008 period (e.g., around 7-yr, between NAO and Seine flow than what was obtained from CWT, which comes to say that variability scales in flow likely to originate from climatic regime fluctuations were much properly identified in river flow. In addition, a more accurate determination of singularities in the natural processes analyzed were authorized by HHT compared to CWT, in which case the time-frequency resolution partly depends on the basic properties of the filter (i.e., the reference wavelet chosen initially). Compared to CWT or even to discrete wavelet multiresolution analysis, HHT is auto-adaptive, non-parametric, allows an orthogonal decomposition of the signal analyzed and provides a more accurate estimation of changing variability scales across time for highly transient signals.

Estimating leaf nitrogen accumulation in maize based on canopy hyperspectrum data

NASA Astrophysics Data System (ADS)

Gu, Xiaohe; Wang, Lizhi; Song, Xiaoyu; Xu, Xingang

2016-10-01

Leaf nitrogen accumulation (LNA) has important influence on the formation of crop yield and grain protein. Monitoring leaf nitrogen accumulation of crop canopy quantitively and real-timely is helpful for mastering crop nutrition status, diagnosing group growth and managing fertilization precisely. The study aimed to develop a universal method to monitor LNA of maize by hyperspectrum data, which could provide mechanism support for mapping LNA of maize at county scale. The correlations between LNA and hyperspectrum reflectivity and its mathematical transformations were analyzed. Then the feature bands and its transformations were screened to develop the optimal model of estimating LNA based on multiple linear regression method. The in-situ samples were used to evaluate the accuracy of the estimating model. Results showed that the estimating model with one differential logarithmic transformation (lgP') of reflectivity could reach highest correlation coefficient (0.889) with lowest RMSE (0.646 g·m-2), which was considered as the optimal model for estimating LNA in maize. The determination coefficient (R2) of testing samples was 0.831, while the RMSE was 1.901 g·m-2. It indicated that the one differential logarithmic transformation of hyperspectrum had good response with LNA of maize. Based on this transformation, the optimal estimating model of LNA could reach good accuracy with high stability.

The morphing of geographical features by Fourier transformation

PubMed Central

Liu, Pengcheng; Yu, Wenhao; Cheng, Xiaoqiang

2018-01-01

This paper presents a morphing model of vector geographical data based on Fourier transformation. This model involves three main steps. They are conversion from vector data to Fourier series, generation of intermediate function by combination of the two Fourier series concerning a large scale and a small scale, and reverse conversion from combination function to vector data. By mirror processing, the model can also be used for morphing of linear features. Experimental results show that this method is sensitive to scale variations and it can be used for vector map features’ continuous scale transformation. The efficiency of this model is linearly related to the point number of shape boundary and the interceptive value n of Fourier expansion. The effect of morphing by Fourier transformation is plausible and the efficiency of the algorithm is acceptable. PMID:29351344

Element analysis: a wavelet-based method for analysing time-localized events in noisy time series

PubMed Central

2017-01-01

A method is derived for the quantitative analysis of signals that are composed of superpositions of isolated, time-localized ‘events’. Here, these events are taken to be well represented as rescaled and phase-rotated versions of generalized Morse wavelets, a broad family of continuous analytic functions. Analysing a signal composed of replicates of such a function using another Morse wavelet allows one to directly estimate the properties of events from the values of the wavelet transform at its own maxima. The distribution of events in general power-law noise is determined in order to establish significance based on an expected false detection rate. Finally, an expression for an event’s ‘region of influence’ within the wavelet transform permits the formation of a criterion for rejecting spurious maxima due to numerical artefacts or other unsuitable events. Signals can then be reconstructed based on a small number of isolated points on the time/scale plane. This method, termed element analysis, is applied to the identification of long-lived eddy structures in ocean currents as observed by along-track measurements of sea surface elevation from satellite altimetry. PMID:28484325

Plasticity-mediated collapse and recrystallization in hollow copper nanowires: a molecular dynamics simulation

PubMed Central

Raychaudhuri, Arup Kumar; Saha-Dasgupta, Tanusri

2016-01-01

Summary We study the thermal stability of hollow copper nanowires using molecular dynamics simulation. We find that the plasticity-mediated structural evolution leads to transformation of the initial hollow structure to a solid wire. The process involves three distinct stages, namely, collapse, recrystallization and slow recovery. We calculate the time scales associated with different stages of the evolution process. Our findings suggest a plasticity-mediated mechanism of collapse and recrystallization. This contradicts the prevailing notion of diffusion driven transport of vacancies from the interior to outer surface being responsible for collapse, which would involve much longer time scales as compared to the plasticity-based mechanism. PMID:26977380

Nonreciprocity in the dynamics of coupled oscillators with nonlinearity, asymmetry, and scale hierarchy

NASA Astrophysics Data System (ADS)

Moore, Keegan J.; Bunyan, Jonathan; Tawfick, Sameh; Gendelman, Oleg V.; Li, Shuangbao; Leamy, Michael; Vakakis, Alexander F.

2018-01-01

In linear time-invariant dynamical and acoustical systems, reciprocity holds by the Onsager-Casimir principle of microscopic reversibility, and this can be broken only by odd external biases, nonlinearities, or time-dependent properties. A concept is proposed in this work for breaking dynamic reciprocity based on irreversible nonlinear energy transfers from large to small scales in a system with nonlinear hierarchical internal structure, asymmetry, and intentional strong stiffness nonlinearity. The resulting nonreciprocal large-to-small scale energy transfers mimic analogous nonlinear energy transfer cascades that occur in nature (e.g., in turbulent flows), and are caused by the strong frequency-energy dependence of the essentially nonlinear small-scale components of the system considered. The theoretical part of this work is mainly based on action-angle transformations, followed by direct numerical simulations of the resulting system of nonlinear coupled oscillators. The experimental part considers a system with two scales—a linear large-scale oscillator coupled to a small scale by a nonlinear spring—and validates the theoretical findings demonstrating nonreciprocal large-to-small scale energy transfer. The proposed study promotes a paradigm for designing nonreciprocal acoustic materials harnessing strong nonlinearity, which in a future application will be implemented in designing lattices incorporating nonlinear hierarchical internal structures, asymmetry, and scale mixing.

Robust preview control for a class of uncertain discrete-time systems with time-varying delay.

PubMed

Li, Li; Liao, Fucheng

2018-02-01

This paper proposes a concept of robust preview tracking control for uncertain discrete-time systems with time-varying delay. Firstly, a model transformation is employed for an uncertain discrete system with time-varying delay. Then, the auxiliary variables related to the system state and input are introduced to derive an augmented error system that includes future information on the reference signal. This leads to the tracking problem being transformed into a regulator problem. Finally, for the augmented error system, a sufficient condition of asymptotic stability is derived and the preview controller design method is proposed based on the scaled small gain theorem and linear matrix inequality (LMI) technique. The method proposed in this paper not only solves the difficulty problem of applying the difference operator to the time-varying matrices but also simplifies the structure of the augmented error system. The numerical simulation example also illustrates the effectiveness of the results presented in the paper. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Time Series of Tropical-Forest Structure from TanDEM-X, Transformed to Time Series of Biomass by MODIS

NASA Astrophysics Data System (ADS)

Treuhaft, R. N.; Baccini, A.; Goncalves, F. G.; Lei, Y.; Keller, M.; Walker, W. S.

2017-12-01

Tropical forests account for about 50% of the world's forested biomass, and play a critical role in the control of atmospheric carbon dioxide. Large-scale (1000's of km) changes in forest structure and biomass bear on global carbon source-sink dynamics, while small-scale (< 100 m) changes bear on deforestation and degradation monitoring. After describing the interferometric SAR (InSAR) phase-height observation, we show forest phase-height time series from the TanDEM-X radar interferometer at X-band (3 cm), taken with monthly and sub-hectare temporal and spatial resolution, respectively. The measurements were taken with more than 30 TanDEM-X passes over Tapajós National Forest in the Brazilian Amazon between 2011 and 2014. The transformation of phase-height rates into aboveground biomass (AGB) rates is based on the idea that the change in AGB due to a change in phase-height depends on the plot's AGB. Plots with higher AGB will produce more AGB for a given increase in height or phase-height. Postulating a power-law dependence of plot-level mass density on physical height, we previously found that the best conversion factors for transforming phase-height rate to AGB rate were indeed dependent on AGB. For 78 plots, we demonstrated AGB rates from InSAR phase-height rates using AGB from field measurements. For regional modeling of the Amazon Basin, field measurements of AGB, to specify the conversion factors, is impractical. Conversion factors from InSAR phase-height rate to AGB rate in this talk will be based on AGB derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). AGB measurement from MODIS is based on the spectral reflectance of 7 bands from the visible to short wave infrared, and auxiliary metrics describing the variance in reflectance. The mapping of MODIS reflectance to AGB is enabled by training a machine learning algorithm with lidar-derived AGB data, which are in turn trained by field measurements for small areas. The performance of TanDEM-X AGB rate from MODIS-derived conversion factors will be compared to that derived from field-based conversion factors. We will also attempt to improve phase-height rate to AGB rate transformation by deriving improved models of mass density dependences on height, based on the aggregation of single-stem allometrics.

Invariant object recognition based on the generalized discrete radon transform

NASA Astrophysics Data System (ADS)

Easley, Glenn R.; Colonna, Flavia

2004-04-01

We introduce a method for classifying objects based on special cases of the generalized discrete Radon transform. We adjust the transform and the corresponding ridgelet transform by means of circular shifting and a singular value decomposition (SVD) to obtain a translation, rotation and scaling invariant set of feature vectors. We then use a back-propagation neural network to classify the input feature vectors. We conclude with experimental results and compare these with other invariant recognition methods.

A scale-invariant internal representation of time.

PubMed

Shankar, Karthik H; Howard, Marc W

2012-01-01

We propose a principled way to construct an internal representation of the temporal stimulus history leading up to the present moment. A set of leaky integrators performs a Laplace transform on the stimulus function, and a linear operator approximates the inversion of the Laplace transform. The result is a representation of stimulus history that retains information about the temporal sequence of stimuli. This procedure naturally represents more recent stimuli more accurately than less recent stimuli; the decrement in accuracy is precisely scale invariant. This procedure also yields time cells that fire at specific latencies following the stimulus with a scale-invariant temporal spread. Combined with a simple associative memory, this representation gives rise to a moment-to-moment prediction that is also scale invariant in time. We propose that this scale-invariant representation of temporal stimulus history could serve as an underlying representation accessible to higher-level behavioral and cognitive mechanisms. In order to illustrate the potential utility of this scale-invariant representation in a variety of fields, we sketch applications using minimal performance functions to problems in classical conditioning, interval timing, scale-invariant learning in autoshaping, and the persistence of the recency effect in episodic memory across timescales.

Iterated oversampled filter banks and wavelet frames

NASA Astrophysics Data System (ADS)

Selesnick, Ivan W.; Sendur, Levent

2000-12-01

This paper takes up the design of wavelet tight frames that are analogous to Daubechies orthonormal wavelets - that is, the design of minimal length wavelet filters satisfying certain polynomial properties, but now in the oversampled case. The oversampled dyadic DWT considered in this paper is based on a single scaling function and tow distinct wavelets. Having more wavelets than necessary gives a closer spacing between adjacent wavelets within the same scale. As a result, the transform is nearly shift-invariant, and can be used to improve denoising. Because the associated time- frequency lattice preserves the dyadic structure of the critically sampled DWT it can be used with tree-based denoising algorithms that exploit parent-child correlation.

Inverse transformation: unleashing spatially heterogeneous dynamics with an alternative approach to XPCS data analysis

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

Andrews, Ross N.; Narayanan, Suresh; Zhang, Fan

X-ray photon correlation spectroscopy (XPCS), an extension of dynamic light scattering (DLS) in the X-ray regime, detects temporal intensity fluctuations of coherent speckles and provides scattering-vector-dependent sample dynamics at length scales smaller than DLS. The penetrating power of X-rays enables XPCS to probe the dynamics in a broad array of materials, including polymers, glasses and metal alloys, where attempts to describe the dynamics with a simple exponential fit usually fail. In these cases, the prevailing XPCS data analysis approach employs stretched or compressed exponential decay functions (Kohlrausch functions), which implicitly assume homogeneous dynamics. This paper proposes an alternative analysis schememore » based upon inverse Laplace or Gaussian transformation for elucidating heterogeneous distributions of dynamic time scales in XPCS, an approach analogous to the CONTIN algorithm widely accepted in the analysis of DLS from polydisperse and multimodal systems. In conclusion, using XPCS data measured from colloidal gels, it is demonstrated that the inverse transform approach reveals hidden multimodal dynamics in materials, unleashing the full potential of XPCS.« less

Shift-, rotation-, and scale-invariant shape recognition system using an optical Hough transform

NASA Astrophysics Data System (ADS)

Schmid, Volker R.; Bader, Gerhard; Lueder, Ernst H.

1998-02-01

We present a hybrid shape recognition system with an optical Hough transform processor. The features of the Hough space offer a separate cancellation of distortions caused by translations and rotations. Scale invariance is also provided by suitable normalization. The proposed system extends the capabilities of Hough transform based detection from only straight lines to areas bounded by edges. A very compact optical design is achieved by a microlens array processor accepting incoherent light as direct optical input and realizing the computationally expensive connections massively parallel. Our newly developed algorithm extracts rotation and translation invariant normalized patterns of bright spots on a 2D grid. A neural network classifier maps the 2D features via a nonlinear hidden layer onto the classification output vector. We propose initialization of the connection weights according to regions of activity specifically assigned to each neuron in the hidden layer using a competitive network. The presented system is designed for industry inspection applications. Presently we have demonstrated detection of six different machined parts in real-time. Our method yields very promising detection results of more than 96% correctly classified parts.

Inverse transformation: unleashing spatially heterogeneous dynamics with an alternative approach to XPCS data analysis

DOE PAGES

Andrews, Ross N.; Narayanan, Suresh; Zhang, Fan; ...

2018-02-01

X-ray photon correlation spectroscopy (XPCS), an extension of dynamic light scattering (DLS) in the X-ray regime, detects temporal intensity fluctuations of coherent speckles and provides scattering-vector-dependent sample dynamics at length scales smaller than DLS. The penetrating power of X-rays enables XPCS to probe the dynamics in a broad array of materials, including polymers, glasses and metal alloys, where attempts to describe the dynamics with a simple exponential fit usually fail. In these cases, the prevailing XPCS data analysis approach employs stretched or compressed exponential decay functions (Kohlrausch functions), which implicitly assume homogeneous dynamics. This paper proposes an alternative analysis schememore » based upon inverse Laplace or Gaussian transformation for elucidating heterogeneous distributions of dynamic time scales in XPCS, an approach analogous to the CONTIN algorithm widely accepted in the analysis of DLS from polydisperse and multimodal systems. In conclusion, using XPCS data measured from colloidal gels, it is demonstrated that the inverse transform approach reveals hidden multimodal dynamics in materials, unleashing the full potential of XPCS.« less

Distinctive Feature Extraction for Indian Sign Language (ISL) Gesture using Scale Invariant Feature Transform (SIFT)

NASA Astrophysics Data System (ADS)

Patil, Sandeep Baburao; Sinha, G. R.

2017-02-01

India, having less awareness towards the deaf and dumb peoples leads to increase the communication gap between deaf and hard hearing community. Sign language is commonly developed for deaf and hard hearing peoples to convey their message by generating the different sign pattern. The scale invariant feature transform was introduced by David Lowe to perform reliable matching between different images of the same object. This paper implements the various phases of scale invariant feature transform to extract the distinctive features from Indian sign language gestures. The experimental result shows the time constraint for each phase and the number of features extracted for 26 ISL gestures.

Automating multistep flow synthesis: approach and challenges in integrating chemistry, machines and logic

PubMed Central

Shukla, Chinmay A

2017-01-01

The implementation of automation in the multistep flow synthesis is essential for transforming laboratory-scale chemistry into a reliable industrial process. In this review, we briefly introduce the role of automation based on its application in synthesis viz. auto sampling and inline monitoring, optimization and process control. Subsequently, we have critically reviewed a few multistep flow synthesis and suggested a possible control strategy to be implemented so that it helps to reliably transfer the laboratory-scale synthesis strategy to a pilot scale at its optimum conditions. Due to the vast literature in multistep synthesis, we have classified the literature and have identified the case studies based on few criteria viz. type of reaction, heating methods, processes involving in-line separation units, telescopic synthesis, processes involving in-line quenching and process with the smallest time scale of operation. This classification will cover the broader range in the multistep synthesis literature. PMID:28684977

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

Espinosa-Paredes, Gilberto; Prieto-Guerrero, Alfonso; Nunez-Carrera, Alejandro

This paper introduces a wavelet-based method to analyze instability events in a boiling water reactor (BWR) during transient phenomena. The methodology to analyze BWR signals includes the following: (a) the short-time Fourier transform (STFT) analysis, (b) decomposition using the continuous wavelet transform (CWT), and (c) application of multiresolution analysis (MRA) using discrete wavelet transform (DWT). STFT analysis permits the study, in time, of the spectral content of analyzed signals. The CWT provides information about ruptures, discontinuities, and fractal behavior. To detect these important features in the signal, a mother wavelet has to be chosen and applied at several scales tomore » obtain optimum results. MRA allows fast implementation of the DWT. Features like important frequencies, discontinuities, and transients can be detected with analysis at different levels of detail coefficients. The STFT was used to provide a comparison between a classic method and the wavelet-based method. The damping ratio, which is an important stability parameter, was calculated as a function of time. The transient behavior can be detected by analyzing the maximum contained in detail coefficients at different levels in the signal decomposition. This method allows analysis of both stationary signals and highly nonstationary signals in the timescale plane. This methodology has been tested with the benchmark power instability event of Laguna Verde nuclear power plant (NPP) Unit 1, which is a BWR-5 NPP.« less

Empirical mode decomposition apparatus, method and article of manufacture for analyzing biological signals and performing curve fitting

NASA Technical Reports Server (NTRS)

Huang, Norden E. (Inventor)

2004-01-01

A computer implemented physical signal analysis method includes four basic steps and the associated presentation techniques of the results. The first step is a computer implemented Empirical Mode Decomposition that extracts a collection of Intrinsic Mode Functions (IMF) from nonlinear, nonstationary physical signals. The decomposition is based on the direct extraction of the energy associated with various intrinsic time scales in the physical signal. Expressed in the IMF's, they have well-behaved Hilbert Transforms from which instantaneous frequencies can be calculated. The second step is the Hilbert Transform which produces a Hilbert Spectrum. Thus, the invention can localize any event on the time as well as the frequency axis. The decomposition can also be viewed as an expansion of the data in terms of the IMF's. Then, these IMF's, based on and derived from the data, can serve as the basis of that expansion. The local energy and the instantaneous frequency derived from the IMF's through the Hilbert transform give a full energy-frequency-time distribution of the data which is designated as the Hilbert Spectrum. The third step filters the physical signal by combining a subset of the IMFs. In the fourth step, a curve may be fitted to the filtered signal which may not have been possible with the original, unfiltered signal.

Empirical mode decomposition apparatus, method and article of manufacture for analyzing biological signals and performing curve fitting

NASA Technical Reports Server (NTRS)

Huang, Norden E. (Inventor)

2002-01-01

A computer implemented physical signal analysis method includes four basic steps and the associated presentation techniques of the results. The first step is a computer implemented Empirical Mode Decomposition that extracts a collection of Intrinsic Mode Functions (IMF) from nonlinear, nonstationary physical signals. The decomposition is based on the direct extraction of the energy associated with various intrinsic time scales in the physical signal. Expressed in the IMF's, they have well-behaved Hilbert Transforms from which instantaneous frequencies can be calculated. The second step is the Hilbert Transform which produces a Hilbert Spectrum. Thus, the invention can localize any event on the time as well as the frequency axis. The decomposition can also be viewed as an expansion of the data in terms of the IMF's. Then, these IMF's, based on and derived from the data, can serve as the basis of that expansion. The local energy and the instantaneous frequency derived from the IMF's through the Hilbert transform give a full energy-frequency-time distribution of the data which is designated as the Hilbert Spectrum. The third step filters the physical signal by combining a subset of the IMFs. In the fourth step, a curve may be fitted to the filtered signal which may not have been possible with the original, unfiltered signal.

Properties of an improved Gabor wavelet transform and its applications to seismic signal processing and interpretation

NASA Astrophysics Data System (ADS)

Ji, Zhan-Huai; Yan, Sheng-Gang

2017-12-01

This paper presents an analytical study of the complete transform of improved Gabor wavelets (IGWs), and discusses its application to the processing and interpretation of seismic signals. The complete Gabor wavelet transform has the following properties. First, unlike the conventional transform, the improved Gabor wavelet transform (IGWT) maps time domain signals to the time-frequency domain instead of the time-scale domain. Second, the IGW's dominant frequency is fixed, so the transform can perform signal frequency division, where the dominant frequency components of the extracted sub-band signal carry essentially the same information as the corresponding components of the original signal, and the subband signal bandwidth can be regulated effectively by the transform's resolution factor. Third, a time-frequency filter consisting of an IGWT and its inverse transform can accurately locate target areas in the time-frequency field and perform filtering in a given time-frequency range. The complete IGW transform's properties are investigated using simulation experiments and test cases, showing positive results for seismic signal processing and interpretation, such as enhancing seismic signal resolution, permitting signal frequency division, and allowing small faults to be identified.

Scaling properties of foreign exchange volatility

NASA Astrophysics Data System (ADS)

Gençay, Ramazan; Selçuk, Faruk; Whitcher, Brandon

2001-01-01

In this paper, we investigate the scaling properties of foreign exchange volatility. Our methodology is based on a wavelet multi-scaling approach which decomposes the variance of a time series and the covariance between two time series on a scale by scale basis through the application of a discrete wavelet transformation. It is shown that foreign exchange rate volatilities follow different scaling laws at different horizons. Particularly, there is a smaller degree of persistence in intra-day volatility as compared to volatility at one day and higher scales. Therefore, a common practice in the risk management industry to convert risk measures calculated at shorter horizons into longer horizons through a global scaling parameter may not be appropriate. This paper also demonstrates that correlation between the foreign exchange volatilities is the lowest at the intra-day scales but exhibits a gradual increase up to a daily scale. The correlation coefficient stabilizes at scales one day and higher. Therefore, the benefit of currency diversification is the greatest at the intra-day scales and diminishes gradually at higher scales (lower frequencies). The wavelet cross-correlation analysis also indicates that the association between two volatilities is stronger at lower frequencies.

Algorithm for the classification of multi-modulating signals on the electrocardiogram.

PubMed

Mita, Mitsuo

2007-03-01

This article discusses the algorithm to measure electrocardiogram (ECG) and respiration simultaneously and to have the diagnostic potentiality for sleep apnoea from ECG recordings. The algorithm is composed by the combination with the three particular scale transform of a(j)(t), u(j)(t), o(j)(a(j)) and the statistical Fourier transform (SFT). Time and magnitude scale transforms of a(j)(t), u(j)(t) change the source into the periodic signal and tau(j) = o(j)(a(j)) confines its harmonics into a few instantaneous components at tau(j) being a common instant on two scales between t and tau(j). As a result, the multi-modulating source is decomposed by the SFT and is reconstructed into ECG, respiration and the other signals by inverse transform. The algorithm is expected to get the partial ventilation and the heart rate variability from scale transforms among a(j)(t), a(j+1)(t) and u(j+1)(t) joining with each modulation. The algorithm has a high potentiality of the clinical checkup for the diagnosis of sleep apnoea from ECG recordings.

Transformation for Adults in an Internet-Based Learning Environment--Is It Necessary to Be Self-Directed?

ERIC Educational Resources Information Center

Chu, Regina Juchun; Chu, Anita Zichun; Weng, Cathy; Tsai, Chin-Chung; Lin, Chia-chun

2012-01-01

This research explores the relationships between self-directed learning readiness and transformative learning theory (TLT) reflected by the Constructivist Internet-based Learning Environment Scale (CILES). A questionnaire survey about adult learner's perceptions of Internet-based learning was administered to adults enrolled in classes in community…

A New Scheme for the Design of Hilbert Transform Pairs of Biorthogonal Wavelet Bases

NASA Astrophysics Data System (ADS)

Shi, Hongli; Luo, Shuqian

2010-12-01

In designing the Hilbert transform pairs of biorthogonal wavelet bases, it has been shown that the requirements of the equal-magnitude responses and the half-sample phase offset on the lowpass filters are the necessary and sufficient condition. In this paper, the relationship between the phase offset and the vanishing moment difference of biorthogonal scaling filters is derived, which implies a simple way to choose the vanishing moments so that the phase response requirement can be satisfied structurally. The magnitude response requirement is approximately achieved by a constrained optimization procedure, where the objective function and constraints are all expressed in terms of the auxiliary filters of scaling filters rather than the scaling filters directly. Generally, the calculation burden in the design implementation will be less than that of the current schemes. The integral of magnitude response difference between the primal and dual scaling filters has been chosen as the objective function, which expresses the magnitude response requirements in the whole frequency range. Two design examples illustrate that the biorthogonal wavelet bases designed by the proposed scheme are very close to Hilbert transform pairs.

Detecting oscillatory patterns and time lags from proxy records with non-uniform sampling: Some pitfalls and possible solutions

NASA Astrophysics Data System (ADS)

Donner, Reik

2013-04-01

Time series analysis offers a rich toolbox for deciphering information from high-resolution geological and geomorphological archives and linking the thus obtained results to distinct climate and environmental processes. Specifically, on various time-scales from inter-annual to multi-millenial, underlying driving forces exhibit more or less periodic oscillations, the detection of which in proxy records often allows linking them to specific mechanisms by which the corresponding drivers may have affected the archive under study. A persistent problem in geomorphology is that available records do not present a clear signal of the variability of environmental conditions, but exhibit considerable uncertainties of both the measured proxy variables and the associated age model. Particularly, time-scale uncertainty as well as the heterogeneity of sampling in the time domain are source of severe conceptual problems that may lead to false conclusions about the presence or absence of oscillatory patterns and their mutual phasing in different archives. In my presentation, I will discuss how one can cope with non-uniformly sampled proxy records to detect and quantify oscillatory patterns in one or more data sets. For this purpose, correlation analysis is reformulated using kernel estimates which are found superior to classical estimators based on interpolation or Fourier transform techniques. In order to characterize non-stationary or noisy periodicities and their relative phasing between different records, an extension of continuous wavelet transform is utilized. The performance of both methods is illustrated for different case studies. An extension to explicitly considering time-scale uncertainties by means of Bayesian techniques is briefly outlined.

Optimal Control Modification for Robust Adaptation of Singularly Perturbed Systems with Slow Actuators

NASA Technical Reports Server (NTRS)

Nguyen, Nhan T.; Ishihara, Abraham; Stepanyan, Vahram; Boskovic, Jovan

2009-01-01

Recently a new optimal control modification has been introduced that can achieve robust adaptation with a large adaptive gain without incurring high-frequency oscillations as with the standard model-reference adaptive control. This modification is based on an optimal control formulation to minimize the L2 norm of the tracking error. The optimal control modification adaptive law results in a stable adaptation in the presence of a large adaptive gain. This study examines the optimal control modification adaptive law in the context of a system with a time scale separation resulting from a fast plant with a slow actuator. A singular perturbation analysis is performed to derive a modification to the adaptive law by transforming the original system into a reduced-order system in slow time. The model matching conditions in the transformed time coordinate results in increase in the feedback gain and modification of the adaptive law.

Stress in highly demanding IT jobs: transformational leadership moderates the impact of time pressure on exhaustion and work-life balance.

PubMed

Syrek, Christine J; Apostel, Ella; Antoni, Conny H

2013-07-01

The objective of this article is to investigate transformational leadership as a potential moderator of the negative relationship of time pressure to work-life balance and of the positive relationship between time pressure and exhaustion. Recent research regards time pressure as a challenge stressor; while being positively related to motivation and performance, time pressure also increases employee strain and decreases well-being. Building on the Job Demand-Resources model, we hypothesize that transformational leadership moderates the relationships between time pressure and both employees' exhaustion and work-life balance such that both relationships will be weaker when transformational leadership is higher. Of seven information technology organizations in Germany, 262 employees participated in the study. Established scales for time pressure, transformational leadership, work-life balance, and exhaustion were used, all showing good internal consistencies. The results support our assumptions. Specifically, we find that under high transformational leadership the impact of time pressure on exhaustion and work-life balance was less strong. The results of this study suggest that, particularly under high time pressure, transformational leadership is an important factor for both employees' work-life balance and exhaustion. PsycINFO Database Record (c) 2013 APA, all rights reserved.

MRS3D: 3D Spherical Wavelet Transform on the Sphere

NASA Astrophysics Data System (ADS)

Lanusse, F.; Rassat, A.; Starck, J.-L.

2011-12-01

Future cosmological surveys will provide 3D large scale structure maps with large sky coverage, for which a 3D Spherical Fourier-Bessel (SFB) analysis is natural. Wavelets are particularly well-suited to the analysis and denoising of cosmological data, but a spherical 3D isotropic wavelet transform does not currently exist to analyse spherical 3D data. We present a new fast Discrete Spherical Fourier-Bessel Transform (DSFBT) based on both a discrete Bessel Transform and the HEALPIX angular pixelisation scheme. We tested the 3D wavelet transform and as a toy-application, applied a denoising algorithm in wavelet space to the Virgo large box cosmological simulations and found we can successfully remove noise without much loss to the large scale structure. The new spherical 3D isotropic wavelet transform, called MRS3D, is ideally suited to analysing and denoising future 3D spherical cosmological surveys; it uses a novel discrete spherical Fourier-Bessel Transform. MRS3D is based on two packages, IDL and Healpix and can be used only if these two packages have been installed.

How do people transform landscapes? A sociological perspective on suburban sprawl and tropical deforestation.

PubMed

Rudel, Thomas K

2009-07-01

Humans transformed landscapes at an unprecedented scale and pace during the 20th century, creating sprawling urban areas in affluent countries and large-scale agricultural expanses in tropics. To date, attempts to explain these processes in other disciplines have had a disembodied, a historical quality to them. A sociological account of these changes emphasizes the role of strategic actions by states and coalitions of interested parties in transforming landscapes. It identifies the agents of change and the timing of transformative events. Case studies of suburban sprawl and tropical deforestation illustrate the value of the sociological approach and the wide range of situations to which it applies.

An innovative approach for characteristic analysis and state-of-health diagnosis for a Li-ion cell based on the discrete wavelet transform

NASA Astrophysics Data System (ADS)

Kim, Jonghoon; Cho, B. H.

2014-08-01

This paper introduces an innovative approach to analyze electrochemical characteristics and state-of-health (SOH) diagnosis of a Li-ion cell based on the discrete wavelet transform (DWT). In this approach, the DWT has been applied as a powerful tool in the analysis of the discharging/charging voltage signal (DCVS) with non-stationary and transient phenomena for a Li-ion cell. Specifically, DWT-based multi-resolution analysis (MRA) is used for extracting information on the electrochemical characteristics in both time and frequency domain simultaneously. Through using the MRA with implementation of the wavelet decomposition, the information on the electrochemical characteristics of a Li-ion cell can be extracted from the DCVS over a wide frequency range. Wavelet decomposition based on the selection of the order 3 Daubechies wavelet (dB3) and scale 5 as the best wavelet function and the optimal decomposition scale is implemented. In particular, this present approach develops these investigations one step further by showing low and high frequency components (approximation component An and detail component Dn, respectively) extracted from variable Li-ion cells with different electrochemical characteristics caused by aging effect. Experimental results show the clearness of the DWT-based approach for the reliable diagnosis of the SOH for a Li-ion cell.

Real-time mapping of the corneal sub-basal nerve plexus by in vivo laser scanning confocal microscopy

NASA Astrophysics Data System (ADS)

Guthoff, Rudolf F.; Zhivov, Andrey; Stachs, Oliver

2010-02-01

The aim of the study was to produce two-dimensional reconstruction maps of the living corneal sub-basal nerve plexus by in vivo laser scanning confocal microscopy in real time. CLSM source data (frame rate 30Hz, 384x384 pixel) were used to create large-scale maps of the scanned area by selecting the Automatic Real Time (ART) composite mode. The mapping algorithm is based on an affine transformation. Microscopy of the sub-basal nerve plexus was performed on normal and LASIK eyes as well as on rabbit eyes. Real-time mapping of the sub-basal nerve plexus was performed in large-scale up to a size of 3.2mm x 3.2mm. The developed method enables a real-time in vivo mapping of the sub-basal nerve plexus which is stringently necessary for statistically firmed conclusions about morphometric plexus alterations.

The Wavelet ToolKat: A set of tools for the analysis of series through wavelet transforms. Application to the channel curvature and the slope control of three free meandering rivers in the Amazon basin.

NASA Astrophysics Data System (ADS)

Vaudor, Lise; Piegay, Herve; Wawrzyniak, Vincent; Spitoni, Marie

2016-04-01

The form and functioning of a geomorphic system result from processes operating at various spatial and temporal scales. Longitudinal channel characteristics thus exhibit complex patterns which vary according to the scale of study, might be periodic or segmented, and are generally blurred by noise. Describing the intricate, multiscale structure of such signals, and identifying at which scales the patterns are dominant and over which sub-reach, could help determine at which scales they should be investigated, and provide insights into the main controlling factors. Wavelet transforms aim at describing data at multiple scales (either in time or space), and are now exploited in geophysics for the analysis of nonstationary series of data. They provide a consistent, non-arbitrary, and multiscale description of a signal's variations and help explore potential causalities. Nevertheless, their use in fluvial geomorphology, notably to study longitudinal patterns, is hindered by a lack of user-friendly tools to help understand, implement, and interpret them. We have developed a free application, The Wavelet ToolKat, designed to facilitate the use of wavelet transforms on temporal or spatial series. We illustrate its usefulness describing longitudinal channel curvature and slope of three freely meandering rivers in the Amazon basin (the Purus, Juruá and Madre de Dios rivers), using topographic data generated from NASA's Shuttle Radar Topography Mission (SRTM) in 2000. Three types of wavelet transforms are used, with different purposes. Continuous Wavelet Transforms are used to identify in a non-arbitrary way the dominant scales and locations at which channel curvature and slope vary. Cross-wavelet transforms, and wavelet coherence and phase are used to identify scales and locations exhibiting significant channel curvature and slope co-variations. Maximal Overlap Discrete Wavelet Transforms decompose data into their variations at a series of scales and are used to provide smoothed descriptions of the series at the scales deemed relevant.

Study of Fourier transform spectrometer based on Michelson interferometer wave-meter

NASA Astrophysics Data System (ADS)

Peng, Yuexiang; Wang, Liqiang; Lin, Li

2008-03-01

A wave-meter based on Michelson interferometer consists of a reference and a measurement channel. The voice-coiled motor using PID means can realize to move in stable motion. The wavelength of a measurement laser can be obtained by counting interference fringes of reference and measurement laser. Reference laser with frequency stabilization creates a cosine interferogram signal whose frequency is proportional to velocity of the moving motor. The interferogram of the reference laser is converted to pulse signal, and it is subdivided into 16 times. In order to get optical spectrum, the analog signal of measurement channel should be collected. The Analog-to-Digital Converter (ADC) for measurement channel is triggered by the 16-times pulse signal of reference laser. So the sampling rate is constant only depending on frequency of reference laser and irrelative to the motor velocity. This means the sampling rate of measurement channel signals is on a uniform time-scale. The optical spectrum of measurement channel can be processed with Fast Fourier Transform (FFT) method by DSP and displayed on LCD.

Segmentation-based wavelet transform for still-image compression

NASA Astrophysics Data System (ADS)

Mozelle, Gerard; Seghier, Abdellatif; Preteux, Francoise J.

1996-10-01

In order to address simultaneously the two functionalities, content-based scalability required by MPEG-4, we introduce a segmentation-based wavelet transform (SBWT). SBWT takes into account both the mathematical properties of multiresolution analysis and the flexibility of region-based approaches for image compression. The associated methodology has two stages: 1) image segmentation into convex and polygonal regions; 2) 2D-wavelet transform of the signal corresponding to each region. In this paper, we have mathematically studied a method for constructing a multiresolution analysis (VjOmega)j (epsilon) N adapted to a polygonal region which provides an adaptive region-based filtering. The explicit construction of scaling functions, pre-wavelets and orthonormal wavelets bases defined on a polygon is carried out by using scaling functions is established by using the theory of Toeplitz operators. The corresponding expression can be interpreted as a location property which allow defining interior and boundary scaling functions. Concerning orthonormal wavelets and pre-wavelets, a similar expansion is obtained by taking advantage of the properties of the orthogonal projector P(V(j(Omega )) perpendicular from the space Vj(Omega ) + 1 onto the space (Vj(Omega )) perpendicular. Finally the mathematical results provide a simple and fast algorithm adapted to polygonal regions.

Oil Spill Detection and Tracking Using Lipschitz Regularity and Multiscale Techniques in Synthetic Aperture Radar Imagery

NASA Astrophysics Data System (ADS)

Ajadi, O. A.; Meyer, F. J.

2014-12-01

Automatic oil spill detection and tracking from Synthetic Aperture Radar (SAR) images is a difficult task, due in large part to the inhomogeneous properties of the sea surface, the high level of speckle inherent in SAR data, the complexity and the highly non-Gaussian nature of amplitude information, and the low temporal sampling that is often achieved with SAR systems. This research presents a promising new oil spill detection and tracking method that is based on time series of SAR images. Through the combination of a number of advanced image processing techniques, the develop approach is able to mitigate some of these previously mentioned limitations of SAR-based oil-spill detection and enables fully automatic spill detection and tracking across a wide range of spatial scales. The method combines an initial automatic texture analysis with a consecutive change detection approach based on multi-scale image decomposition. The first step of the approach, a texture transformation of the original SAR images, is performed in order to normalize the ocean background and enhance the contrast between oil-covered and oil-free ocean surfaces. The Lipschitz regularity (LR), a local texture parameter, is used here due to its proven ability to normalize the reflectivity properties of ocean water and maximize the visibly of oil in water. To calculate LR, the images are decomposed using two-dimensional continuous wavelet transform (2D-CWT), and transformed into Holder space to measure LR. After texture transformation, the now normalized images are inserted into our multi-temporal change detection algorithm. The multi-temporal change detection approach is a two-step procedure including (1) data enhancement and filtering and (2) multi-scale automatic change detection. The performance of the developed approach is demonstrated by an application to oil spill areas in the Gulf of Mexico. In this example, areas affected by oil spills were identified from a series of ALOS PALSAR images acquired in 2010. The comparison showed exceptional performance of our method. This method can be applied to emergency management and decision support systems with a need for real-time data, and it shows great potential for rapid data analysis in other areas, including volcano detection, flood boundaries, forest health, and wildfires.

On hydrostatic flows in isentropic coordinates

NASA Astrophysics Data System (ADS)

Bokhove, Onno

2000-01-01

The hydrostatic primitive equations of motion which have been used in large-scale weather prediction and climate modelling over the last few decades are analysed with variational methods in an isentropic Eulerian framework. The use of material isentropic coordinates for the Eulerian hydrostatic equations is known to have distinct conceptual advantages since fluid motion is, under inviscid and statically stable circumstances, confined to take place on quasi-horizontal isentropic surfaces. First, an Eulerian isentropic Hamilton's principle, expressed in terms of fluid parcel variables, is therefore derived by transformation of a Lagrangian Hamilton's principle to an Eulerian one. This Eulerian principle explicitly describes the boundary dynamics of the time-dependent domain in terms of advection of boundary isentropes sB; these are the values the isentropes have at their intersection with the (lower) boundary. A partial Legendre transform for only the interior variables yields an Eulerian ‘action’ principle. Secondly, Noether's theorem is used to derive energy and potential vorticity conservation from the Eulerian Hamilton's principle. Thirdly, these conservation laws are used to derive a wave-activity invariant which is second-order in terms of small-amplitude disturbances relative to a resting or moving basic state. Linear stability criteria are derived but only for resting basic states. In mid-latitudes a time- scale separation between gravity and vortical modes occurs. Finally, this time-scale separation suggests that conservative geostrophic and ageostrophic approximations can be made to the Eulerian action principle for hydrostatic flows. Approximations to Eulerian variational principles may be more advantageous than approximations to Lagrangian ones because non-dimensionalization and scaling tend to be based on Eulerian estimates of the characteristic scales involved. These approximations to the stratified hydrostatic formulation extend previous approximations to the shallow- water equations. An explicit variational derivation is given of an isentropic version of Hoskins & Bretherton's model for atmospheric fronts.

Real-time detection of organic contamination events in water distribution systems by principal components analysis of ultraviolet spectral data.

PubMed

Zhang, Jian; Hou, Dibo; Wang, Ke; Huang, Pingjie; Zhang, Guangxin; Loáiciga, Hugo

2017-05-01

The detection of organic contaminants in water distribution systems is essential to protect public health from potential harmful compounds resulting from accidental spills or intentional releases. Existing methods for detecting organic contaminants are based on quantitative analyses such as chemical testing and gas/liquid chromatography, which are time- and reagent-consuming and involve costly maintenance. This study proposes a novel procedure based on discrete wavelet transform and principal component analysis for detecting organic contamination events from ultraviolet spectral data. Firstly, the spectrum of each observation is transformed using discrete wavelet with a coiflet mother wavelet to capture the abrupt change along the wavelength. Principal component analysis is then employed to approximate the spectra based on capture and fusion features. The significant value of Hotelling's T 2 statistics is calculated and used to detect outliers. An alarm of contamination event is triggered by sequential Bayesian analysis when the outliers appear continuously in several observations. The effectiveness of the proposed procedure is tested on-line using a pilot-scale setup and experimental data.

Multifractals embedded in short time series: An unbiased estimation of probability moment

NASA Astrophysics Data System (ADS)

Qiu, Lu; Yang, Tianguang; Yin, Yanhua; Gu, Changgui; Yang, Huijie

2016-12-01

An exact estimation of probability moments is the base for several essential concepts, such as the multifractals, the Tsallis entropy, and the transfer entropy. By means of approximation theory we propose a new method called factorial-moment-based estimation of probability moments. Theoretical prediction and computational results show that it can provide us an unbiased estimation of the probability moments of continuous order. Calculations on probability redistribution model verify that it can extract exactly multifractal behaviors from several hundred recordings. Its powerfulness in monitoring evolution of scaling behaviors is exemplified by two empirical cases, i.e., the gait time series for fast, normal, and slow trials of a healthy volunteer, and the closing price series for Shanghai stock market. By using short time series with several hundred lengths, a comparison with the well-established tools displays significant advantages of its performance over the other methods. The factorial-moment-based estimation can evaluate correctly the scaling behaviors in a scale range about three generations wider than the multifractal detrended fluctuation analysis and the basic estimation. The estimation of partition function given by the wavelet transform modulus maxima has unacceptable fluctuations. Besides the scaling invariance focused in the present paper, the proposed factorial moment of continuous order can find its various uses, such as finding nonextensive behaviors of a complex system and reconstructing the causality relationship network between elements of a complex system.

Hydrograph structure informed calibration in the frequency domain with time localization

NASA Astrophysics Data System (ADS)

Kumarasamy, K.; Belmont, P.

2015-12-01

Complex models with large number of parameters are commonly used to estimate sediment yields and predict changes in sediment loads as a result of changes in management or conservation practice at large watershed (>2000 km2) scales. As sediment yield is a strongly non-linear function that responds to channel (peak or mean) velocity or flow depth, it is critical to accurately represent flows. The process of calibration in such models (e.g., SWAT) generally involves the adjustment of several parameters to obtain better estimates of goodness of fit metrics such as Nash Sutcliff Efficiency (NSE). However, such indicators only provide a global view of model performance, potentially obscuring accuracy of the timing or magnitude of specific flows of interest. We describe an approach for streamflow calibration that will greatly reduce the black-box nature of calibration, when response from a parameter adjustment is not clearly known. Fourier Transform or the Short Term Fourier Transform could be used to characterize model performance in the frequency domain as well, however, the ambiguity of a Fourier transform with regards to time localization renders its implementation in a model calibration setting rather useless. Brief and sudden changes (e.g. stream flow peaks) in signals carry the most interesting information from parameter adjustments, which are completely lost in the transform without time localization. Wavelet transform captures the frequency component in the signal without compromising time and is applied to contrast changes in signal response to parameter adjustments. Here we employ the mother wavelet called the Mexican hat wavelet and apply a Continuous Wavelet Transform to understand the signal in the frequency domain. Further, with the use of the cross-wavelet spectrum we examine the relationship between the two signals (prior or post parameter adjustment) in the time-scale plane (e.g., lower scales correspond to higher frequencies). The non-stationarity of the streamflow signal does not hinder this assessment and regions of change called boundaries of influence (seasons or time when such change occurs in the hydrograph) for each parameter are delineated. In addition, we can discover the structural component of the signal (e.g., shifts or amplitude change) that has changed.

Dissociating object-based from egocentric transformations in mental body rotation: effect of stimuli size.

PubMed

Habacha, Hamdi; Moreau, David; Jarraya, Mohamed; Lejeune-Poutrain, Laure; Molinaro, Corinne

2018-01-01

The effect of stimuli size on the mental rotation of abstract objects has been extensively investigated, yet its effect on the mental rotation of bodily stimuli remains largely unexplored. Depending on the experimental design, mentally rotating bodily stimuli can elicit object-based transformations, relying mainly on visual processes, or egocentric transformations, which typically involve embodied motor processes. The present study included two mental body rotation tasks requiring either a same-different or a laterality judgment, designed to elicit object-based or egocentric transformations, respectively. Our findings revealed shorter response times for large-sized stimuli than for small-sized stimuli only for greater angular disparities, suggesting that the more unfamiliar the orientations of the bodily stimuli, the more stimuli size affected mental processing. Importantly, when comparing size transformation times, results revealed different patterns of size transformation times as a function of angular disparity between object-based and egocentric transformations. This indicates that mental size transformation and mental rotation proceed differently depending on the mental rotation strategy used. These findings are discussed with respect to the different spatial manipulations involved during object-based and egocentric transformations.

Can histologic transformation of follicular lymphoma be predicted and prevented?

PubMed

Kridel, Robert; Sehn, Laurie H; Gascoyne, Randy D

2017-07-20

Transformation to aggressive lymphoma is a critical event in the clinical course of follicular lymphoma (FL) patients. Yet, it is a challenge to reliably predict transformation at the time of diagnosis. Understanding the risk of transformation would be useful for guiding and monitoring patients, as well as for evaluating novel treatment strategies that could potentially prevent transformation. Herein, we review the contribution of clinical, pathological, and genetic risk factors to transformation. Patients with multiple clinical high-risk factors are at elevated risk of transformation but we are currently lacking a prognostic index that would specifically address transformation rather than disease progression or overall survival. From the biological standpoint, multiple studies have correlated individual biomarkers with transformation. However, accurate prediction of this event is currently hampered by our limited knowledge of the evolutionary pathways leading to transformation, as well as the scarcity of comprehensive, large-scale studies that assess both the genomic landscape of alterations within tumor cells and the composition of the microenvironment. Liquid biopsies hold great promise for achieving precision medicine. Indeed, mutations detected within circulating tumor DNA may be a better reflection of the inherent intratumoral heterogeneity than the biopsy of a single site. Last, we will assess whether evidence exists in the literature that transformation might be prevented altogether, based on the choice of therapy for FL. © 2017 by The American Society of Hematology.

The Gains from Vertical Scaling

ERIC Educational Resources Information Center

Briggs, Derek C.; Domingue, Ben

2013-01-01

It is often assumed that a vertical scale is necessary when value-added models depend upon the gain scores of students across two or more points in time. This article examines the conditions under which the scale transformations associated with the vertical scaling process would be expected to have a significant impact on normative interpretations…

Quantum watermarking scheme through Arnold scrambling and LSB steganography

NASA Astrophysics Data System (ADS)

Zhou, Ri-Gui; Hu, Wenwen; Fan, Ping

2017-09-01

Based on the NEQR of quantum images, a new quantum gray-scale image watermarking scheme is proposed through Arnold scrambling and least significant bit (LSB) steganography. The sizes of the carrier image and the watermark image are assumed to be 2n× 2n and n× n, respectively. Firstly, a classical n× n sized watermark image with 8-bit gray scale is expanded to a 2n× 2n sized image with 2-bit gray scale. Secondly, through the module of PA-MOD N, the expanded watermark image is scrambled to a meaningless image by the Arnold transform. Then, the expanded scrambled image is embedded into the carrier image by the steganography method of LSB. Finally, the time complexity analysis is given. The simulation experiment results show that our quantum circuit has lower time complexity, and the proposed watermarking scheme is superior to others.

Shock-induced thermal wave propagation and response analysis of a viscoelastic thin plate under transient heating loads

NASA Astrophysics Data System (ADS)

Li, Chenlin; Guo, Huili; Tian, Xiaogeng

2018-04-01

This paper is devoted to the thermal shock analysis for viscoelastic materials under transient heating loads. The governing coupled equations with time-delay parameter and nonlocal scale parameter are derived based on the generalized thermo-viscoelasticity theory. The problem of a thin plate composed of viscoelastic material, subjected to a sudden temperature rise at the boundary plane, is solved by employing Laplace transformation techniques. The transient responses, i.e. temperature, displacement, stresses, heat flux as well as strain, are obtained and discussed. The effects of time-delay and nonlocal scale parameter on the transient responses are analyzed and discussed. It can be observed that: the propagation of thermal wave is dynamically smoothed and changed with the variation of time-delay; while the displacement, strain, and stress can be rapidly reduced by nonlocal scale parameter, which can be viewed as an important indicator for predicting the stiffness softening behavior for viscoelastic materials.

Accurate lumen diameter measurement in curved vessels in carotid ultrasound: an iterative scale-space and spatial transformation approach.

PubMed

Krishna Kumar, P; Araki, Tadashi; Rajan, Jeny; Saba, Luca; Lavra, Francesco; Ikeda, Nobutaka; Sharma, Aditya M; Shafique, Shoaib; Nicolaides, Andrew; Laird, John R; Gupta, Ajay; Suri, Jasjit S

2017-08-01

Monitoring of cerebrovascular diseases via carotid ultrasound has started to become a routine. The measurement of image-based lumen diameter (LD) or inter-adventitial diameter (IAD) is a promising approach for quantification of the degree of stenosis. The manual measurements of LD/IAD are not reliable, subjective and slow. The curvature associated with the vessels along with non-uniformity in the plaque growth poses further challenges. This study uses a novel and generalized approach for automated LD and IAD measurement based on a combination of spatial transformation and scale-space. In this iterative procedure, the scale-space is first used to get the lumen axis which is then used with spatial image transformation paradigm to get a transformed image. The scale-space is then reapplied to retrieve the lumen region and boundary in the transformed framework. Then, inverse transformation is applied to display the results in original image framework. Two hundred and two patients' left and right common carotid artery (404 carotid images) B-mode ultrasound images were retrospectively analyzed. The validation of our algorithm has done against the two manual expert tracings. The coefficient of correlation between the two manual tracings for LD was 0.98 (p < 0.0001) and 0.99 (p < 0.0001), respectively. The precision of merit between the manual expert tracings and the automated system was 97.7 and 98.7%, respectively. The experimental analysis demonstrated superior performance of the proposed method over conventional approaches. Several statistical tests demonstrated the stability and reliability of the automated system.

Leadership and transformational change in healthcare organisations: a qualitative analysis of the North East Transformation System.

PubMed

Erskine, Jonathan; Hunter, David J; Small, Adrian; Hicks, Chris; McGovern, Tom; Lugsden, Ed; Whitty, Paula; Steen, Nick; Eccles, Martin Paul

2013-02-01

The research project 'An Evaluation of Transformational Change in NHS North East' examines the progress and success of National Health Service (NHS) organisations in north east England in implementing and embedding the North East Transformation System (NETS), a region-wide programme to improve healthcare quality and safety, and to reduce waste, using a combination of Vision, Compact, and Lean-based Method. This paper concentrates on findings concerning the role of leadership in enabling tranformational change, based on semi-structured interviews with a mix of senior NHS managers and quality improvement staff in 14 study sites. Most interviewees felt that implementing the NETS requires committed, stable leadership, attention to team-building across disciplines and leadership development at many levels. We conclude that without senior leader commitment to continuous improvement over a long time scale and serious efforts to distribute leadership tasks to all levels, healthcare organisations are less likely to achieve positive changes in managerial-clinical relations, sustainable improvements to organisational culture and, ultimately, the region-wide step change in quality, safety and efficiency that the NETS was designed to deliver. © The Author(s) 2013 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Transfers and transformations of zinc in flow-through wetland microcosms.

PubMed

Gillespie, W B; Hawkins, W B; Rodgers, J H; Cano, M L; Dorn, P B

1999-06-01

Two microcosm-scale wetlands (570-liter containers) were integratively designed and constructed to investigate transfers and transformations of zinc associated with an aqueous matrix, and to provide future design parameters for pilot-scale constructed wetlands. The fundamental design of these wetland microcosms was based on biogeochemical principles regulating fate and transformations of zinc (pH, redox, etc.). Each wetland consisted of a 45-cm hydrosoil depth inundated with 25 cm of water, and planted with Scirpus californicus. Zinc ( approximately 2 mg/liter) as ZnCl2 was amended to each wetland for 62 days. Individual wetland hydraulic retention times (HRT) were approximately 24 h. Total recoverable zinc was measured daily in microcosm inflow and outflows, and zinc concentrations in hydrosoil and S. californicus tissue were measured pre- and post-treatment. Ceriodaphnia dubia and Pimephales promelas7-day aqueous toxicity tests were performed on wetland inflows and outflows, and Hyalella azteca whole sediment toxicity tests (10-day) were performed pre- and post-treatment. Approximately 75% of total recoverable zinc was transferred from the water column. Toxicity decreased from inflow to outflow based on 7-day C. dubia tests, and survival of H. azteca in hydrosoil was >80%. Data illustrate the ability of integratively designed wetlands to transfer and sequester zinc from the water column while concomitantly decreasing associated toxicity. Copyright 1999 Academic Press.

PCTDSE: A parallel Cartesian-grid-based TDSE solver for modeling laser-atom interactions

NASA Astrophysics Data System (ADS)

Fu, Yongsheng; Zeng, Jiaolong; Yuan, Jianmin

2017-01-01

We present a parallel Cartesian-grid-based time-dependent Schrödinger equation (TDSE) solver for modeling laser-atom interactions. It can simulate the single-electron dynamics of atoms in arbitrary time-dependent vector potentials. We use a split-operator method combined with fast Fourier transforms (FFT), on a three-dimensional (3D) Cartesian grid. Parallelization is realized using a 2D decomposition strategy based on the Message Passing Interface (MPI) library, which results in a good parallel scaling on modern supercomputers. We give simple applications for the hydrogen atom using the benchmark problems coming from the references and obtain repeatable results. The extensions to other laser-atom systems are straightforward with minimal modifications of the source code.

Ship detection using STFT sea background statistical modeling for large-scale oceansat remote sensing image

NASA Astrophysics Data System (ADS)

Wang, Lixia; Pei, Jihong; Xie, Weixin; Liu, Jinyuan

2018-03-01

Large-scale oceansat remote sensing images cover a big area sea surface, which fluctuation can be considered as a non-stationary process. Short-Time Fourier Transform (STFT) is a suitable analysis tool for the time varying nonstationary signal. In this paper, a novel ship detection method using 2-D STFT sea background statistical modeling for large-scale oceansat remote sensing images is proposed. First, the paper divides the large-scale oceansat remote sensing image into small sub-blocks, and 2-D STFT is applied to each sub-block individually. Second, the 2-D STFT spectrum of sub-blocks is studied and the obvious different characteristic between sea background and non-sea background is found. Finally, the statistical model for all valid frequency points in the STFT spectrum of sea background is given, and the ship detection method based on the 2-D STFT spectrum modeling is proposed. The experimental result shows that the proposed algorithm can detect ship targets with high recall rate and low missing rate.

Random noise attenuation of non-uniformly sampled 3D seismic data along two spatial coordinates using non-equispaced curvelet transform

NASA Astrophysics Data System (ADS)

Zhang, Hua; Yang, Hui; Li, Hongxing; Huang, Guangnan; Ding, Zheyi

2018-04-01

The attenuation of random noise is important for improving the signal to noise ratio (SNR). However, the precondition for most conventional denoising methods is that the noisy data must be sampled on a uniform grid, making the conventional methods unsuitable for non-uniformly sampled data. In this paper, a denoising method capable of regularizing the noisy data from a non-uniform grid to a specified uniform grid is proposed. Firstly, the denoising method is performed for every time slice extracted from the 3D noisy data along the source and receiver directions, then the 2D non-equispaced fast Fourier transform (NFFT) is introduced in the conventional fast discrete curvelet transform (FDCT). The non-equispaced fast discrete curvelet transform (NFDCT) can be achieved based on the regularized inversion of an operator that links the uniformly sampled curvelet coefficients to the non-uniformly sampled noisy data. The uniform curvelet coefficients can be calculated by using the inversion algorithm of the spectral projected-gradient for ℓ1-norm problems. Then local threshold factors are chosen for the uniform curvelet coefficients for each decomposition scale, and effective curvelet coefficients are obtained respectively for each scale. Finally, the conventional inverse FDCT is applied to the effective curvelet coefficients. This completes the proposed 3D denoising method using the non-equispaced curvelet transform in the source-receiver domain. The examples for synthetic data and real data reveal the effectiveness of the proposed approach in applications to noise attenuation for non-uniformly sampled data compared with the conventional FDCT method and wavelet transformation.

Investigation of scale effects in the TRF determined by VLBI

NASA Astrophysics Data System (ADS)

Wahl, Daniel; Heinkelmann, Robert; Schuh, Harald

2017-04-01

The improvement of the International Terrestrial Reference Frame (ITRF) is of great significance for Earth sciences and one of the major tasks in geodesy. The translation, rotation and the scale-factor, as well as their linear rates, are solved in a 14-parameter transformation between individual frames of each space geodetic technique and the combined frame. In ITRF2008, as well as in the current release ITRF2014, the scale-factor is provided by Very Long Baseline Interferometry (VLBI) and Satellite Laser Ranging (SLR) in equal shares. Since VLBI measures extremely precise group delays that are transformed to baseline lengths by the velocity of light, a natural constant, VLBI is the most suitable method for providing the scale. The aim of the current work is to identify possible shortcomings in the VLBI scale contribution to ITRF2008. For developing recommendations for an enhanced estimation, scale effects in the Terrestrial Reference Frame (TRF) determined with VLBI are considered in detail and compared to ITRF2008. In contrast to station coordinates, where the scale is defined by a geocentric position vector, pointing from the origin of the reference frame to the station, baselines are not related to the origin. They are describing the absolute scale independently from the datum. The more accurate a baseline length, and consequently the scale, is estimated by VLBI, the better the scale contribution to the ITRF. Considering time series of baseline length between different stations, a non-linear periodic signal can clearly be recognized, caused by seasonal effects at observation sites. Modeling these seasonal effects and subtracting them from the original data enhances the repeatability of single baselines significantly. Other effects influencing the scale strongly, are jumps in the time series of baseline length, mainly evoked by major earthquakes. Co- and post-seismic effects can be identified in the data, having a non-linear character likewise. Modeling the non-linear motion or completely excluding affected stations is another important step for an improved scale determination. In addition to the investigation of single baseline repeatabilities also the spatial transformation, which is performed for determining parameters of the ITRF2008, are considered. Since the reliability of the resulting transformation parameters is higher the more identical points are used in the transformation, an approach where all possible stations are used as control points is comprehensible. Experiments that examine the scale-factor and its spatial behavior between control points in ITRF2008 and coordinates determined by VLBI only showed that the network geometry has a large influence on the outcome as well. Introducing an unequally distributed network for the datum configuration, the correlations between translation parameters and the scale-factor can become remarkably high. Only a homogeneous spatial distribution of participating stations yields a maximally uncorrelated scale-factor that can be interpreted independent from other parameters. In the current release of the ITRF, the ITRF2014, for the first time, non-linear effects in the time series of station coordinates are taken into account. The present work shows the importance and the right direction of the modification of the ITRF calculation. But also further improvements were found which lead to an enhanced scale determination.

Enhancing water cycle measurements for future hydrologic research

USGS Publications Warehouse

Loescher, H.W.; Jacobs, J.M.; Wendroth, O.; Robinson, D.A.; Poulos, G.S.; McGuire, K.; Reed, P.; Mohanty, B.P.; Shanley, J.B.; Krajewski, W.

2007-01-01

The Consortium of Universities for the Advancement of Hydrologic Sciences, Inc., established the Hydrologic Measurement Facility to transform watershed-scale hydrologic research by facilitating access to advanced instrumentation and expertise that would not otherwise be available to individual investigators. We outline a committee-based process that determined which suites of instrumentation best fit the needs of the hydrological science community and a proposed mechanism for the governance and distribution of these sensors. Here, we also focus on how these proposed suites of instrumentation can be used to address key scientific challenges, including scaling water cycle science in time and space, broadening the scope of individual subdisciplines of water cycle science, and developing mechanistic linkages among these subdisciplines and spatio-temporal scales. ?? 2007 American Meteorological Society.

An efficient and high-throughput protocol for Agrobacterium-mediated transformation based on phosphomannose isomerase positive selection in Japonica rice (Oryza sativa L.).

PubMed

Duan, Yongbo; Zhai, Chenguang; Li, Hao; Li, Juan; Mei, Wenqian; Gui, Huaping; Ni, Dahu; Song, Fengshun; Li, Li; Zhang, Wanggen; Yang, Jianbo

2012-09-01

A number of Agrobacterium-mediated rice transformation systems have been developed and widely used in numerous laboratories and research institutes. However, those systems generally employ antibiotics like kanamycin and hygromycin, or herbicide as selectable agents, and are used for the small-scale experiments. To address high-throughput production of transgenic rice plants via Agrobacterium-mediated transformation, and to eliminate public concern on antibiotic markers, we developed a comprehensive efficient protocol, covering from explant preparation to the acquisition of low copy events by real-time PCR analysis before transplant to field, for high-throughput production of transgenic plants of Japonica rice varieties Wanjing97 and Nipponbare using Escherichia coli phosphomannose isomerase gene (pmi) as a selectable marker. The transformation frequencies (TF) of Wanjing97 and Nipponbare were achieved as high as 54.8 and 47.5%, respectively, in one round of selection of 7.5 or 12.5 g/L mannose appended with 5 g/L sucrose. High-throughput transformation from inoculation to transplant of low copy events was accomplished within 55-60 days. Moreover, the Taqman assay data from a large number of transformants showed 45.2% in Wanjing97 and 31.5% in Nipponbare as a low copy rate, and the transformants are fertile and follow the Mendelian segregation ratio. This protocol facilitates us to perform genome-wide functional annotation of the open reading frames and utilization of the agronomically important genes in rice under a reduced public concern on selectable markers. We describe a comprehensive protocol for large scale production of transgenic Japonica rice plants using non-antibiotic selectable agent, at simplified, cost- and labor-saving manners.

Automatic small bowel tumor diagnosis by using multi-scale wavelet-based analysis in wireless capsule endoscopy images.

PubMed

Barbosa, Daniel C; Roupar, Dalila B; Ramos, Jaime C; Tavares, Adriano C; Lima, Carlos S

2012-01-11

Wireless capsule endoscopy has been introduced as an innovative, non-invasive diagnostic technique for evaluation of the gastrointestinal tract, reaching places where conventional endoscopy is unable to. However, the output of this technique is an 8 hours video, whose analysis by the expert physician is very time consuming. Thus, a computer assisted diagnosis tool to help the physicians to evaluate CE exams faster and more accurately is an important technical challenge and an excellent economical opportunity. The set of features proposed in this paper to code textural information is based on statistical modeling of second order textural measures extracted from co-occurrence matrices. To cope with both joint and marginal non-Gaussianity of second order textural measures, higher order moments are used. These statistical moments are taken from the two-dimensional color-scale feature space, where two different scales are considered. Second and higher order moments of textural measures are computed from the co-occurrence matrices computed from images synthesized by the inverse wavelet transform of the wavelet transform containing only the selected scales for the three color channels. The dimensionality of the data is reduced by using Principal Component Analysis. The proposed textural features are then used as the input of a classifier based on artificial neural networks. Classification performances of 93.1% specificity and 93.9% sensitivity are achieved on real data. These promising results open the path towards a deeper study regarding the applicability of this algorithm in computer aided diagnosis systems to assist physicians in their clinical practice.

Watermarking on 3D mesh based on spherical wavelet transform.

PubMed

Jin, Jian-Qiu; Dai, Min-Ya; Bao, Hu-Jun; Peng, Qun-Sheng

2004-03-01

In this paper we propose a robust watermarking algorithm for 3D mesh. The algorithm is based on spherical wavelet transform. Our basic idea is to decompose the original mesh into a series of details at different scales by using spherical wavelet transform; the watermark is then embedded into the different levels of details. The embedding process includes: global sphere parameterization, spherical uniform sampling, spherical wavelet forward transform, embedding watermark, spherical wavelet inverse transform, and at last resampling the mesh watermarked to recover the topological connectivity of the original model. Experiments showed that our algorithm can improve the capacity of the watermark and the robustness of watermarking against attacks.

A data-driven wavelet-based approach for generating jumping loads

NASA Astrophysics Data System (ADS)

Chen, Jun; Li, Guo; Racic, Vitomir

2018-06-01

This paper suggests an approach to generate human jumping loads using wavelet transform and a database of individual jumping force records. A total of 970 individual jumping force records of various frequencies were first collected by three experiments from 147 test subjects. For each record, every jumping pulse was extracted and decomposed into seven levels by wavelet transform. All the decomposition coefficients were stored in an information database. Probability distributions of jumping cycle period, contact ratio and energy of the jumping pulse were statistically analyzed. Inspired by the theory of DNA recombination, an approach was developed by interchanging the wavelet coefficients between different jumping pulses. To generate a jumping force time history with N pulses, wavelet coefficients were first selected randomly from the database at each level. They were then used to reconstruct N pulses by the inverse wavelet transform. Jumping cycle periods and contract ratios were then generated randomly based on their probabilistic functions. These parameters were assigned to each of the N pulses which were in turn scaled by the amplitude factors βi to account for energy relationship between successive pulses. The final jumping force time history was obtained by linking all the N cycles end to end. This simulation approach can preserve the non-stationary features of the jumping load force in time-frequency domain. Application indicates that this approach can be used to generate jumping force time history due to single people jumping and also can be extended further to stochastic jumping loads due to groups and crowds.

Numerical Simulation of Monitoring Corrosion in Reinforced Concrete Based on Ultrasonic Guided Waves

PubMed Central

Zheng, Zhupeng; Lei, Ying; Xue, Xin

2014-01-01

Numerical simulation based on finite element method is conducted to predict the location of pitting corrosion in reinforced concrete. Simulation results show that it is feasible to predict corrosion monitoring based on ultrasonic guided wave in reinforced concrete, and wavelet analysis can be used for the extremely weak signal of guided waves due to energy leaking into concrete. The characteristic of time-frequency localization of wavelet transform is adopted in the corrosion monitoring of reinforced concrete. Guided waves can be successfully used to identify corrosion defects in reinforced concrete with the analysis of suitable wavelet-based function and its scale. PMID:25013865

Hilbert-Huang spectral analysis for characterizing the intrinsic time-scales of variability in decennial time-series of surface solar radiation

NASA Astrophysics Data System (ADS)

Bengulescu, Marc; Blanc, Philippe; Wald, Lucien

2016-04-01

An analysis of the variability of the surface solar irradiance (SSI) at different local time-scales is presented in this study. Since geophysical signals, such as long-term measurements of the SSI, are often produced by the non-linear interaction of deterministic physical processes that may also be under the influence of non-stationary external forcings, the Hilbert-Huang transform (HHT), an adaptive, noise-assisted, data-driven technique, is employed to extract locally - in time and in space - the embedded intrinsic scales at which a signal oscillates. The transform consists of two distinct steps. First, by means of the Empirical Mode Decomposition (EMD), the time-series is "de-constructed" into a finite number - often small - of zero-mean components that have distinct temporal scales of variability, termed hereinafter the Intrinsic Mode Functions (IMFs). The signal model of the components is an amplitude modulation - frequency modulation (AM - FM) one, and can also be thought of as an extension of a Fourier series having both time varying amplitude and frequency. Following the decomposition, Hilbert spectral analysis is then employed on the IMFs, yielding a time-frequency-energy representation that portrays changes in the spectral contents of the original data, with respect to time. As measurements of surface solar irradiance may possibly be contaminated by the manifestation of different type of stochastic processes (i.e. noise), the identification of real, physical processes from this background of random fluctuations is of interest. To this end, an adaptive background noise null hypothesis is assumed, based on the robust statistical properties of the EMD when applied to time-series of different classes of noise (e.g. white, red or fractional Gaussian). Since the algorithm acts as an efficient constant-Q dyadic, "wavelet-like", filter bank, the different noise inputs are decomposed into components having the same spectral shape, but that are translated to the next lower octave in the spectral domain. Thus, when the sampling step is increased, the spectral shape of IMFs cannot remain at its original position, due to the new lower Nyquist frequency, and is instead pushed toward the lower scaled frequency. Based on these features, the identification of potential signals within the data should become possible without any prior knowledge of the background noises. When applying the above outlined procedure to decennial time-series of surface solar irradiance, only the component that has an annual time-scale of variability is shown to have statistical properties that diverge from those of noise. Nevertheless, the noise-like components are not completely devoid of information, as it is found that their AM components have a non-null rank correlation coefficient with the annual mode, i.e. the background noise intensity seems to be modulated by the seasonal cycle. The findings have possible implications on the modelling and forecast of the surface solar irradiance, by discriminating its deterministic from its quasi-stochastic constituents, at distinct local time-scales.

Time-distance domain transformation for Acoustic Emission source localization in thin metallic plates.

PubMed

Grabowski, Krzysztof; Gawronski, Mateusz; Baran, Ireneusz; Spychalski, Wojciech; Staszewski, Wieslaw J; Uhl, Tadeusz; Kundu, Tribikram; Packo, Pawel

2016-05-01

Acoustic Emission used in Non-Destructive Testing is focused on analysis of elastic waves propagating in mechanical structures. Then any information carried by generated acoustic waves, further recorded by a set of transducers, allow to determine integrity of these structures. It is clear that material properties and geometry strongly impacts the result. In this paper a method for Acoustic Emission source localization in thin plates is presented. The approach is based on the Time-Distance Domain Transform, that is a wavenumber-frequency mapping technique for precise event localization. The major advantage of the technique is dispersion compensation through a phase-shifting of investigated waveforms in order to acquire the most accurate output, allowing for source-sensor distance estimation using a single transducer. The accuracy and robustness of the above process are also investigated. This includes the study of Young's modulus value and numerical parameters influence on damage detection. By merging the Time-Distance Domain Transform with an optimal distance selection technique, an identification-localization algorithm is achieved. The method is investigated analytically, numerically and experimentally. The latter involves both laboratory and large scale industrial tests. Copyright © 2016 Elsevier B.V. All rights reserved.

Light, Molecules, Action: Using Ultrafast Uv-Visible and X-Ray Spectroscopy to Probe Excited State Dynamics in Photoactive Molecules

NASA Astrophysics Data System (ADS)

Sension, R. J.

2017-06-01

Light provides a versatile energy source capable of precise manipulation of material systems on size scales ranging from molecular to macroscopic. Photochemistry provides the means for transforming light energy from photon to process via movement of charge, a change in shape, a change in size, or the cleavage of a bond. Photochemistry produces action. In the work to be presented here ultrafast UV-Visible pump-probe, and pump-repump-probe methods have been used to probe the excited state dynamics of stilbene-based molecular motors, cyclohexadiene-based switches, and polyene-based photoacids. Both ultrafast UV-Visible and X-ray absorption spectroscopies have been applied to the study of cobalamin (vitamin B_{12}) based compounds. Optical measurements provide precise characterization of spectroscopic signatures of the intermediate species on the S_{1} surface, while time-resolved XANES spectra at the Co K-edge probe the structural changes that accompany these transformations.

Mellin Transform-Based Correction Method for Linear Scale Inconsistency of Intrusion Events Identification in OFPS

NASA Astrophysics Data System (ADS)

Wang, Baocheng; Qu, Dandan; Tian, Qing; Pang, Liping

2018-05-01

For the problem that the linear scale of intrusion signals in the optical fiber pre-warning system (OFPS) is inconsistent, this paper presents a method to correct the scale. Firstly, the intrusion signals are intercepted, and an aggregate of the segments with equal length is obtained. Then, the Mellin transform (MT) is applied to convert them into the same scale. The spectral characteristics are obtained by the Fourier transform. Finally, we adopt back-propagation (BP) neural network to identify intrusion types, which takes the spectral characteristics as input. We carried out the field experiments and collected the optical fiber intrusion signals which contain the picking signal, shoveling signal, and running signal. The experimental results show that the proposed algorithm can effectively improve the recognition accuracy of the intrusion signals.

Correction of projective distortion in long-image-sequence mosaics without prior information

NASA Astrophysics Data System (ADS)

Yang, Chenhui; Mao, Hongwei; Abousleman, Glen; Si, Jennie

2010-04-01

Image mosaicking is the process of piecing together multiple video frames or still images from a moving camera to form a wide-area or panoramic view of the scene being imaged. Mosaics have widespread applications in many areas such as security surveillance, remote sensing, geographical exploration, agricultural field surveillance, virtual reality, digital video, and medical image analysis, among others. When mosaicking a large number of still images or video frames, the quality of the resulting mosaic is compromised by projective distortion. That is, during the mosaicking process, the image frames that are transformed and pasted to the mosaic become significantly scaled down and appear out of proportion with respect to the mosaic. As more frames continue to be transformed, important target information in the frames can be lost since the transformed frames become too small, which eventually leads to the inability to continue further. Some projective distortion correction techniques make use of prior information such as GPS information embedded within the image, or camera internal and external parameters. Alternatively, this paper proposes a new algorithm to reduce the projective distortion without using any prior information whatsoever. Based on the analysis of the projective distortion, we approximate the projective matrix that describes the transformation between image frames using an affine model. Using singular value decomposition, we can deduce the affine model scaling factor that is usually very close to 1. By resetting the image scale of the affine model to 1, the transformed image size remains unchanged. Even though the proposed correction introduces some error in the image matching, this error is typically acceptable and more importantly, the final mosaic preserves the original image size after transformation. We demonstrate the effectiveness of this new correction algorithm on two real-world unmanned air vehicle (UAV) sequences. The proposed method is shown to be effective and suitable for real-time implementation.

Structural contribution to the ferroelectric fatigue in lead zirconate titanate ceramics

NASA Astrophysics Data System (ADS)

Hinterstein, M.; Rouquette, J.; Haines, J.; Papet, Ph.; Glaum, J.; Knapp, M.; Eckert, J.; Hoffman, M.

2014-09-01

Many ferroelectric devices are based on doped lead zirconate titanate (PZT) ceramics with compositions near the morphotropic phase boundary (MPB), at which the relevant material's properties approach their maximum. Based on a synchrotron x-ray diffraction study of MPB PZT, bulk fatigue is unambiguously found to arise from a less effective field induced tetragonal-to-monoclinic transformation, at which the degradation of the polarization flipping is detected by a less intense and more diffuse anomaly in the atomic displacement parameter of lead. The time dependence of the ferroelectric response on a structural level down to 250 μs confirms this interpretation in the time scale of the piezolectric strain response.

OBS Data Denoising Based on Compressed Sensing Using Fast Discrete Curvelet Transform

NASA Astrophysics Data System (ADS)

Nan, F.; Xu, Y.

2017-12-01

OBS (Ocean Bottom Seismometer) data denoising is an important step of OBS data processing and inversion. It is necessary to get clearer seismic phases for further velocity structure analysis. Traditional methods for OBS data denoising include band-pass filter, Wiener filter and deconvolution etc. (Liu, 2015). Most of these filtering methods are based on Fourier Transform (FT). Recently, the multi-scale transform methods such as wavelet transform (WT) and Curvelet transform (CvT) are widely used for data denoising in various applications. The FT, WT and CvT could represent signal sparsely and separate noise in transform domain. They could be used in different cases. Compared with Curvelet transform, the FT has Gibbs phenomenon and it cannot handle points discontinuities well. WT is well localized and multi scale, but it has poor orientation selectivity and could not handle curves discontinuities well. CvT is a multiscale directional transform that could represent curves with only a small number of coefficients. It provide an optimal sparse representation of objects with singularities along smooth curves, which is suitable for seismic data processing. As we know, different seismic phases in OBS data are showed as discontinuous curves in time domain. Hence, we promote to analysis the OBS data via CvT and separate the noise in CvT domain. In this paper, our sparsity-promoting inversion approach is restrained by L1 condition and we solve this L1 problem by using modified iteration thresholding. Results show that the proposed method could suppress the noise well and give sparse results in Curvelet domain. Figure 1 compares the Curvelet denoising method with Wavelet method on the same iterations and threshold through synthetic example. a)Original data. b) Add-noise data. c) Denoised data using CvT. d) Denoised data using WT. The CvT can well eliminate the noise and has better result than WT. Further we applied the CvT denoise method for the OBS data processing. Figure 2a is a common receiver gather collected in the Bohai Sea, China. The whole profile is 120km long with 987 shots. The horizontal axis is shot number. The vertical axis is travel time reduced by 6km/s. We use our method to process the data and get a denoised profile figure 2b. After denoising, most of the high frequency noise was suppressed and the seismic phases were clearer.

Scale size-dependent characteristics of the nightside aurora

NASA Astrophysics Data System (ADS)

Humberset, B. K.; Gjerloev, J. W.; Samara, M.; Michell, R. G.

2017-02-01

We have determined the spatiotemporal characteristics of the magnetosphere-ionosphere (M-I) coupling using auroral imaging. Observations at fixed positions for an extended period of time are provided by a ground-based all-sky imager measuring the 557.7 nm auroral emissions. We report on a single event of nightside aurora (˜22 magnetic local time) preceding a substorm onset. To determine the spatiotemporal characteristics, we perform an innovative analysis of an all-sky imager movie (19 min duration, images at 3.31 Hz) that combines a two-dimensional spatial fast Fourier transform with a temporal correlation. We find a scale size-dependent variability where the largest scale sizes are stable on timescales of minutes while the small scale sizes are more variable. When comparing two smaller time intervals of different types of auroral displays, we find a variation in their characteristics. The characteristics averaged over the event are in remarkable agreement with the spatiotemporal characteristics of the nightside field-aligned currents during moderately disturbed times. Thus, two different electrodynamical parameters of the M-I coupling show similar behavior. This gives independent support to the claim of a system behavior that uses repeatable solutions to transfer energy and momentum from the magnetosphere to the ionosphere.

Multi-scale Slip Inversion Based on Simultaneous Spatial and Temporal Domain Wavelet Transform

NASA Astrophysics Data System (ADS)

Liu, W.; Yao, H.; Yang, H. Y.

2017-12-01

Finite fault inversion is a widely used method to study earthquake rupture processes. Some previous studies have proposed different methods to implement finite fault inversion, including time-domain, frequency-domain, and wavelet-domain methods. Many previous studies have found that different frequency bands show different characteristics of the seismic rupture (e.g., Wang and Mori, 2011; Yao et al., 2011, 2013; Uchide et al., 2013; Yin et al., 2017). Generally, lower frequency waveforms correspond to larger-scale rupture characteristics while higher frequency data are representative of smaller-scale ones. Therefore, multi-scale analysis can help us understand the earthquake rupture process thoroughly from larger scale to smaller scale. By the use of wavelet transform, the wavelet-domain methods can analyze both the time and frequency information of signals in different scales. Traditional wavelet-domain methods (e.g., Ji et al., 2002) implement finite fault inversion with both lower and higher frequency signals together to recover larger-scale and smaller-scale characteristics of the rupture process simultaneously. Here we propose an alternative strategy with a two-step procedure, i.e., firstly constraining the larger-scale characteristics with lower frequency signals, and then resolving the smaller-scale ones with higher frequency signals. We have designed some synthetic tests to testify our strategy and compare it with the traditional one. We also have applied our strategy to study the 2015 Gorkha Nepal earthquake using tele-seismic waveforms. Both the traditional method and our two-step strategy only analyze the data in different temporal scales (i.e., different frequency bands), while the spatial distribution of model parameters also shows multi-scale characteristics. A more sophisticated strategy is to transfer the slip model into different spatial scales, and then analyze the smooth slip distribution (larger scales) with lower frequency data firstly and more detailed slip distribution (smaller scales) with higher frequency data subsequently. We are now implementing the slip inversion using both spatial and temporal domain wavelets. This multi-scale analysis can help us better understand frequency-dependent rupture characteristics of large earthquakes.

A Biologically Plausible Transform for Visual Recognition that is Invariant to Translation, Scale, and Rotation.

PubMed

Sountsov, Pavel; Santucci, David M; Lisman, John E

2011-01-01

Visual object recognition occurs easily despite differences in position, size, and rotation of the object, but the neural mechanisms responsible for this invariance are not known. We have found a set of transforms that achieve invariance in a neurally plausible way. We find that a transform based on local spatial frequency analysis of oriented segments and on logarithmic mapping, when applied twice in an iterative fashion, produces an output image that is unique to the object and that remains constant as the input image is shifted, scaled, or rotated.

A Biologically Plausible Transform for Visual Recognition that is Invariant to Translation, Scale, and Rotation

PubMed Central

Sountsov, Pavel; Santucci, David M.; Lisman, John E.

2011-01-01

Visual object recognition occurs easily despite differences in position, size, and rotation of the object, but the neural mechanisms responsible for this invariance are not known. We have found a set of transforms that achieve invariance in a neurally plausible way. We find that a transform based on local spatial frequency analysis of oriented segments and on logarithmic mapping, when applied twice in an iterative fashion, produces an output image that is unique to the object and that remains constant as the input image is shifted, scaled, or rotated. PMID:22125522

Active subspace: toward scalable low-rank learning.

PubMed

Liu, Guangcan; Yan, Shuicheng

2012-12-01

We address the scalability issues in low-rank matrix learning problems. Usually these problems resort to solving nuclear norm regularized optimization problems (NNROPs), which often suffer from high computational complexities if based on existing solvers, especially in large-scale settings. Based on the fact that the optimal solution matrix to an NNROP is often low rank, we revisit the classic mechanism of low-rank matrix factorization, based on which we present an active subspace algorithm for efficiently solving NNROPs by transforming large-scale NNROPs into small-scale problems. The transformation is achieved by factorizing the large solution matrix into the product of a small orthonormal matrix (active subspace) and another small matrix. Although such a transformation generally leads to nonconvex problems, we show that a suboptimal solution can be found by the augmented Lagrange alternating direction method. For the robust PCA (RPCA) (Candès, Li, Ma, & Wright, 2009 ) problem, a typical example of NNROPs, theoretical results verify the suboptimality of the solution produced by our algorithm. For the general NNROPs, we empirically show that our algorithm significantly reduces the computational complexity without loss of optimality.

Size effects on the martensitic phase transformation of NiTi nanograins

NASA Astrophysics Data System (ADS)

Waitz, T.; Antretter, T.; Fischer, F. D.; Simha, N. K.; Karnthaler, H. P.

2007-02-01

The analysis of nanocrystalline NiTi by transmission electron microscopy (TEM) shows that the martensitic transformation proceeds by the formation of atomic-scale twins. Grains of a size less than about 50 nm do not transform to martensite even upon large undercooling. A systematic investigation of these phenomena was carried out elucidating the influence of the grain size on the energy barrier of the transformation. Based on the experiment, nanograins were modeled as spherical inclusions containing (0 0 1) compound twinned martensite. Decomposition of the transformation strains of the inclusions into a shear eigenstrain and a normal eigenstrain facilitates the analytical calculation of shear and normal strain energies in dependence of grain size, twin layer width and elastic properties. Stresses were computed analytically for special cases, otherwise numerically. The shear stresses that alternate from twin layer to twin layer are concentrated at the grain boundaries causing a contribution to the strain energy scaling with the surface area of the inclusion, whereas the strain energy induced by the normal components of the transformation strain and the temperature dependent chemical free energy scale with the volume of the inclusion. In the nanograins these different energy contributions were calculated which allow to predict a critical grain size below which the martensitic transformation becomes unlikely. Finally, the experimental result of the atomic-scale twinning can be explained by analytical calculations that account for the transformation-opposing contributions of the shear strain and the twin boundary energy of the twin-banded morphology of martensitic nanograins.

Retrieving pace in vegetation growth using precipitation and soil moisture

NASA Astrophysics Data System (ADS)

Sohoulande Djebou, D. C.; Singh, V. P.

2013-12-01

The complexity of interactions between the biophysical components of the watershed increases the challenge of understanding water budget. Hence, the perspicacity of the continuum soil-vegetation-atmosphere's functionality still remains crucial for science. This study targeted the Texas Gulf watershed and evaluated the behavior of vegetation covers by coupling precipitation and soil moisture patterns. Growing season's Normalized Differential Vegetation Index NDVI for deciduous forest and grassland were used over a 23 year period as well as precipitation and soil moisture data. The role of time scales on vegetation dynamics analysis was appraised using both entropy rescaling and correlation analysis. This resulted in that soil moisture at 5 cm and 25cm are potentially more efficient to use for vegetation dynamics monitoring at finer time scale compared to precipitation. Albeit soil moisture at 5 cm and 25 cm series are highly correlated (R2>0.64), it appeared that 5 cm soil moisture series can better explain the variability of vegetation growth. A logarithmic transformation of soil moisture and precipitation data increased correlation with NDVI for the different time scales considered. Based on a monthly time scale we came out with a relationship between vegetation index and the couple soil moisture and precipitation [NDVI=a*Log(% soil moisture)+b*Log(Precipitation)+c] with R2>0.25 for each vegetation type. Further, we proposed to assess vegetation green-up using logistic regression model and transinformation entropy using the couple soil moisture and precipitation as independent variables and vegetation growth metrics (NDVI, NDVI ratio, NDVI slope) as the dependent variable. The study is still ongoing and the results will surely contribute to the knowledge in large scale vegetation monitoring. Keywords: Precipitation, soil moisture, vegetation growth, entropy Time scale, Logarithmic transformation and correlation between soil moisture and NDVI, precipitation and NDVI. The analysis is performed by combining both scenes 7 and 8 data. Schematic illustration of the two dimension transinformation entropy approach. T(P,SM;VI) stand for the transinformation contained in the couple soil moisture (SM)/precipitation (P) and explaining vegetation growth (VI).

Higher spin Chern-Simons theory and the super Boussinesq hierarchy

NASA Astrophysics Data System (ADS)

Gutperle, Michael; Li, Yi

2018-05-01

In this paper, we construct a map between a solution of supersymmetric Chern-Simons higher spin gravity based on the superalgebra sl(3|2) with Lifshitz scaling and the N = 2 super Boussinesq hierarchy. We show that under this map the time evolution equations of both theories coincide. In addition, we identify the Poisson structure of the Chern-Simons theory induced by gauge transformation with the second Hamiltonian structure of the super Boussinesq hierarchy.

Invariance in the recurrence of large returns and the validation of models of price dynamics

NASA Astrophysics Data System (ADS)

Chang, Lo-Bin; Geman, Stuart; Hsieh, Fushing; Hwang, Chii-Ruey

2013-08-01

Starting from a robust, nonparametric definition of large returns (“excursions”), we study the statistics of their occurrences, focusing on the recurrence process. The empirical waiting-time distribution between excursions is remarkably invariant to year, stock, and scale (return interval). This invariance is related to self-similarity of the marginal distributions of returns, but the excursion waiting-time distribution is a function of the entire return process and not just its univariate probabilities. Generalized autoregressive conditional heteroskedasticity (GARCH) models, market-time transformations based on volume or trades, and generalized (Lévy) random-walk models all fail to fit the statistical structure of excursions.

Decentralized Adaptive Neural Output-Feedback DSC for Switched Large-Scale Nonlinear Systems.

PubMed

Lijun Long; Jun Zhao

2017-04-01

In this paper, for a class of switched large-scale uncertain nonlinear systems with unknown control coefficients and unmeasurable states, a switched-dynamic-surface-based decentralized adaptive neural output-feedback control approach is developed. The approach proposed extends the classical dynamic surface control (DSC) technique for nonswitched version to switched version by designing switched first-order filters, which overcomes the problem of multiple "explosion of complexity." Also, a dual common coordinates transformation of all subsystems is exploited to avoid individual coordinate transformations for subsystems that are required when applying the backstepping recursive design scheme. Nussbaum-type functions are utilized to handle the unknown control coefficients, and a switched neural network observer is constructed to estimate the unmeasurable states. Combining with the average dwell time method and backstepping and the DSC technique, decentralized adaptive neural controllers of subsystems are explicitly designed. It is proved that the approach provided can guarantee the semiglobal uniformly ultimately boundedness for all the signals in the closed-loop system under a class of switching signals with average dwell time, and the tracking errors to a small neighborhood of the origin. A two inverted pendulums system is provided to demonstrate the effectiveness of the method proposed.

Density-Aware Clustering Based on Aggregated Heat Kernel and Its Transformation

DOE PAGES

Huang, Hao; Yoo, Shinjae; Yu, Dantong; ...

2015-06-01

Current spectral clustering algorithms suffer from the sensitivity to existing noise, and parameter scaling, and may not be aware of different density distributions across clusters. If these problems are left untreated, the consequent clustering results cannot accurately represent true data patterns, in particular, for complex real world datasets with heterogeneous densities. This paper aims to solve these problems by proposing a diffusion-based Aggregated Heat Kernel (AHK) to improve the clustering stability, and a Local Density Affinity Transformation (LDAT) to correct the bias originating from different cluster densities. AHK statistically\\ models the heat diffusion traces along the entire time scale, somore » it ensures robustness during clustering process, while LDAT probabilistically reveals local density of each instance and suppresses the local density bias in the affinity matrix. Our proposed framework integrates these two techniques systematically. As a result, not only does it provide an advanced noise-resisting and density-aware spectral mapping to the original dataset, but also demonstrates the stability during the processing of tuning the scaling parameter (which usually controls the range of neighborhood). Furthermore, our framework works well with the majority of similarity kernels, which ensures its applicability to many types of data and problem domains. The systematic experiments on different applications show that our proposed algorithms outperform state-of-the-art clustering algorithms for the data with heterogeneous density distributions, and achieve robust clustering performance with respect to tuning the scaling parameter and handling various levels and types of noise.« less

Interplay between black carbon and minerals contributes to long term carbon stabilization and mineral transformation

NASA Astrophysics Data System (ADS)

Liang, B.; Weng, Y. T.; Wang, C. C.; Chiang, C. C.; Liu, C. C.; Lehmann, J.

2017-12-01

Black carbon receives increasing global wide research attention due to its role in carbon sequestration, soil fertility enhancement and remediation application. Generally considered chemically stable in bulk, the reactive surface of BC can interplays with minerals and form strong chemical bondage, which renders physical protection of BC and contributes to its long term stabilization. Using historical BC-rich Amazonian Dark Earth (ADE), we probe the in-situ organo-mineral association and transformation of BC and minerals over a millennium scale using various synchrotron-based spectroscopic (XANES, FTIR) and microscopic (TXM) methods. Higher content of SRO minerals was found in BC-rich ADE compare to adjacent tropical soils. The iron signature found in BC-rich ADE was mainly ferrihydrite/lepidocrocite, a more reactive form of Fe compared to goethite, which was dominant in adjacent soil. Abundant nano minerals particles were observed in-situ associated with BC surface, in clusters and layers. The organo-mineral interaction lowers BC bioavailability and enhances its long-term stabilization in environment, while at the same time, transforms associated minerals into more reactive forms under rapid redox/weathering environment. The results suggest that mineral physical protection for BC sequestration may be more important than previous understanding. The scale up application of BC/biochar into agricultural systems and natural environments have long lasting impact on the in-situ transformation of associated minerals.

Unique Fock quantization of scalar cosmological perturbations

NASA Astrophysics Data System (ADS)

Fernández-Méndez, Mikel; Mena Marugán, Guillermo A.; Olmedo, Javier; Velhinho, José M.

2012-05-01

We investigate the ambiguities in the Fock quantization of the scalar perturbations of a Friedmann-Lemaître-Robertson-Walker model with a massive scalar field as matter content. We consider the case of compact spatial sections (thus avoiding infrared divergences), with the topology of a three-sphere. After expanding the perturbations in series of eigenfunctions of the Laplace-Beltrami operator, the Hamiltonian of the system is written up to quadratic order in them. We fix the gauge of the local degrees of freedom in two different ways, reaching in both cases the same qualitative results. A canonical transformation, which includes the scaling of the matter-field perturbations by the scale factor of the geometry, is performed in order to arrive at a convenient formulation of the system. We then study the quantization of these perturbations in the classical background determined by the homogeneous variables. Based on previous work, we introduce a Fock representation for the perturbations in which: (a) the complex structure is invariant under the isometries of the spatial sections and (b) the field dynamics is implemented as a unitary operator. These two properties select not only a unique unitary equivalence class of representations, but also a preferred field description, picking up a canonical pair of field variables among all those that can be obtained by means of a time-dependent scaling of the matter field (completed into a linear canonical transformation). Finally, we present an equivalent quantization constructed in terms of gauge-invariant quantities. We prove that this quantization can be attained by a mode-by-mode time-dependent linear canonical transformation which admits a unitary implementation, so that it is also uniquely determined.

Origin of two time-scale regimes in potentiometric titration of metal oxides. A replica kinetic Monte Carlo study.

PubMed

Zarzycki, Piotr; Rosso, Kevin M

2009-06-16

Replica kinetic Monte Carlo simulations were used to study the characteristic time scales of potentiometric titration of the metal oxides and (oxy)hydroxides. The effect of surface heterogeneity and surface transformation on the titration kinetics were also examined. Two characteristic relaxation times are often observed experimentally, with the trailing slower part attributed to surface nonuniformity, porosity, polymerization, amorphization, and other dynamic surface processes induced by unbalanced surface charge. However, our simulations show that these two characteristic relaxation times are intrinsic to the proton-binding reaction for energetically homogeneous surfaces, and therefore surface heterogeneity or transformation does not necessarily need to be invoked. However, all such second-order surface processes are found to intensify the separation and distinction of the two kinetic regimes. The effect of surface energetic-topographic nonuniformity, as well dynamic surface transformation, interface roughening/smoothing were described in a statistical fashion. Furthermore, our simulations show that a shift in the point-of-zero charge is expected from increased titration speed, and the pH-dependence of the titration measurement error is in excellent agreement with experimental studies.

Novel high-frequency, high-power, pulsed oscillator based on a transmission line transformer.

PubMed

Burdt, R; Curry, R D

2007-07-01

Recent analysis and experiments have demonstrated the potential for transmission line transformers to be employed as compact, high-frequency, high-power, pulsed oscillators with variable rise time, high output impedance, and high operating efficiency. A prototype system was fabricated and tested that generates a damped sinusoidal wave form at a center frequency of 4 MHz into a 200 Omega load, with operating efficiency above 90% and peak power on the order of 10 MW. The initial rise time of the pulse is variable and two experiments were conducted to demonstrate initial rise times of 12 and 3 ns, corresponding to a spectral content from 4-30 and from 4-100 MHz, respectively. A SPICE model has been developed to accurately predict the circuit behavior and scaling laws have been identified to allow for circuit design at higher frequencies and higher peak power. The applications, circuit analysis, test stand, experimental results, circuit modeling, and design of future systems are all discussed.

Thermal Cycling and Isothermal Deformation Response of Polycrystalline NiTi: Simulations vs. Experiment

NASA Technical Reports Server (NTRS)

Manchiraju, Sivom; Gaydosh, Darrell; Benafan, Othmane; Noebe, Ronald; Vaidyanathan, Raj; Anderson, Peter M.

2011-01-01

A recent microstructure-based FEM model that couples crystal-based plasticity, the B2<-> MB190 phase transformation and anisotropic elasticity at the grain scale is calibrated to recent data for polycrystalline NiTi (49.9 at.% Ni). Inputs include anisotropic elastic properties, texture and differential scanning calorimetry data, as well as a subset of recent isothermal deformation and load-biased thermal cycling data. The model is assessed against additional experimental data. Several experimental trends are captured - in particular, the transformation strain during thermal cycling monotonically increases and reaches a peak with increasing bias stress. This is achieved, in part, by modifying the martensite hardening matrix proposed by Patoor et al. [Patoor E, Eberhardt A, Berveiller M. J Phys IV 1996;6:277]. Some experimental trends are underestimated - in particular, the ratcheting of macrostrain during thermal cycling. This may reflect a model limitation that transformation-plasticity coupling is captured on a coarse (grain) scale but not on a fine (martensitic plate) scale.

Real-time atomistic observation of structural phase transformations in individual hafnia nanorods

DOE PAGES

Hudak, Bethany M.; Depner, Sean W.; Waetzig, Gregory R.; ...

2017-05-12

High-temperature phases of hafnium dioxide have exceptionally high dielectric constants and large bandgaps, but quenching them to room temperature remains a challenge. Scaling the bulk form to nanocrystals, while successful in stabilizing the tetragonal phase of isomorphous ZrO 2, has produced nanorods with a twinned version of the room temperature monoclinic phase in HfO 2. Here we use in situ heating in a scanning transmission electron microscope to observe the transformation of an HfO 2 nanorod from monoclinic to tetragonal, with a transformation temperature suppressed by over 1000°C from bulk. When the nanorod is annealed, we observe with atomic-scale resolutionmore » the transformation from twinned-monoclinic to tetragonal, starting at a twin boundary and propagating via coherent transformation dislocation; the nanorod is reduced to hafnium on cooling. Unlike the bulk displacive transition, nanoscale size-confinement enables us to manipulate the transformation mechanism, and we observe discrete nucleation events and sigmoidal nucleation and growth kinetics.« less

Transient Structures and Possible Limits of Data Recording in Phase-Change Materials.

PubMed

Hu, Jianbo; Vanacore, Giovanni M; Yang, Zhe; Miao, Xiangshui; Zewail, Ahmed H

2015-07-28

Phase-change materials (PCMs) represent the leading candidates for universal data storage devices, which exploit the large difference in the physical properties of their transitional lattice structures. On a nanoscale, it is fundamental to determine their performance, which is ultimately controlled by the speed limit of transformation among the different structures involved. Here, we report observation with atomic-scale resolution of transient structures of nanofilms of crystalline germanium telluride, a prototypical PCM, using ultrafast electron crystallography. A nonthermal transformation from the initial rhombohedral phase to the cubic structure was found to occur in 12 ps. On a much longer time scale, hundreds of picoseconds, equilibrium heating of the nanofilm is reached, driving the system toward amorphization, provided that high excitation energy is invoked. These results elucidate the elementary steps defining the structural pathway in the transformation of crystalline-to-amorphous phase transitions and describe the essential atomic motions involved when driven by an ultrafast excitation. The establishment of the time scales of the different transient structures, as reported here, permits determination of the possible limit of performance, which is crucial for high-speed recording applications of PCMs.

Computer implemented empirical mode decomposition method, apparatus, and article of manufacture for two-dimensional signals

NASA Technical Reports Server (NTRS)

Huang, Norden E. (Inventor)

2001-01-01

A computer implemented method of processing two-dimensional physical signals includes five basic components and the associated presentation techniques of the results. The first component decomposes the two-dimensional signal into one-dimensional profiles. The second component is a computer implemented Empirical Mode Decomposition that extracts a collection of Intrinsic Mode Functions (IMF's) from each profile based on local extrema and/or curvature extrema. The decomposition is based on the direct extraction of the energy associated with various intrinsic time scales in the profiles. In the third component, the IMF's of each profile are then subjected to a Hilbert Transform. The fourth component collates the Hilbert transformed IMF's of the profiles to form a two-dimensional Hilbert Spectrum. A fifth component manipulates the IMF's by, for example, filtering the two-dimensional signal by reconstructing the two-dimensional signal from selected IMF(s).

Scattering transform and LSPTSVM based fault diagnosis of rotating machinery

NASA Astrophysics Data System (ADS)

Ma, Shangjun; Cheng, Bo; Shang, Zhaowei; Liu, Geng

2018-05-01

This paper proposes an algorithm for fault diagnosis of rotating machinery to overcome the shortcomings of classical techniques which are noise sensitive in feature extraction and time consuming for training. Based on the scattering transform and the least squares recursive projection twin support vector machine (LSPTSVM), the method has the advantages of high efficiency and insensitivity for noise signal. Using the energy of the scattering coefficients in each sub-band, the features of the vibration signals are obtained. Then, an LSPTSVM classifier is used for fault diagnosis. The new method is compared with other common methods including the proximal support vector machine, the standard support vector machine and multi-scale theory by using fault data for two systems, a motor bearing and a gear box. The results show that the new method proposed in this study is more effective for fault diagnosis of rotating machinery.

Application of affinity propagation algorithm based on manifold distance for transformer PD pattern recognition

NASA Astrophysics Data System (ADS)

Wei, B. G.; Huo, K. X.; Yao, Z. F.; Lou, J.; Li, X. Y.

2018-03-01

It is one of the difficult problems encountered in the research of condition maintenance technology of transformers to recognize partial discharge (PD) pattern. According to the main physical characteristics of PD, three models of oil-paper insulation defects were set up in laboratory to study the PD of transformers, and phase resolved partial discharge (PRPD) was constructed. By using least square method, the grey-scale images of PRPD were constructed and features of each grey-scale image were 28 box dimensions and 28 information dimensions. Affinity propagation algorithm based on manifold distance (AP-MD) for transformers PD pattern recognition was established, and the data of box dimension and information dimension were clustered based on AP-MD. Study shows that clustering result of AP-MD is better than the results of affinity propagation (AP), k-means and fuzzy c-means algorithm (FCM). By choosing different k values of k-nearest neighbor, we find clustering accuracy of AP-MD falls when k value is larger or smaller, and the optimal k value depends on sample size.

Time scale dependent negative emission potential of forests and biomass plantations via wood burial, torrefied biomass, biochar and pyrogas condensate sequestration in soil

NASA Astrophysics Data System (ADS)

Schmidt, Hans-Peter; Kammann, Claudia; Lucht, Wolfgang; Gerten, Dieter; Foidl, Nikolaus

2017-04-01

The efficiency of Negative Emission Technologies (NET) is dependent on (1) the transformation of the biomass carbon into a form that can be sequestered, (2) the mean residence time of the sequestered carbon, (3) the regrowth and thus carbon re-accumulation of the harvested biomass, and (4) the positive or negative priming of soil carbon. These four parameters define the time scale dependent C-balance of various NET-Systems and permit a global economic and environmental evaluation. As far as geologic CO2 storage is considered to be feasible with close to zero losses and if the energy for transport, transformation and disposal is taken from the process bioenergy, conventional BE-CCS has a C sequestration potential of 50 - 70 % depending on the type of biomass and the technology used. Beside unknown risks of deep stored CO2 and high costs, regrowth of C-accumulating biomass is hampered in the long-term as not only carbon but also essential soil nutrients are mined. Under this scenario, biomass regrowth is expected to slow down and soil carbon content to decrease. These factors enlarge the time horizon until a BE-CCS system becomes carbon neutral and eventual carbon negative (when biomass regrowth exceeds the difference between the harvested biomass carbon and BE-CCS stored carbon). Thermal treatment of biomass under a low oxygen regime (torrefaction, pyrolysis, gasification) can transform up to 85% of biomass carbon into various solid and liquid forms of recalcitrant carbon that can be sequestered. Depending on the process parameters and temperature, the mean residence time of the torrefied or pyrolysed biomass can last from several decennials to centennials when applied to the soil of the biomass production site. The carbon can thus be stored at comparatively low costs within the ecosystem itself. As the thermal treatment preserves most of the biomass-accumulated nutrients (except N), natural nutrient cycles are maintained within the biomass system. Depending on the quality of the charred biomass (biochar), post thermal treatment and plant nutrient enhancement, regrowth is expected to accelerate and soil carbon content to increase. Overall, the time until such a biochar based CSS systems generates negative carbon emissions (biomass regrowth exceeds the C-loss from CSS transformation) can thus be reduced compared to BE-CCS while increasing the sustainability of the global biomass production system and fostering ecosystem services. In our presentation we will provide first assessments of various biochar-based CCS systems and compare them to conventional BE-CCS, an evaluation of their global time scale dependent C-sequestration potential and their economic frame. E.g. (1) a biochar system with pyrolysis temperatures of 750°C and without liquefying the pyrolysis gases delivers a very recalcitrant biochar but the C-efficiency is low (40%) and fostering of regrowth is only about 10-15%. A (2) biochar system with trunk burial, pyrolysis of needles, bark, twigs, and branches with organic N-enhancement, and pyrolysis gas condensation and chemical oxidation could achieve a C-efficiency of 85% to 90% and foster regrowth over a time scale of 60% by up to 50%. Future challenges of biochar classification, certification, ecotoxicology, C-leaching, carbon credits and integration into agro-forestry practices will be discussed.

Using reactive transport codes to provide mechanistic biogeochemistry representations in global land surface models: CLM-PFLOTRAN 1.0

DOE PAGES

Tang, G.; Yuan, F.; Bisht, G.; ...

2015-12-17

We explore coupling to a configurable subsurface reactive transport code as a flexible and extensible approach to biogeochemistry in land surface models; our goal is to facilitate testing of alternative models and incorporation of new understanding. A reaction network with the CLM-CN decomposition, nitrification, denitrification, and plant uptake is used as an example. We implement the reactions in the open-source PFLOTRAN code, coupled with the Community Land Model (CLM), and test at Arctic, temperate, and tropical sites. To make the reaction network designed for use in explicit time stepping in CLM compatible with the implicit time stepping used in PFLOTRAN,more » the Monod substrate rate-limiting function with a residual concentration is used to represent the limitation of nitrogen availability on plant uptake and immobilization. To achieve accurate, efficient, and robust numerical solutions, care needs to be taken to use scaling, clipping, or log transformation to avoid negative concentrations during the Newton iterations. With a tight relative update tolerance to avoid false convergence, an accurate solution can be achieved with about 50 % more computing time than CLM in point mode site simulations using either the scaling or clipping methods. The log transformation method takes 60–100 % more computing time than CLM. The computing time increases slightly for clipping and scaling; it increases substantially for log transformation for half saturation decrease from 10 −3 to 10 −9 mol m −3, which normally results in decreasing nitrogen concentrations. The frequent occurrence of very low concentrations (e.g. below nanomolar) can increase the computing time for clipping or scaling by about 20 %; computing time can be doubled for log transformation. Caution needs to be taken in choosing the appropriate scaling factor because a small value caused by a negative update to a small concentration may diminish the update and result in false convergence even with very tight relative update tolerance. As some biogeochemical processes (e.g., methane and nitrous oxide production and consumption) involve very low half saturation and threshold concentrations, this work provides insights for addressing nonphysical negativity issues and facilitates the representation of a mechanistic biogeochemical description in earth system models to reduce climate prediction uncertainty.« less

[A wavelet-transform-based method for the automatic detection of late-type stars].

PubMed

Liu, Zhong-tian; Zhao, Rrui-zhen; Zhao, Yong-heng; Wu, Fu-chao

2005-07-01

The LAMOST project, the world largest sky survey project, urgently needs an automatic late-type stars detection system. However, to our knowledge, no effective methods for automatic late-type stars detection have been reported in the literature up to now. The present study work is intended to explore possible ways to deal with this issue. Here, by "late-type stars" we mean those stars with strong molecule absorption bands, including oxygen-rich M, L and T type stars and carbon-rich C stars. Based on experimental results, the authors find that after a wavelet transform with 5 scales on the late-type stars spectra, their frequency spectrum of the transformed coefficient on the 5th scale consistently manifests a unimodal distribution, and the energy of frequency spectrum is largely concentrated on a small neighborhood centered around the unique peak. However, for the spectra of other celestial bodies, the corresponding frequency spectrum is of multimodal and the energy of frequency spectrum is dispersible. Based on such a finding, the authors presented a wavelet-transform-based automatic late-type stars detection method. The proposed method is shown by extensive experiments to be practical and of good robustness.

Time and metamorphic petrology: Calcite to aragonite experiments

USGS Publications Warehouse

Hacker, B.R.; Kirby, S.H.; Bohlen, S.R.

1992-01-01

Although the equilibrium phase relations of many mineral systems are generally well established, the rates of transformations, particularly in polycrystalline rocks, are not. The results of experiments on the calcite to aragonite transformation in polycrystalline marble are different from those for earlier experiments on powdered and single-crystal calcite. The transformation in the polycrystalline samples occurs by different mechanisms, with a different temperature dependence, and at a markedly slower rate. This work demonstrates the importance of kinetic studies on fully dense polycrystalline aggregates for understanding mineralogic phase changes in nature. Extrapolation of these results to geological time scales suggests that transformation of calcite to aragonite does not occur in the absence of volatiles at temperatures below 200??C. Kinetic hindrance is likely to extend to higher temperatures in more complex transformations.

Forecasting Hourly Water Demands With Seasonal Autoregressive Models for Real-Time Application

NASA Astrophysics Data System (ADS)

Chen, Jinduan; Boccelli, Dominic L.

2018-02-01

Consumer water demands are not typically measured at temporal or spatial scales adequate to support real-time decision making, and recent approaches for estimating unobserved demands using observed hydraulic measurements are generally not capable of forecasting demands and uncertainty information. While time series modeling has shown promise for representing total system demands, these models have generally not been evaluated at spatial scales appropriate for representative real-time modeling. This study investigates the use of a double-seasonal time series model to capture daily and weekly autocorrelations to both total system demands and regional aggregated demands at a scale that would capture demand variability across a distribution system. Emphasis was placed on the ability to forecast demands and quantify uncertainties with results compared to traditional time series pattern-based demand models as well as nonseasonal and single-seasonal time series models. Additional research included the implementation of an adaptive-parameter estimation scheme to update the time series model when unobserved changes occurred in the system. For two case studies, results showed that (1) for the smaller-scale aggregated water demands, the log-transformed time series model resulted in improved forecasts, (2) the double-seasonal model outperformed other models in terms of forecasting errors, and (3) the adaptive adjustment of parameters during forecasting improved the accuracy of the generated prediction intervals. These results illustrate the capabilities of time series modeling to forecast both water demands and uncertainty estimates at spatial scales commensurate for real-time modeling applications and provide a foundation for developing a real-time integrated demand-hydraulic model.

Construction of a self-cloning system in the unicellular green alga Pseudochoricystis ellipsoidea.

PubMed

Kasai, Yuki; Oshima, Kohei; Ikeda, Fukiko; Abe, Jun; Yoshimitsu, Yuya; Harayama, Shigeaki

2015-01-01

Microalgae have received considerable interest as a source of biofuel production. The unicellular green alga Pseudochoricystis ellipsoidea (non-validated scientific name) strain Obi appears to be suitable for large-scale cultivation in outdoor open ponds for biodiesel production because it accumulates lipids to more than 30 % of dry cell weight under nitrogen-depleted conditions. It also grows rapidly under acidic conditions at which most protozoan grazers of microalgae may not be tolerant. The lipid productivity of this alga could be improved using genetic engineering techniques; however, genetically modified organisms are the subject of regulation by specific laws. Therefore, the aim of this study was to develop a self-cloning-based positive selection system for the breeding of P. ellipsoidea. In this study, uracil auxotrophic mutants were isolated after the mutagenesis of P. ellipsoidea using either ultraviolet light or a transcription activator-like effector nuclease (TALEN) system. The cDNA of the uridine monophosphate synthase gene (PeUMPS) of P. ellipsoidea was cloned downstream of the promoter of either a beta-tubulin gene (PeTUBULIN1) or the gene for the small subunit of ribulose 1,5-bisphosphate carboxylase/oxygenase (PeRBCS) to construct the pUT1 or pUT2 plasmid, respectively. These constructs were introduced into uracil auxotroph strains, and genetically complementary transformants were isolated successfully on minimal agar plates. Use of Noble agar as the solidifying agent was essential to avoid the development of false-positive colonies. It took more than 6 weeks for the formation of colonies of pUT1 transformants, whereas pUT2 transformants formed colonies in 2 weeks. Real-time PCR revealed that there were more PeUMPS transcripts in pUT2 transformants than in pUT1 transformants. Uracil synthesis (Ura(+)) transformants were also obtained using a gene cassette consisting solely of PeUMPS flanked by the PeRBCS promoter and terminator. A self-cloning-based positive selection system for the genetic transformation of P. ellipsoidea was developed. Self-cloned P. ellipsoidea strains will require less-stringent containment measures for large-scale outdoor cultivation.

Finger-Vein Verification Based on Multi-Features Fusion

PubMed Central

Qin, Huafeng; Qin, Lan; Xue, Lian; He, Xiping; Yu, Chengbo; Liang, Xinyuan

2013-01-01

This paper presents a new scheme to improve the performance of finger-vein identification systems. Firstly, a vein pattern extraction method to extract the finger-vein shape and orientation features is proposed. Secondly, to accommodate the potential local and global variations at the same time, a region-based matching scheme is investigated by employing the Scale Invariant Feature Transform (SIFT) matching method. Finally, the finger-vein shape, orientation and SIFT features are combined to further enhance the performance. The experimental results on databases of 426 and 170 fingers demonstrate the consistent superiority of the proposed approach. PMID:24196433

4D visualization of embryonic, structural crystallization by single-pulse microscopy

PubMed Central

Kwon, Oh-Hoon; Barwick, Brett; Park, Hyun Soon; Baskin, J. Spencer; Zewail, Ahmed H.

2008-01-01

In many physical and biological systems the transition from an amorphous to ordered native structure involves complex energy landscapes, and understanding such transformations requires not only their thermodynamics but also the structural dynamics during the process. Here, we extend our 4D visualization method with electron imaging to include the study of irreversible processes with a single pulse in the same ultrafast electron microscope (UEM) as used before in the single-electron mode for the study of reversible processes. With this augmentation, we report on the transformation of amorphous to crystalline structure with silicon as an example. A single heating pulse was used to initiate crystallization from the amorphous phase while a single packet of electrons imaged selectively in space the transformation as the structure continuously changes with time. From the evolution of crystallinity in real time and the changes in morphology, for nanosecond and femtosecond pulse heating, we describe two types of processes, one that occurs at early time and involves a nondiffusive motion and another that takes place on a longer time scale. Similar mechanisms of two distinct time scales may perhaps be important in biomolecular folding. PMID:18562291

Simulation of coherent nonlinear neutrino flavor transformation in the supernova environment: Correlated neutrino trajectories

NASA Astrophysics Data System (ADS)

Duan, Huaiyu; Fuller, George M.; Carlson, J.; Qian, Yong-Zhong

2006-11-01

We present results of large-scale numerical simulations of the evolution of neutrino and antineutrino flavors in the region above the late-time post-supernova-explosion proto-neutron star. Our calculations are the first to allow explicit flavor evolution histories on different neutrino trajectories and to self-consistently couple flavor development on these trajectories through forward scattering-induced quantum coupling. Employing the atmospheric-scale neutrino mass-squared difference (|δm2|≃3×10-3eV2) and values of θ13 allowed by current bounds, we find transformation of neutrino and antineutrino flavors over broad ranges of energy and luminosity in roughly the “bi-polar” collective mode. We find that this large-scale flavor conversion, largely driven by the flavor off-diagonal neutrino-neutrino forward scattering potential, sets in much closer to the proto-neutron star than simple estimates based on flavor-diagonal potentials and Mikheyev-Smirnov-Wolfenstein evolution would indicate. In turn, this suggests that models of r-process nucleosynthesis sited in the neutrino-driven wind could be affected substantially by active-active neutrino flavor mixing, even with the small measured neutrino mass-squared differences.

Transformative Change Initiative

ERIC Educational Resources Information Center

Bragg, D. D.; Kirby, C.; Witt, M. A.; Richie, D.; Mix, S.; Feldbaum, M.; Liu, S.; Mason, M.

2014-01-01

The Transformative Change Initiative (TCI) is dedicated to assisting community colleges to scale up innovation in the form of guided pathways, programs of study, and evidence-based strategies to improve student outcomes and program, organization, and system performance. The impetus for TCI is the Trade Adjustment Assistance Community College and…

Sharply curved turn around duct flow predictions using spectral partitioning of the turbulent kinetic energy and a pressure modified wall law

NASA Technical Reports Server (NTRS)

Santi, L. Michael

1986-01-01

Computational predictions of turbulent flow in sharply curved 180 degree turn around ducts are presented. The CNS2D computer code is used to solve the equations of motion for two-dimensional incompressible flows transformed to a nonorthogonal body-fitted coordinate system. This procedure incorporates the pressure velocity correction algorithm SIMPLE-C to iteratively solve a discretized form of the transformed equations. A multiple scale turbulence model based on simplified spectral partitioning is employed to obtain closure. Flow field predictions utilizing the multiple scale model are compared to features predicted by the traditional single scale k-epsilon model. Tuning parameter sensitivities of the multiple scale model applied to turn around duct flows are also determined. In addition, a wall function approach based on a wall law suitable for incompressible turbulent boundary layers under strong adverse pressure gradients is tested. Turn around duct flow characteristics utilizing this modified wall law are presented and compared to results based on a standard wall treatment.

Dynamics analysis of the fast-slow hydro-turbine governing system with different time-scale coupling

NASA Astrophysics Data System (ADS)

Zhang, Hao; Chen, Diyi; Wu, Changzhi; Wang, Xiangyu

2018-01-01

Multi-time scales modeling of hydro-turbine governing system is crucial in precise modeling of hydropower plant and provides support for the stability analysis of the system. Considering the inertia and response time of the hydraulic servo system, the hydro-turbine governing system is transformed into the fast-slow hydro-turbine governing system. The effects of the time-scale on the dynamical behavior of the system are analyzed and the fast-slow dynamical behaviors of the system are investigated with different time-scale. Furthermore, the theoretical analysis of the stable regions is presented. The influences of the time-scale on the stable region are analyzed by simulation. The simulation results prove the correctness of the theoretical analysis. More importantly, the methods and results of this paper provide a perspective to multi-time scales modeling of hydro-turbine governing system and contribute to the optimization analysis and control of the system.

Video Bandwidth Compression System.

DTIC Science & Technology

1980-08-01

scaling function, located between the inverse DPCM and inverse transform , on the decoder matrix multiplier chips. 1"V1 T.. ---- i.13 SECURITY...Bit Unpacker and Inverse DPCM Slave Sync Board 15 e. Inverse DPCM Loop Boards 15 f. Inverse Transform Board 16 g. Composite Video Output Board 16...36 a. Display Refresh Memory 36 (1) Memory Section 37 (2) Timing and Control 39 b. Bit Unpacker and Inverse DPCM 40 c. Inverse Transform Processor 43

Experimental evidence of stress-field-induced selection of variants in Ni-Mn-Ga ferromagnetic shape-memory alloys

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

Wang, Y. D.; Key Laboratory for Anisotropy and Texture of Materials; Brown, D. W.

2007-05-01

The in situ time-of-flight neutron-diffraction measurements captured well the martensitic transformation behavior of the Ni-Mn-Ga ferromagnetic shape-memory alloys under uniaxial stress fields. We found that a small uniaxial stress applied during phase transformation dramatically disturbed the distribution of variants in the product phase. The observed changes in the distributions of variants may be explained by considering the role of the minimum distortion energy of the Bain transformation in the effective partition among the variants belonging to the same orientation of parent phase. It was also found that transformation kinetics under various stress fields follows the scale law. The present investigationsmore » provide the fundamental approach for scaling the evolution of microstructures in martensitic transitions, which is of general interest to the condensed matter community.« less

Combined self-learning based single-image super-resolution and dual-tree complex wavelet transform denoising for medical images

NASA Astrophysics Data System (ADS)

Yang, Guang; Ye, Xujiong; Slabaugh, Greg; Keegan, Jennifer; Mohiaddin, Raad; Firmin, David

2016-03-01

In this paper, we propose a novel self-learning based single-image super-resolution (SR) method, which is coupled with dual-tree complex wavelet transform (DTCWT) based denoising to better recover high-resolution (HR) medical images. Unlike previous methods, this self-learning based SR approach enables us to reconstruct HR medical images from a single low-resolution (LR) image without extra training on HR image datasets in advance. The relationships between the given image and its scaled down versions are modeled using support vector regression with sparse coding and dictionary learning, without explicitly assuming reoccurrence or self-similarity across image scales. In addition, we perform DTCWT based denoising to initialize the HR images at each scale instead of simple bicubic interpolation. We evaluate our method on a variety of medical images. Both quantitative and qualitative results show that the proposed approach outperforms bicubic interpolation and state-of-the-art single-image SR methods while effectively removing noise.

Impact of compost process conditions on organic micro pollutant degradation during full scale composting.

PubMed

Sadef, Yumna; Poulsen, Tjalfe Gorm; Bester, Kai

2015-06-01

Knowledge about the effects of oxygen concentration, nutrient availability and moisture content on removal of organic micro-pollutants during aerobic composting is at present very limited. Impact of oxygen concentration, readily available nitrogen content (NH4(+), NO3(-)), and moisture content on biological transformation of 15 key organic micro-pollutants during composting, was therefore investigated using bench-scale degradation experiments based on non-sterile compost samples, collected at full-scale composting facilities. In addition, the adequacy of bench-scale composting experiments for representing full-scale composting conditions, was investigated using micro-pollutant concentration measurements from both bench- and full-scale composting experiments. Results showed that lack of oxygen generally prevented transformation of organic micro-pollutants. Increasing readily available nitrogen content from about 50 mg N per 100 g compost to about 140 mg N per 100 g compost actually reduced micro-pollutant transformation, while changes in compost moisture content from 50% to 20% by weight, only had minor influence on micro-pollutant transformation. First-order micro-pollutant degradation rates for 13 organic micro-pollutants were calculated using data from both full- and bench-scale experiments. First-order degradation coefficients for both types of experiments were similar and ranged from 0.02 to 0.03 d(-1) on average, indicating that if a proper sampling strategy is employed, bench-scale experiments can be used to represent full-scale composting conditions. Copyright © 2015 Elsevier Ltd. All rights reserved.

Addressing Spatial Dependence Bias in Climate Model Simulations—An Independent Component Analysis Approach

NASA Astrophysics Data System (ADS)

Nahar, Jannatun; Johnson, Fiona; Sharma, Ashish

2018-02-01

Conventional bias correction is usually applied on a grid-by-grid basis, meaning that the resulting corrections cannot address biases in the spatial distribution of climate variables. To solve this problem, a two-step bias correction method is proposed here to correct time series at multiple locations conjointly. The first step transforms the data to a set of statistically independent univariate time series, using a technique known as independent component analysis (ICA). The mutually independent signals can then be bias corrected as univariate time series and back-transformed to improve the representation of spatial dependence in the data. The spatially corrected data are then bias corrected at the grid scale in the second step. The method has been applied to two CMIP5 General Circulation Model simulations for six different climate regions of Australia for two climate variables—temperature and precipitation. The results demonstrate that the ICA-based technique leads to considerable improvements in temperature simulations with more modest improvements in precipitation. Overall, the method results in current climate simulations that have greater equivalency in space and time with observational data.

Residence-time framework for modeling multicomponent reactive transport in stream hyporheic zones

NASA Astrophysics Data System (ADS)

Painter, S. L.; Coon, E. T.; Brooks, S. C.

2017-12-01

Process-based models for transport and transformation of nutrients and contaminants in streams require tractable representations of solute exchange between the stream channel and biogeochemically active hyporheic zones. Residence-time based formulations provide an alternative to detailed three-dimensional simulations and have had good success in representing hyporheic exchange of non-reacting solutes. We extend the residence-time formulation for hyporheic transport to accommodate general multicomponent reactive transport. To that end, the integro-differential form of previous residence time models is replaced by an equivalent formulation based on a one-dimensional advection dispersion equation along the channel coupled at each channel location to a one-dimensional transport model in Lagrangian travel-time form. With the channel discretized for numerical solution, the associated Lagrangian model becomes a subgrid model representing an ensemble of streamlines that are diverted into the hyporheic zone before returning to the channel. In contrast to the previous integro-differential forms of the residence-time based models, the hyporheic flowpaths have semi-explicit spatial representation (parameterized by travel time), thus allowing coupling to general biogeochemical models. The approach has been implemented as a stream-corridor subgrid model in the open-source integrated surface/subsurface modeling software ATS. We use bedform-driven flow coupled to a biogeochemical model with explicit microbial biomass dynamics as an example to show that the subgrid representation is able to represent redox zonation in sediments and resulting effects on metal biogeochemical dynamics in a tractable manner that can be scaled to reach scales.

Fast parallel approach for 2-D DHT-based real-valued discrete Gabor transform.

PubMed

Tao, Liang; Kwan, Hon Keung

2009-12-01

Two-dimensional fast Gabor transform algorithms are useful for real-time applications due to the high computational complexity of the traditional 2-D complex-valued discrete Gabor transform (CDGT). This paper presents two block time-recursive algorithms for 2-D DHT-based real-valued discrete Gabor transform (RDGT) and its inverse transform and develops a fast parallel approach for the implementation of the two algorithms. The computational complexity of the proposed parallel approach is analyzed and compared with that of the existing 2-D CDGT algorithms. The results indicate that the proposed parallel approach is attractive for real time image processing.

Hydrocarbon Reservoir Prediction Using Bi-Gaussian S Transform Based Time-Frequency Analysis Approach

NASA Astrophysics Data System (ADS)

Cheng, Z.; Chen, Y.; Liu, Y.; Liu, W.; Zhang, G.

2015-12-01

Among those hydrocarbon reservoir detection techniques, the time-frequency analysis based approach is one of the most widely used approaches because of its straightforward indication of low-frequency anomalies from the time-frequency maps, that is to say, the low-frequency bright spots usually indicate the potential hydrocarbon reservoirs. The time-frequency analysis based approach is easy to implement, and more importantly, is usually of high fidelity in reservoir prediction, compared with the state-of-the-art approaches, and thus is of great interest to petroleum geologists, geophysicists, and reservoir engineers. The S transform has been frequently used in obtaining the time-frequency maps because of its better performance in controlling the compromise between the time and frequency resolutions than the alternatives, such as the short-time Fourier transform, Gabor transform, and continuous wavelet transform. The window function used in the majority of previous S transform applications is the symmetric Gaussian window. However, one problem with the symmetric Gaussian window is the degradation of time resolution in the time-frequency map due to the long front taper. In our study, a bi-Gaussian S transform that substitutes the symmetric Gaussian window with an asymmetry bi-Gaussian window is proposed to analyze the multi-channel seismic data in order to predict hydrocarbon reservoirs. The bi-Gaussian window introduces asymmetry in the resultant time-frequency spectrum, with time resolution better in the front direction, as compared with the back direction. It is the first time that the bi-Gaussian S transform is used for analyzing multi-channel post-stack seismic data in order to predict hydrocarbon reservoirs since its invention in 2003. The superiority of the bi-Gaussian S transform over traditional S transform is tested on a real land seismic data example. The performance shows that the enhanced temporal resolution can help us depict more clearly the edge of the hydrocarbon reservoir, especially when the thickness of the reservoir is small (such as the thin beds).

Global output feedback control for a class of high-order feedforward nonlinear systems with input delay.

PubMed

Zha, Wenting; Zhai, Junyong; Fei, Shumin

2013-07-01

This paper investigates the problem of output feedback stabilization for a class of high-order feedforward nonlinear systems with time-varying input delay. First, a scaling gain is introduced into the system under a set of coordinate transformations. Then, the authors construct an observer and controller to make the nominal system globally asymptotically stable. Based on homogeneous domination approach and Lyapunov-Krasovskii functional, it is shown that the closed-loop system can be rendered globally asymptotically stable by the scaling gain. Finally, two simulation examples are provided to illustrate the effectiveness of the proposed scheme. Copyright © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Relaxation Processes and Time Scale Transformation.

DTIC Science & Technology

1982-03-01

the response function may be immediately recognized as being 14 of the Kubo - Green type in the classical regime. Given this general framework, it is now...discussions of the master equation, 2and has recently been applied in cumulative damage models with discrete time parameter .3 However, it does not seem to...development parameter is taken tG be a positive, cumulative function that increases from an origin monotonically. Consider two continuous time scales e and t

The emergence of place-based conservation [Chapter 1

Treesearch

Daniel R. Williams; William P. Stewart; Linda E. Kruger

2013-01-01

Place has emerged as a significant topic within conservation research and practice. The transformative changes connected to contemporary conservation are related to recognition of multi-scaled, social-ecological dynamics; emergent, multiscaled governance structures; and rising importance of place-specific meanings and local knowledge. These transformative changes are...

A New Instantaneous Frequency Measure Based on The Stockwell Transform

NASA Astrophysics Data System (ADS)

yedlin, M. J.; Ben-Horrin, Y.; Fraser, J. D.

2011-12-01

We propose the use of a new transform, the Stockwell transform[1], as a means of creating time-frequency maps and applying them to distinguish blasts from earthquakes. This new transform, the Stockwell transform can be considered as a variant of the continuous wavelet transform, that preserves the absolute phase.The Stockwell transform employs a complex Morlet mother wavelet. The novelty of this transform lies in its resolution properties. High frequencies in the candidate signal are well-resolved in time but poorly resolved in frequency, while the converse is true for low frequency signal components. The goal of this research is to obtain the instantaneous frequency as a function of time for both the earthquakes and the blasts. Two methods will be compared. In the first method, we will compute the analytic signal, the envelope and the instantaneous phase as a function of time[2]. The instantaneous phase derivative will yield the instantaneous angular frequency. The second method will be based on time-frequency analysis using the Stockwell transform. The Stockwell transform will be computed in non-redundant fashion using a dyadic representation[3]. For each time-point, the frequency centroid will be computed -- a representation for the most likely frequency at that time. A detailed comparison will be presented for both approaches to the computation of the instantaneous frequency. An advantage of the Stockwell approach is that no differentiation is applied. The Hilbert transform method can be less sensitive to edge effects. The goal of this research is to see if the new Stockwell-based method could be used as a discriminant between earthquakes and blasts. References [1] Stockwell, R.G., Mansinha, L. and Lowe, R.P. "Localization of the complex spectrum: the S transform", IEEE Trans. Signal Processing, vol.44, no.4, pp.998-1001, (1996). [2]Taner, M.T., Koehler, F. "Complex seismic trace analysis", Geophysics, vol. 44, Issue 6, pp. 1041-1063 (1979). [3] Brown, R.A., Lauzon, M.L. and Frayne, R. "General Description of Linear Time-Frequency Transforms and Formulation of a Fast, Invertible Transform That Samples the Continuous S-Transform Spectrum Nonredundantly", IEEE Transactions on Signal Processing, 1:281-90 (2010).

A novel recursive Fourier transform for nonuniform sampled signals: application to heart rate variability spectrum estimation.

PubMed

Holland, Alexander; Aboy, Mateo

2009-07-01

We present a novel method to iteratively calculate discrete Fourier transforms for discrete time signals with sample time intervals that may be widely nonuniform. The proposed recursive Fourier transform (RFT) does not require interpolation of the samples to uniform time intervals, and each iterative transform update of N frequencies has computational order N. Because of the inherent non-uniformity in the time between successive heart beats, an application particularly well suited for this transform is power spectral density (PSD) estimation for heart rate variability. We compare RFT based spectrum estimation with Lomb-Scargle Transform (LST) based estimation. PSD estimation based on the LST also does not require uniform time samples, but the LST has a computational order greater than Nlog(N). We conducted an assessment study involving the analysis of quasi-stationary signals with various levels of randomly missing heart beats. Our results indicate that the RFT leads to comparable estimation performance to the LST with significantly less computational overhead and complexity for applications requiring iterative spectrum estimations.

Experimental and numerical investigations of the impingement of an oblique liquid jet onto a superhydrophobic surface: energy transformation

NASA Astrophysics Data System (ADS)

Kibar, Ali

2016-02-01

This study presents the theory of impinging an oblique liquid jet onto a vertical superhydrophobic surface based on both experimental and numerical results. A Brassica oleracea leaf with a 160° apparent contact angle was used for the superhydrophobic surface. Distilled water was sent onto the vertical superhydrophobic surface in the range of 1750-3050 Reynolds number, with an inclination angle of 20°-40°, using a circular glass tube with a 1.75 mm inner diameter. The impinging liquid jet spread onto the surface governed by the inertia of the liquid and then reflected off the superhydrophobic surface due to the surface energy of the spreading liquid. Two different energy approaches, which have time-scale and per-unit length, were performed to determine transformation of the energy. The kinetic energy of the impinging liquid jet was transformed into the surface energy with an increasing interfacial surface area between the liquid and air during spreading. Afterwards, this surface energy of the spreading liquid was transformed into the reflection kinetic energy.

Phase correlation of foreign exchange time series

NASA Astrophysics Data System (ADS)

Wu, Ming-Chya

2007-03-01

Correlation of foreign exchange rates in currency markets is investigated based on the empirical data of USD/DEM and USD/JPY exchange rates for a period from February 1 1986 to December 31 1996. The return of exchange time series is first decomposed into a number of intrinsic mode functions (IMFs) by the empirical mode decomposition method. The instantaneous phases of the resultant IMFs calculated by the Hilbert transform are then used to characterize the behaviors of pricing transmissions, and the correlation is probed by measuring the phase differences between two IMFs in the same order. From the distribution of phase differences, our results show explicitly that the correlations are stronger in daily time scale than in longer time scales. The demonstration for the correlations in periods of 1986-1989 and 1990-1993 indicates two exchange rates in the former period were more correlated than in the latter period. The result is consistent with the observations from the cross-correlation calculation.

Precipitation and Phase Transformations in 2101 Lean Duplex Stainless Steel During Isothermal Aging

NASA Astrophysics Data System (ADS)

Maetz, Jean-Yves; Cazottes, Sophie; Verdu, Catherine; Kleber, Xavier

2016-01-01

The effect of isothermal aging at 963 K (690 °C) on the microstructure of a 2101 lean duplex stainless steel, with the composition Fe-21.5Cr-5Mn-1.6Ni-0.22N-0.3Mo, was investigated using a multi-technique and multi-scale approach. The kinetics of phase transformation and precipitation was followed from a few minutes to thousands of hours using thermoelectric power measurements; based on these results, certain aging states were selected for electron microscopy characterization. Scanning electron microscopy, electron back-scattered diffraction, and transmission electron microscopy were used to quantitatively describe the microstructural evolution through crystallographic analysis, chemical analysis, and volume fraction measurements from the macroscopic scale down to the nanometric scale. During aging, the precipitation of M23C6 carbides, Cr2N nitrides, and σ phase as well as the transformation of ferrite into austenite and austenite into martensite was observed. These complex microstructural changes are controlled by Cr volume diffusion. The precipitation and phase transformation mechanisms are described.

A robust computational technique for model order reduction of two-time-scale discrete systems via genetic algorithms.

PubMed

Alsmadi, Othman M K; Abo-Hammour, Zaer S

2015-01-01

A robust computational technique for model order reduction (MOR) of multi-time-scale discrete systems (single input single output (SISO) and multi-input multioutput (MIMO)) is presented in this paper. This work is motivated by the singular perturbation of multi-time-scale systems where some specific dynamics may not have significant influence on the overall system behavior. The new approach is proposed using genetic algorithms (GA) with the advantage of obtaining a reduced order model, maintaining the exact dominant dynamics in the reduced order, and minimizing the steady state error. The reduction process is performed by obtaining an upper triangular transformed matrix of the system state matrix defined in state space representation along with the elements of B, C, and D matrices. The GA computational procedure is based on maximizing the fitness function corresponding to the response deviation between the full and reduced order models. The proposed computational intelligence MOR method is compared to recently published work on MOR techniques where simulation results show the potential and advantages of the new approach.

Methods for performing fast discrete curvelet transforms of data

DOEpatents

Candes, Emmanuel; Donoho, David; Demanet, Laurent

2010-11-23

Fast digital implementations of the second generation curvelet transform for use in data processing are disclosed. One such digital transformation is based on unequally-spaced fast Fourier transforms (USFFT) while another is based on the wrapping of specially selected Fourier samples. Both digital transformations return a table of digital curvelet coefficients indexed by a scale parameter, an orientation parameter, and a spatial location parameter. Both implementations are fast in the sense that they run in about O(n.sup.2 log n) flops for n by n Cartesian arrays or about O(N log N) flops for Cartesian arrays of size N=n.sup.3; in addition, they are also invertible, with rapid inversion algorithms of about the same complexity.

Nanoscale Properties of Rocks and Subduction Zone Rheology: Inferences for the Mechanisms of Deep Earthquakes

NASA Astrophysics Data System (ADS)

Riedel, M. R.

2007-12-01

Grain boundaries are the key for the understanding of mineral reaction kinetics. More generally, nanometer scale processes involved in breaking and establishing bonds at reaction sites determine how and at which rate bulk rock properties change in response to external tectonic forcing and possibly feed back into various geodynamic processes. A particular problem is the effects of grain-boundary energy on the kinetics of the olivine-spinel phase transformation in subducting slabs. Slab rheology is affected in many ways by this (metastable) mineral phase change. Sluggish kinetics due to metastable hindrance is likely to cause particular difficulties, because of possible strong non-linear feedback loops between strain-rate and change of creep properties during transformation. In order to get these nanoscale properties included into thermo-mechanical models, reliable kinetic data is required. The measurement of grain-boundary energies is, however, a rather difficult problem. Conventional methods of grain boundary surface tension measurement include (a) equilibrium angles at triple junction (b) rotating ball method (c) thermal groove method, and others (Gottstein & Shvindlerman, 1999). Here I suggest a new method that allows for the derivation of grain-boundary energies for an isochemical phase transformation based on experimental (in-situ) kinetic data in combination with a corresponding dynamic scaling law (Riedel and Karato, 1997). The application of this method to the olivine-spinel phase transformation in subducting slabs provides a solution to the extrapolation problem of measured kinetic data: Any kinetic phase boundary measured at the laboratory time scale can be "scaled" to the correct critical isotherm at subduction zones, under experimentelly "forbidden" conditions (Liou et al., 2000). Consequences for the metastability hypothesis that relates deep seismicity with olivine metastability are derived and discussed. References: Gottstein G, Shvindlerman LS (1999) Grain Boundary Migration in Metals, CRC Press, 385 pp., New York. Riedel MR, Karato S (1997) Grain-Size Evolution in Subducted Oceanic Lithosphere Associated with the Olivine- Spinel Transformation and Its Effects on Rheology. EPSL 148: 27-43. Liou JG, Hacker BR, Zhang RY (2000) Into the forbidden zone. Science 287, 1215-1216.

Leadership: validation of a self-report scale: comment on Dussault, Frenette, and Fernet (2013).

PubMed

Chakrabarty, Subhra

2014-10-01

In a recent study, Dussault, Frenette, and Fernet (2013) developed a 21-item self-report instrument to measure leadership based on Bass's (1985) transformational/transactional leadership paradigm. The final specification included a third-order dimension (leadership), two second-order dimensions (transactional leadership and transformational leadership), and a first-order dimension (laissez-faire leadership). This note focuses on the need for assessing convergent and discriminant validity of the scale, and on ruling out the potential for common method bias.

A rapid, highly efficient and economical method of Agrobacterium-mediated in planta transient transformation in living onion epidermis.

PubMed

Xu, Kedong; Huang, Xiaohui; Wu, Manman; Wang, Yan; Chang, Yunxia; Liu, Kun; Zhang, Ju; Zhang, Yi; Zhang, Fuli; Yi, Liming; Li, Tingting; Wang, Ruiyue; Tan, Guangxuan; Li, Chengwei

2014-01-01

Transient transformation is simpler, more efficient and economical in analyzing protein subcellular localization than stable transformation. Fluorescent fusion proteins were often used in transient transformation to follow the in vivo behavior of proteins. Onion epidermis, which has large, living and transparent cells in a monolayer, is suitable to visualize fluorescent fusion proteins. The often used transient transformation methods included particle bombardment, protoplast transfection and Agrobacterium-mediated transformation. Particle bombardment in onion epidermis was successfully established, however, it was expensive, biolistic equipment dependent and with low transformation efficiency. We developed a highly efficient in planta transient transformation method in onion epidermis by using a special agroinfiltration method, which could be fulfilled within 5 days from the pretreatment of onion bulb to the best time-point for analyzing gene expression. The transformation conditions were optimized to achieve 43.87% transformation efficiency in living onion epidermis. The developed method has advantages in cost, time-consuming, equipment dependency and transformation efficiency in contrast with those methods of particle bombardment in onion epidermal cells, protoplast transfection and Agrobacterium-mediated transient transformation in leaf epidermal cells of other plants. It will facilitate the analysis of protein subcellular localization on a large scale.

Contaminant attenuation by shallow aquifer systems under steady flow

NASA Astrophysics Data System (ADS)

Soltani, S. S.; Cvetkovic, V.

2017-10-01

We present a framework for analyzing advection-dominated solute transport and transformation in aquifer systems of boreal catchments that are typically shallow and rest on crystalline bedrock. A methodology is presented for estimating tracer discharge based on particle trajectories from recharge to discharge locations and computing their first passage times assuming that the flow pattern is approximately steady-state. Transformation processes can be included by solving one-dimensional reactive transport with randomized water travel time as the independent variable; the distribution of the travel times incorporates morphological dispersion (due to catchment geometry/topography) as well as macro-dispersion (due to heterogeneity of underlying hydraulic properties). The implementation of the framework is illustrated for the well characterized coastal catchment of Forsmark (Sweden). We find that macro-dispersion has a notable effect on attenuation even though the morphological dispersion is significantly larger. Preferential flow on the catchment scale is found to be considerable with only 5% of the Eulerian velocities contributing to transport over the simulation period of 375 years. Natural attenuation is illustrated as a simple (linear decay) transformation process. Simulated natural attenuation can be estimated analytically reasonably well by using basic hydrological and structural information, the latter being the pathway length distribution and average aquifer depth to the bedrock.

Wavelet transformation to determine impedance spectra of lithium-ion rechargeable battery

NASA Astrophysics Data System (ADS)

Hoshi, Yoshinao; Yakabe, Natsuki; Isobe, Koichiro; Saito, Toshiki; Shitanda, Isao; Itagaki, Masayuki

2016-05-01

A new analytical method is proposed to determine the electrochemical impedance of lithium-ion rechargeable batteries (LIRB) from time domain data by wavelet transformation (WT). The WT is a waveform analysis method that can transform data in the time domain to the frequency domain while retaining time information. In this transformation, the frequency domain data are obtained by the convolution integral of a mother wavelet and original time domain data. A complex Morlet mother wavelet (CMMW) is used to obtain the complex number data in the frequency domain. The CMMW is expressed by combining a Gaussian function and sinusoidal term. The theory to select a set of suitable conditions for variables and constants related to the CMMW, i.e., band, scale, and time parameters, is established by determining impedance spectra from wavelet coefficients using input voltage to the equivalent circuit and the output current. The impedance spectrum of LIRB determined by WT agrees well with that measured using a frequency response analyzer.

Kinetic Modeling of Slow Energy Release in Non-Ideal Carbon Rich Explosives

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

Vitello, P; Fried, L; Glaesemann, K

2006-06-20

We present here the first self-consistent kinetic based model for long time-scale energy release in detonation waves in the non-ideal explosive LX-17. Non-ideal, insensitive carbon rich explosives, such as those based on TATB, are believed to have significant late-time slow release in energy. One proposed source of this energy is diffusion-limited growth of carbon clusters. In this paper we consider the late-time energy release problem in detonation waves using the thermochemical code CHEETAH linked to a multidimensional ALE hydrodynamics model. The linked CHEETAH-ALE model dimensional treats slowly reacting chemical species using kinetic rate laws, with chemical equilibrium assumed for speciesmore » coupled via fast time-scale reactions. In the model presented here we include separate rate equations for the transformation of the un-reacted explosive to product gases and for the growth of a small particulate form of condensed graphite to a large particulate form. The small particulate graphite is assumed to be in chemical equilibrium with the gaseous species allowing for coupling between the instantaneous thermodynamic state and the production of graphite clusters. For the explosive burn rate a pressure dependent rate law was used. Low pressure freezing of the gas species mass fractions was also included to account for regions where the kinetic coupling rates become longer than the hydrodynamic time-scales. The model rate parameters were calibrated using cylinder and rate-stick experimental data. Excellent long time agreement and size effect results were achieved.« less

Characterizing the utility of the TMPA real-time product for hydrologic predictions over global river basins across scales

NASA Astrophysics Data System (ADS)

Gao, H.; Zhang, S.; Nijssen, B.; Zhou, T.; Voisin, N.; Sheffield, J.; Lee, K.; Shukla, S.; Lettenmaier, D. P.

2017-12-01

Despite its errors and uncertainties, the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis real-time product (TMPA-RT) has been widely used for hydrological monitoring and forecasting due to its timely availability for real-time applications. To evaluate the utility of TMPA-RT in hydrologic predictions, many studies have compared modeled streamflows driven by TMPA-RT against gauge data. However, because of the limited availability of streamflow observations in data sparse regions, there is still a lack of comprehensive comparisons for TMPA-RT based hydrologic predictions at the global scale. Furthermore, it is expected that its skill is less optimal at the subbasin scale than the basin scale. In this study, we evaluate and characterize the utility of the TMPA-RT product over selected global river basins during the period of 1998 to 2015 using the TMPA research product (TMPA-RP) as a reference. The Variable Infiltration Capacity (VIC) model, which was calibrated and validated previously, is adopted to simulate streamflows driven by TMPA-RT and TMPA-RP, respectively. The objective of this study is to analyze the spatial and temporal characteristics of the hydrologic predictions by answering the following questions: (1) How do the precipitation errors associated with the TMPA-RT product transform into streamflow errors with respect to geographical and climatological characteristics? (2) How do streamflow errors vary across scales within a basin?

Automated Box-Cox Transformations for Improved Visual Encoding.

PubMed

Maciejewski, Ross; Pattath, Avin; Ko, Sungahn; Hafen, Ryan; Cleveland, William S; Ebert, David S

2013-01-01

The concept of preconditioning data (utilizing a power transformation as an initial step) for analysis and visualization is well established within the statistical community and is employed as part of statistical modeling and analysis. Such transformations condition the data to various inherent assumptions of statistical inference procedures, as well as making the data more symmetric and easier to visualize and interpret. In this paper, we explore the use of the Box-Cox family of power transformations to semiautomatically adjust visual parameters. We focus on time-series scaling, axis transformations, and color binning for choropleth maps. We illustrate the usage of this transformation through various examples, and discuss the value and some issues in semiautomatically using these transformations for more effective data visualization.

Decoding the spatial signatures of multi-scale climate variability - a climate network perspective

NASA Astrophysics Data System (ADS)

Donner, R. V.; Jajcay, N.; Wiedermann, M.; Ekhtiari, N.; Palus, M.

2017-12-01

During the last years, the application of complex networks as a versatile tool for analyzing complex spatio-temporal data has gained increasing interest. Establishing this approach as a new paradigm in climatology has already provided valuable insights into key spatio-temporal climate variability patterns across scales, including novel perspectives on the dynamics of the El Nino Southern Oscillation or the emergence of extreme precipitation patterns in monsoonal regions. In this work, we report first attempts to employ network analysis for disentangling multi-scale climate variability. Specifically, we introduce the concept of scale-specific climate networks, which comprises a sequence of networks representing the statistical association structure between variations at distinct time scales. For this purpose, we consider global surface air temperature reanalysis data and subject the corresponding time series at each grid point to a complex-valued continuous wavelet transform. From this time-scale decomposition, we obtain three types of signals per grid point and scale - amplitude, phase and reconstructed signal, the statistical similarity of which is then represented by three complex networks associated with each scale. We provide a detailed analysis of the resulting connectivity patterns reflecting the spatial organization of climate variability at each chosen time-scale. Global network characteristics like transitivity or network entropy are shown to provide a new view on the (global average) relevance of different time scales in climate dynamics. Beyond expected trends originating from the increasing smoothness of fluctuations at longer scales, network-based statistics reveal different degrees of fragmentation of spatial co-variability patterns at different scales and zonal shifts among the key players of climate variability from tropically to extra-tropically dominated patterns when moving from inter-annual to decadal scales and beyond. The obtained results demonstrate the potential usefulness of systematically exploiting scale-specific climate networks, whose general patterns are in line with existing climatological knowledge, but provide vast opportunities for further quantifications at local, regional and global scales that are yet to be explored.

Quasi-coarse-grained dynamics: modelling of metallic materials at mesoscales

NASA Astrophysics Data System (ADS)

Dongare, Avinash M.

2014-12-01

A computationally efficient modelling method called quasi-coarse-grained dynamics (QCGD) is developed to expand the capabilities of molecular dynamics (MD) simulations to model behaviour of metallic materials at the mesoscales. This mesoscale method is based on solving the equations of motion for a chosen set of representative atoms from an atomistic microstructure and using scaling relationships for the atomic-scale interatomic potentials in MD simulations to define the interactions between representative atoms. The scaling relationships retain the atomic-scale degrees of freedom and therefore energetics of the representative atoms as would be predicted in MD simulations. The total energetics of the system is retained by scaling the energetics and the atomic-scale degrees of freedom of these representative atoms to account for the missing atoms in the microstructure. This scaling of the energetics renders improved time steps for the QCGD simulations. The success of the QCGD method is demonstrated by the prediction of the structural energetics, high-temperature thermodynamics, deformation behaviour of interfaces, phase transformation behaviour, plastic deformation behaviour, heat generation during plastic deformation, as well as the wave propagation behaviour, as would be predicted using MD simulations for a reduced number of representative atoms. The reduced number of atoms and the improved time steps enables the modelling of metallic materials at the mesoscale in extreme environments.

Wavelet-based multiscale window transform and energy and vorticity analysis

NASA Astrophysics Data System (ADS)

Liang, Xiang San

A new methodology, Multiscale Energy and Vorticity Analysis (MS-EVA), is developed to investigate sub-mesoscale, meso-scale, and large-scale dynamical interactions in geophysical fluid flows which are intermittent in space and time. The development begins with the construction of a wavelet-based functional analysis tool, the multiscale window transform (MWT), which is local, orthonormal, self-similar, and windowed on scale. The MWT is first built over the real line then modified onto a finite domain. Properties are explored, the most important one being the property of marginalization which brings together a quadratic quantity in physical space with its phase space representation. Based on MWT the MS-EVA is developed. Energy and enstrophy equations for the large-, meso-, and sub-meso-scale windows are derived and their terms interpreted. The processes thus represented are classified into four categories: transport; transfer, conversion, and dissipation/diffusion. The separation of transport from transfer is made possible with the introduction of the concept of perfect transfer. By the property of marginalization, the classical energetic analysis proves to be a particular case of the MS-EVA. The MS-EVA developed is validated with classical instability problems. The validation is carried out through two steps. First, it is established that the barotropic and baroclinic instabilities are indicated by the spatial averages of certain transfer term interaction analyses. Then calculations of these indicators are made with an Eady model and a Kuo model. The results agree precisely with what is expected from their analytical solutions, and the energetics reproduced reveal a consistent and important aspect of the unknown dynamic structures of instability processes. As an application, the MS-EVA is used to investigate the Iceland-Faeroe frontal (IFF) variability. A MS-EVA-ready dataset is first generated, through a forecasting study with the Harvard Ocean Prediction System using the data gathered during the 1993 NRV Alliance cruise. The application starts with a determination of the scale window bounds, which characterize a double-peak structure in either the time wavelet spectrum or the space wavelet spectrum. The resulting energetics, when locally averaged, reveal that there is a clear baroclinic instability happening around the cold tongue intrusion observed in the forecast. Moreover, an interaction analysis shows that the energy released by the instability indeed goes to the meso-scale window and fuel the growth of the intrusion. The sensitivity study shows that, in this case, the key to a successful application is a correct decomposition of the large-scale window from the meso-scale window.

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.

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.

Abdomen disease diagnosis in CT images using flexiscale curvelet transform and improved genetic algorithm.

PubMed

Sethi, Gaurav; Saini, B S

2015-12-01

This paper presents an abdomen disease diagnostic system based on the flexi-scale curvelet transform, which uses different optimal scales for extracting features from computed tomography (CT) images. To optimize the scale of the flexi-scale curvelet transform, we propose an improved genetic algorithm. The conventional genetic algorithm assumes that fit parents will likely produce the healthiest offspring that leads to the least fit parents accumulating at the bottom of the population, reducing the fitness of subsequent populations and delaying the optimal solution search. In our improved genetic algorithm, combining the chromosomes of a low-fitness and a high-fitness individual increases the probability of producing high-fitness offspring. Thereby, all of the least fit parent chromosomes are combined with high fit parent to produce offspring for the next population. In this way, the leftover weak chromosomes cannot damage the fitness of subsequent populations. To further facilitate the search for the optimal solution, our improved genetic algorithm adopts modified elitism. The proposed method was applied to 120 CT abdominal images; 30 images each of normal subjects, cysts, tumors and stones. The features extracted by the flexi-scale curvelet transform were more discriminative than conventional methods, demonstrating the potential of our method as a diagnostic tool for abdomen diseases.

Spherical 3D isotropic wavelets

NASA Astrophysics Data System (ADS)

Lanusse, F.; Rassat, A.; Starck, J.-L.

2012-04-01

Context. Future cosmological surveys will provide 3D large scale structure maps with large sky coverage, for which a 3D spherical Fourier-Bessel (SFB) analysis in spherical coordinates is natural. Wavelets are particularly well-suited to the analysis and denoising of cosmological data, but a spherical 3D isotropic wavelet transform does not currently exist to analyse spherical 3D data. Aims: The aim of this paper is to present a new formalism for a spherical 3D isotropic wavelet, i.e. one based on the SFB decomposition of a 3D field and accompany the formalism with a public code to perform wavelet transforms. Methods: We describe a new 3D isotropic spherical wavelet decomposition based on the undecimated wavelet transform (UWT) described in Starck et al. (2006). We also present a new fast discrete spherical Fourier-Bessel transform (DSFBT) based on both a discrete Bessel transform and the HEALPIX angular pixelisation scheme. We test the 3D wavelet transform and as a toy-application, apply a denoising algorithm in wavelet space to the Virgo large box cosmological simulations and find we can successfully remove noise without much loss to the large scale structure. Results: We have described a new spherical 3D isotropic wavelet transform, ideally suited to analyse and denoise future 3D spherical cosmological surveys, which uses a novel DSFBT. We illustrate its potential use for denoising using a toy model. All the algorithms presented in this paper are available for download as a public code called MRS3D at http://jstarck.free.fr/mrs3d.html

Lie symmetries and conservation laws for the time fractional Derrida-Lebowitz-Speer-Spohn equation

NASA Astrophysics Data System (ADS)

Rui, Wenjuan; Zhang, Xiangzhi

2016-05-01

This paper investigates the invariance properties of the time fractional Derrida-Lebowitz-Speer-Spohn (FDLSS) equation with Riemann-Liouville derivative. By using the Lie group analysis method of fractional differential equations, we derive Lie symmetries for the FDLSS equation. In a particular case of scaling transformations, we transform the FDLSS equation into a nonlinear ordinary fractional differential equation. Conservation laws for this equation are obtained with the aid of the new conservation theorem and the fractional generalization of the Noether operators.

Sparse maps—A systematic infrastructure for reduced-scaling electronic structure methods. II. Linear scaling domain based pair natural orbital coupled cluster theory

NASA Astrophysics Data System (ADS)

Riplinger, Christoph; Pinski, Peter; Becker, Ute; Valeev, Edward F.; Neese, Frank

2016-01-01

Domain based local pair natural orbital coupled cluster theory with single-, double-, and perturbative triple excitations (DLPNO-CCSD(T)) is a highly efficient local correlation method. It is known to be accurate and robust and can be used in a black box fashion in order to obtain coupled cluster quality total energies for large molecules with several hundred atoms. While previous implementations showed near linear scaling up to a few hundred atoms, several nonlinear scaling steps limited the applicability of the method for very large systems. In this work, these limitations are overcome and a linear scaling DLPNO-CCSD(T) method for closed shell systems is reported. The new implementation is based on the concept of sparse maps that was introduced in Part I of this series [P. Pinski, C. Riplinger, E. F. Valeev, and F. Neese, J. Chem. Phys. 143, 034108 (2015)]. Using the sparse map infrastructure, all essential computational steps (integral transformation and storage, initial guess, pair natural orbital construction, amplitude iterations, triples correction) are achieved in a linear scaling fashion. In addition, a number of additional algorithmic improvements are reported that lead to significant speedups of the method. The new, linear-scaling DLPNO-CCSD(T) implementation typically is 7 times faster than the previous implementation and consumes 4 times less disk space for large three-dimensional systems. For linear systems, the performance gains and memory savings are substantially larger. Calculations with more than 20 000 basis functions and 1000 atoms are reported in this work. In all cases, the time required for the coupled cluster step is comparable to or lower than for the preceding Hartree-Fock calculation, even if this is carried out with the efficient resolution-of-the-identity and chain-of-spheres approximations. The new implementation even reduces the error in absolute correlation energies by about a factor of two, compared to the already accurate previous implementation.

Accelerating universe with time variation of G and Λ

NASA Astrophysics Data System (ADS)

Darabi, F.

2012-03-01

We study a gravitational model in which scale transformations play the key role in obtaining dynamical G and Λ. We take a non-scale invariant gravitational action with a cosmological constant and a gravitational coupling constant. Then, by a scale transformation, through a dilaton field, we obtain a new action containing cosmological and gravitational coupling terms which are dynamically dependent on the dilaton field with Higgs type potential. The vacuum expectation value of this dilaton field, through spontaneous symmetry breaking on the basis of anthropic principle, determines the time variations of G and Λ. The relevance of these time variations to the current acceleration of the universe, coincidence problem, Mach's cosmological coincidence and those problems of standard cosmology addressed by inflationary models, are discussed. The current acceleration of the universe is shown to be a result of phase transition from radiation toward matter dominated eras. No real coincidence problem between matter and vacuum energy densities exists in this model and this apparent coincidence together with Mach's cosmological coincidence are shown to be simple consequences of a new kind of scale factor dependence of the energy momentum density as ρ˜ a -4. This model also provides the possibility for a super fast expansion of the scale factor at very early universe by introducing exotic type matter like cosmic strings.

Spatial and Temporal Scales of Surface Water-Groundwater Interactions

NASA Astrophysics Data System (ADS)

Boano, F.

2016-12-01

The interfaces between surface water and groundwater (i.e., river and lake sediments) represent hotspots for nutrient transformation in watersheds. This intense biochemical activity stems from the peculiar physicochemical properties of these interface areas. Here, the exchange of water and nutrients between surface and subsurface environments creates an ecotone region that can support the presence of different microbial species responsible for nutrient transformation. Previous studies have elucidated that water exchange between rivers and aquifers is organized in a complex system of nested flow cells. Each cell entails a range of residence timescales spanning multiple order of magnitudes, providing opportunities for different biochemical reactions to occur. Physically-bases models represent useful tools to deal with the wide range of spatial and temporal scales that characterize surface-subsurface water exchange. This contribution will present insights about how hydrodynamic processes control scale organization for surface water - groundwater interactions. The specific focus will be the influence of exchange processes on microbial activity and nutrient transformation, discussing how groundwater flow at watershed scale controls flow conditions and hence constrain microbial reactions at much smaller scales.

Visualization of synchronization of the uterine contraction signals: running cross-correlation and wavelet running cross-correlation methods.

PubMed

Oczeretko, Edward; Swiatecka, Jolanta; Kitlas, Agnieszka; Laudanski, Tadeusz; Pierzynski, Piotr

2006-01-01

In physiological research, we often study multivariate data sets, containing two or more simultaneously recorded time series. The aim of this paper is to present the cross-correlation and the wavelet cross-correlation methods to assess synchronization between contractions in different topographic regions of the uterus. From a medical point of view, it is important to identify time delays between contractions, which may be of potential diagnostic significance in various pathologies. The cross-correlation was computed in a moving window with a width corresponding to approximately two or three contractions. As a result, the running cross-correlation function was obtained. The propagation% parameter assessed from this function allows quantitative description of synchronization in bivariate time series. In general, the uterine contraction signals are very complicated. Wavelet transforms provide insight into the structure of the time series at various frequencies (scales). To show the changes of the propagation% parameter along scales, a wavelet running cross-correlation was used. At first, the continuous wavelet transforms as the uterine contraction signals were received and afterwards, a running cross-correlation analysis was conducted for each pair of transformed time series. The findings show that running functions are very useful in the analysis of uterine contractions.

Sonoran Desert ecosystem transformation by a C4 grass without the grass/fire cycle

USGS Publications Warehouse

Olsson, Aaryn D.; Betancourt, Julio; McClaran, Mitchel P.; Marsh, Stuart E.

2012-01-01

Aim Biological invasions facilitate ecosystem transformation by altering the structure and function, diversity, dominance and disturbance regimes. A classic case is the grass–fire cycle in which grass invasion increases the frequency, scale and/or intensity of wildfires and promotes the continued invasion of invasive grasses. Despite wide acceptance of the grass–fire cycle, questions linger about the relative roles that interspecific plant competition and fire play in ecosystem transformations. Location Sonoran Desert Arizona Upland of the Santa Catalina Mountains, Arizona, USA. Methods We measured species cover, density and saguaro (Carnegiea gigantea) size structure along gradients of Pennisetum ciliare invasion at 10 unburned/ungrazed P. ciliare patches. Regression models quantified differences in diversity, cover and density with respect to P. ciliare cover, and residence time and a Fisher's exact test detected demographic changes in saguaro populations. Because P. ciliare may have initially invaded locations that were both more invasible and less diverse, we ran analyses with and without the plots in which initial infestations were located. Results Richness and diversity decreased with P. ciliare cover as did cover and density of most dominant species. Richness and diversity declined with increasing time since invasion, suggesting an ongoing transformation. The proportion of old-to-young Carnegiea gigantea was significantly lower in plots with dominant P. ciliare cover. Main conclusions Rich desert scrub (15–25 species per plot) was transformed into depauperate grassland (2–5 species per plot) within 20 years following P. ciliare invasion without changes to the fire regime. While the onset of a grass–fire cycle may drive ecosystem change in the later stages and larger scales of grass invasions of arid lands, competition by P. ciliare can drive small-scale transformations earlier in the invasion. Linking competition-induced transformation rates with spatially explicit models of spread may be necessary for predicting landscape-level impacts on ecosystem processes in advance of a grass–fire cycle.

Environmental status of livestock and poultry sectors in China under current transformation stage.

PubMed

Qian, Yi; Song, Kaihui; Hu, Tao; Ying, Tianyu

2018-05-01

Intensive animal husbandry had aroused great environmental concerns in many developed countries. However, some developing countries are still undergoing the environmental pollution from livestock and poultry sectors. Driven by the large demand, China has experienced a remarkable increase in dairy and meat production, especially in the transformation stage from conventional household breeding to large-scale industrial breeding. At the same time, a large amount of manure from the livestock and poultry sector is released into waterbodies and soil, causing eutrophication and soil degradation. This condition will be reinforced in the large-scale cultivation where the amount of manure exceeds the soil nutrient capacity, if not treated or utilized properly. Our research aims to analyze whether the transformation of raising scale would be beneficial to the environment as well as present the latest status of livestock and poultry sectors in China. The estimation of the pollutants generated and discharged from livestock and poultry sector in China will facilitate the legislation of manure management. This paper analyzes the pollutants generated from the manure of the five principal commercial animals in different farming practices. The results show that the fattening pigs contribute almost half of the pollutants released from manure. Moreover, the beef cattle exert the largest environmental impact for unitary production, about 2-3 times of pork and 5-20 times of chicken. The animals raised with large-scale feedlots practice generate fewer pollutants than those raised in households. The shift towards industrial production of livestock and poultry is easier to manage from the environmental perspective, but adequate large-scale cultivation is encouraged. Regulation control, manure treatment and financial subsidies for the manure treatment and utilization are recommended to achieve the ecological agriculture in China. Copyright © 2017 Elsevier B.V. All rights reserved.

An alternative approach to calculating Area-Under-the-Curve (AUC) in delay discounting research.

PubMed

Borges, Allison M; Kuang, Jinyi; Milhorn, Hannah; Yi, Richard

2016-09-01

Applied to delay discounting data, Area-Under-the-Curve (AUC) provides an atheoretical index of the rate of delay discounting. The conventional method of calculating AUC, by summing the areas of the trapezoids formed by successive delay-indifference point pairings, does not account for the fact that most delay discounting tasks scale delay pseudoexponentially, that is, time intervals between delays typically get larger as delays get longer. This results in a disproportionate contribution of indifference points at long delays to the total AUC, with minimal contribution from indifference points at short delays. We propose two modifications that correct for this imbalance via a base-10 logarithmic transformation and an ordinal scaling transformation of delays. These newly proposed indices of discounting, AUClog d and AUCor d, address the limitation of AUC while preserving a primary strength (remaining atheoretical). Re-examination of previously published data provides empirical support for both AUClog d and AUCor d . Thus, we believe theoretical and empirical arguments favor these methods as the preferred atheoretical indices of delay discounting. © 2016 Society for the Experimental Analysis of Behavior.

Scientific and Technological Foundations for Scaling Production of Nanostructured Metals

NASA Astrophysics Data System (ADS)

Lowe, Terry C.; Davis, Casey F.; Rovira, Peter M.; Hayne, Mathew L.; Campbell, Gordon S.; Grzenia, Joel E.; Stock, Paige J.; Meagher, Rilee C.; Rack, Henry J.

2017-05-01

Severe Plastic Deformation (SPD) has been explored in a wide range of metals and alloys. However, there are only a few industrial scale implementations of SPD for commercial alloys. To demonstrate and evolve technology for producing ultrafine grain metals by SPD, a Nanostructured Metals Manufacturing Testbed (NMMT) has been established in Golden, Colorado. Machines for research scale and pilot scale Equal Channel Angular Pressing-Conform (ECAP-C) technology have been configured in the NMMT to systematically evaluate and evolve SPD processing and advance the foundational science and technology for manufacturing. We highlight the scientific and technological areas that are critical for scale up of continuous SPD of aluminum, copper, magnesium, titanium, and iron-based alloys. Key areas that we will address in this presentation include the need for comprehensive analysis of starting microstructures, data on operating deformation mechanisms, high pressure thermodynamics and phase transformation kinetics, tribological behaviors, temperature dependence of lubricant properties, adaptation of tolerances and shear intensity to match viscoplastic behaviors, real-time process monitoring, and mechanics of billet/tooling interactions.

Aeroelastic-Acoustics Simulation of Flight Systems

NASA Technical Reports Server (NTRS)

Gupta, kajal K.; Choi, S.; Ibrahim, A.

2009-01-01

This paper describes the details of a numerical finite element (FE) based analysis procedure and a resulting code for the simulation of the acoustics phenomenon arising from aeroelastic interactions. Both CFD and structural simulations are based on FE discretization employing unstructured grids. The sound pressure level (SPL) on structural surfaces is calculated from the root mean square (RMS) of the unsteady pressure and the acoustic wave frequencies are computed from a fast Fourier transform (FFT) of the unsteady pressure distribution as a function of time. The resulting tool proves to be unique as it is designed to analyze complex practical problems, involving large scale computations, in a routine fashion.

Real-time processing for full-range Fourier-domain optical-coherence tomography with zero-filling interpolation using multiple graphic processing units.

PubMed

Watanabe, Yuuki; Maeno, Seiya; Aoshima, Kenji; Hasegawa, Haruyuki; Koseki, Hitoshi

2010-09-01

The real-time display of full-range, 2048?axial pixelx1024?lateral pixel, Fourier-domain optical-coherence tomography (FD-OCT) images is demonstrated. The required speed was achieved by using dual graphic processing units (GPUs) with many stream processors to realize highly parallel processing. We used a zero-filling technique, including a forward Fourier transform, a zero padding to increase the axial data-array size to 8192, an inverse-Fourier transform back to the spectral domain, a linear interpolation from wavelength to wavenumber, a lateral Hilbert transform to obtain the complex spectrum, a Fourier transform to obtain the axial profiles, and a log scaling. The data-transfer time of the frame grabber was 15.73?ms, and the processing time, which includes the data transfer between the GPU memory and the host computer, was 14.75?ms, for a total time shorter than the 36.70?ms frame-interval time using a line-scan CCD camera operated at 27.9?kHz. That is, our OCT system achieved a processed-image display rate of 27.23 frames/s.

Relaxation dynamics and transformation kinetics of deeply supercooled water: Temperature, pressure, doping, and proton/deuteron isotope effects.

PubMed

Lemke, Sonja; Handle, Philip H; Plaga, Lucie J; Stern, Josef N; Seidl, Markus; Fuentes-Landete, Violeta; Amann-Winkel, Katrin; Köster, Karsten W; Gainaru, Catalin; Loerting, Thomas; Böhmer, Roland

2017-07-21

Above its glass transition, the equilibrated high-density amorphous ice (HDA) transforms to the low-density pendant (LDA). The temperature dependence of the transformation is monitored at ambient pressure using dielectric spectroscopy and at elevated pressures using dilatometry. It is found that near the glass transition temperature of deuterated samples, the transformation kinetics is 300 times slower than the structural relaxation, while for protonated samples, the time scale separation is at least 30 000 and insensitive to doping. The kinetics of the HDA to LDA transformation lacks a proton/deuteron isotope effect, revealing that this process is dominated by the restructuring of the oxygen network. The x-ray diffraction experiments performed on samples at intermediate transition stages reflect a linear combination of the LDA and HDA patterns implying a macroscopic phase separation, instead of a local intermixing of the two amorphous states.

Relaxation dynamics and transformation kinetics of deeply supercooled water: Temperature, pressure, doping, and proton/deuteron isotope effects

NASA Astrophysics Data System (ADS)

Lemke, Sonja; Handle, Philip H.; Plaga, Lucie J.; Stern, Josef N.; Seidl, Markus; Fuentes-Landete, Violeta; Amann-Winkel, Katrin; Köster, Karsten W.; Gainaru, Catalin; Loerting, Thomas; Böhmer, Roland

2017-07-01

Above its glass transition, the equilibrated high-density amorphous ice (HDA) transforms to the low-density pendant (LDA). The temperature dependence of the transformation is monitored at ambient pressure using dielectric spectroscopy and at elevated pressures using dilatometry. It is found that near the glass transition temperature of deuterated samples, the transformation kinetics is 300 times slower than the structural relaxation, while for protonated samples, the time scale separation is at least 30 000 and insensitive to doping. The kinetics of the HDA to LDA transformation lacks a proton/deuteron isotope effect, revealing that this process is dominated by the restructuring of the oxygen network. The x-ray diffraction experiments performed on samples at intermediate transition stages reflect a linear combination of the LDA and HDA patterns implying a macroscopic phase separation, instead of a local intermixing of the two amorphous states.

Controlling Surface Plasmons Through Covariant Transformation of the Spin-Dependent Geometric Phase Between Curved Metamaterials

NASA Astrophysics Data System (ADS)

Zhong, Fan; Li, Jensen; Liu, Hui; Zhu, Shining

2018-06-01

General relativity uses curved space-time to describe accelerating frames. The movement of particles in different curved space-times can be regarded as equivalent physical processes based on the covariant transformation between different frames. In this Letter, we use one-dimensional curved metamaterials to mimic accelerating particles in curved space-times. The different curved shapes of structures are used to mimic different accelerating frames. The different geometric phases along the structure are used to mimic different movements in the frame. Using the covariant principle of general relativity, we can obtain equivalent nanostructures based on space-time transformations, such as the Lorentz transformation and conformal transformation. In this way, many covariant structures can be found that produce the same surface plasmon fields when excited by spin photons. A new kind of accelerating beam, the Rindler beam, is obtained based on the Rindler metric in gravity. Very large effective indices can be obtained in such systems based on geometric-phase gradient. This general covariant design method can be extended to many other optical media.

Spatio-temporal precipitation climatology over complex terrain using a censored additive regression model.

PubMed

Stauffer, Reto; Mayr, Georg J; Messner, Jakob W; Umlauf, Nikolaus; Zeileis, Achim

2017-06-15

Flexible spatio-temporal models are widely used to create reliable and accurate estimates for precipitation climatologies. Most models are based on square root transformed monthly or annual means, where a normal distribution seems to be appropriate. This assumption becomes invalid on a daily time scale as the observations involve large fractions of zero observations and are limited to non-negative values. We develop a novel spatio-temporal model to estimate the full climatological distribution of precipitation on a daily time scale over complex terrain using a left-censored normal distribution. The results demonstrate that the new method is able to account for the non-normal distribution and the large fraction of zero observations. The new climatology provides the full climatological distribution on a very high spatial and temporal resolution, and is competitive with, or even outperforms existing methods, even for arbitrary locations.

Transformations of Carotenoids in the Oceanic Water Column.

DTIC Science & Technology

1982-11-01

suggests that dehydration and epoxide rearrangement occur over considerably longer time scales than ester hydrolysis . Isofucoxanthin was not isolated...transformations: 1) ester hydrolysis via zooplanktonic metabolism, 2) dehydration via bacterial metabolism, and 3) epoxide opening via slow chemical...be restricted to zooplankton and not common to other higher heLerotrophs, as is ester hydrolysis . The high concentration of fuco- dehydrates and short

Incoherent optical generalized Hough transform: pattern recognition and feature extraction applications

NASA Astrophysics Data System (ADS)

Fernández, Ariel; Ferrari, José A.

2017-05-01

Pattern recognition and feature extraction are image processing applications of great interest in defect inspection and robot vision among others. In comparison to purely digital methods, the attractiveness of optical processors for pattern recognition lies in their highly parallel operation and real-time processing capability. This work presents an optical implementation of the generalized Hough transform (GHT), a well-established technique for recognition of geometrical features in binary images. Detection of a geometric feature under the GHT is accomplished by mapping the original image to an accumulator space; the large computational requirements for this mapping make the optical implementation an attractive alternative to digital-only methods. We explore an optical setup where the transformation is obtained, and the size and orientation parameters can be controlled, allowing for dynamic scale and orientation-variant pattern recognition. A compact system for the above purposes results from the use of an electrically tunable lens for scale control and a pupil mask implemented on a high-contrast spatial light modulator for orientation/shape variation of the template. Real-time can also be achieved. In addition, by thresholding of the GHT and optically inverse transforming, the previously detected features of interest can be extracted.

Directional Multi-scale Modeling of High-Resolution Computed Tomography (HRCT) Lung Images for Diffuse Lung Disease Classification

NASA Astrophysics Data System (ADS)

Vo, Kiet T.; Sowmya, Arcot

A directional multi-scale modeling scheme based on wavelet and contourlet transforms is employed to describe HRCT lung image textures for classifying four diffuse lung disease patterns: normal, emphysema, ground glass opacity (GGO) and honey-combing. Generalized Gaussian density parameters are used to represent the detail sub-band features obtained by wavelet and contourlet transforms. In addition, support vector machines (SVMs) with excellent performance in a variety of pattern classification problems are used as classifier. The method is tested on a collection of 89 slices from 38 patients, each slice of size 512x512, 16 bits/pixel in DICOM format. The dataset contains 70,000 ROIs of those slices marked by experienced radiologists. We employ this technique at different wavelet and contourlet transform scales for diffuse lung disease classification. The technique presented here has best overall sensitivity 93.40% and specificity 98.40%.

Singularity analysis based on wavelet transform of fractal measures for identifying geochemical anomaly in mineral exploration

NASA Astrophysics Data System (ADS)

Chen, Guoxiong; Cheng, Qiuming

2016-02-01

Multi-resolution and scale-invariance have been increasingly recognized as two closely related intrinsic properties endowed in geofields such as geochemical and geophysical anomalies, and they are commonly investigated by using multiscale- and scaling-analysis methods. In this paper, the wavelet-based multiscale decomposition (WMD) method was proposed to investigate the multiscale natures of geochemical pattern from large scale to small scale. In the light of the wavelet transformation of fractal measures, we demonstrated that the wavelet approximation operator provides a generalization of box-counting method for scaling analysis of geochemical patterns. Specifically, the approximation coefficient acts as the generalized density-value in density-area fractal modeling of singular geochemical distributions. Accordingly, we presented a novel local singularity analysis (LSA) using the WMD algorithm which extends the conventional moving averaging to a kernel-based operator for implementing LSA. Finally, the novel LSA was validated using a case study dealing with geochemical data (Fe2O3) in stream sediments for mineral exploration in Inner Mongolia, China. In comparison with the LSA implemented using the moving averaging method the novel LSA using WMD identified improved weak geochemical anomalies associated with mineralization in covered area.

Coherent time-stretch transformation for real-time capture of wideband signals.

PubMed

Buckley, Brandon W; Madni, Asad M; Jalali, Bahram

2013-09-09

Time stretch transformation of wideband waveforms boosts the performance of analog-to-digital converters and digital signal processors by slowing down analog electrical signals before digitization. The transform is based on dispersive Fourier transformation implemented in the optical domain. A coherent receiver would be ideal for capturing the time-stretched optical signal. Coherent receivers offer improved sensitivity, allow for digital cancellation of dispersion-induced impairments and optical nonlinearities, and enable decoding of phase-modulated optical data formats. Because time-stretch uses a chirped broadband (>1 THz) optical carrier, a new coherent detection technique is required. In this paper, we introduce and demonstrate coherent time stretch transformation; a technique that combines dispersive Fourier transform with optically broadband coherent detection.

Conjunction of wavelet transform and SOM-mutual information data pre-processing approach for AI-based Multi-Station nitrate modeling of watersheds

NASA Astrophysics Data System (ADS)

Nourani, Vahid; Andalib, Gholamreza; Dąbrowska, Dominika

2017-05-01

Accurate nitrate load predictions can elevate decision management of water quality of watersheds which affects to environment and drinking water. In this paper, two scenarios were considered for Multi-Station (MS) nitrate load modeling of the Little River watershed. In the first scenario, Markovian characteristics of streamflow-nitrate time series were proposed for the MS modeling. For this purpose, feature extraction criterion of Mutual Information (MI) was employed for input selection of artificial intelligence models (Feed Forward Neural Network, FFNN and least square support vector machine). In the second scenario for considering seasonality-based characteristics of the time series, wavelet transform was used to extract multi-scale features of streamflow-nitrate time series of the watershed's sub-basins to model MS nitrate loads. Self-Organizing Map (SOM) clustering technique which finds homogeneous sub-series clusters was also linked to MI for proper cluster agent choice to be imposed into the models for predicting the nitrate loads of the watershed's sub-basins. The proposed MS method not only considers the prediction of the outlet nitrate but also covers predictions of interior sub-basins nitrate load values. The results indicated that the proposed FFNN model coupled with the SOM-MI improved the performance of MS nitrate predictions compared to the Markovian-based models up to 39%. Overall, accurate selection of dominant inputs which consider seasonality-based characteristics of streamflow-nitrate process could enhance the efficiency of nitrate load predictions.

A Rapid, Highly Efficient and Economical Method of Agrobacterium-Mediated In planta Transient Transformation in Living Onion Epidermis

PubMed Central

Xu, Kedong; Huang, Xiaohui; Wu, Manman; Wang, Yan; Chang, Yunxia; Liu, Kun; Zhang, Ju; Zhang, Yi; Zhang, Fuli; Yi, Liming; Li, Tingting; Wang, Ruiyue; Tan, Guangxuan; Li, Chengwei

2014-01-01

Transient transformation is simpler, more efficient and economical in analyzing protein subcellular localization than stable transformation. Fluorescent fusion proteins were often used in transient transformation to follow the in vivo behavior of proteins. Onion epidermis, which has large, living and transparent cells in a monolayer, is suitable to visualize fluorescent fusion proteins. The often used transient transformation methods included particle bombardment, protoplast transfection and Agrobacterium-mediated transformation. Particle bombardment in onion epidermis was successfully established, however, it was expensive, biolistic equipment dependent and with low transformation efficiency. We developed a highly efficient in planta transient transformation method in onion epidermis by using a special agroinfiltration method, which could be fulfilled within 5 days from the pretreatment of onion bulb to the best time-point for analyzing gene expression. The transformation conditions were optimized to achieve 43.87% transformation efficiency in living onion epidermis. The developed method has advantages in cost, time-consuming, equipment dependency and transformation efficiency in contrast with those methods of particle bombardment in onion epidermal cells, protoplast transfection and Agrobacterium-mediated transient transformation in leaf epidermal cells of other plants. It will facilitate the analysis of protein subcellular localization on a large scale. PMID:24416168

Super-Resolution for Color Imagery

DTIC Science & Technology

2017-09-01

separately; however, it requires performing the super-resolution computation 3 times. We transform images in the default red, green, blue (RGB) color space...chrominance components based on ARL’s alias-free image upsampling using Fourier-based windowing methods. A reverse transformation is performed on... Transformation from sRGB to CIELAB............................................... 3 Fig. 2 YCbCr mathematical coordinate transformation

Multi-scale variability and long-range memory in indoor Radon concentrations from Coimbra, Portugal

NASA Astrophysics Data System (ADS)

Donner, Reik V.; Potirakis, Stelios; Barbosa, Susana

2014-05-01

The presence or absence of long-range correlations in the variations of indoor Radon concentrations has recently attracted considerable interest. As a radioactive gas naturally emitted from the ground in certain geological settings, understanding environmental factors controlling Radon concentrations and their dynamics is important for estimating its effect on human health and the efficiency of possible measures for reducing the corresponding exposition. In this work, we re-analyze two high-resolution records of indoor Radon concentrations from Coimbra, Portugal, each of which spans several months of continuous measurements. In order to evaluate the presence of long-range correlations and fractal scaling, we utilize a multiplicity of complementary methods, including power spectral analysis, ARFIMA modeling, classical and multi-fractal detrended fluctuation analysis, and two different estimators of the signals' fractal dimensions. Power spectra and fluctuation functions reveal some complex behavior with qualitatively different properties on different time-scales: white noise in the high-frequency part, indications of some long-range correlated process dominating time scales of several hours to days, and pronounced low-frequency variability associated with tidal and/or meteorological forcing. In order to further decompose these different scales of variability, we apply two different approaches. On the one hand, applying multi-resolution analysis based on the discrete wavelet transform allows separately studying contributions on different time scales and characterize their specific correlation and scaling properties. On the other hand, singular system analysis (SSA) provides a reconstruction of the essential modes of variability. Specifically, by considering only the first leading SSA modes, we achieve an efficient de-noising of our environmental signals, highlighting the low-frequency variations together with some distinct scaling on sub-daily time-scales resembling the properties of a long-range correlated process.

Machine learning algorithms for mode-of-action classification in toxicity assessment.

PubMed

Zhang, Yile; Wong, Yau Shu; Deng, Jian; Anton, Cristina; Gabos, Stephan; Zhang, Weiping; Huang, Dorothy Yu; Jin, Can

2016-01-01

Real Time Cell Analysis (RTCA) technology is used to monitor cellular changes continuously over the entire exposure period. Combining with different testing concentrations, the profiles have potential in probing the mode of action (MOA) of the testing substances. In this paper, we present machine learning approaches for MOA assessment. Computational tools based on artificial neural network (ANN) and support vector machine (SVM) are developed to analyze the time-concentration response curves (TCRCs) of human cell lines responding to tested chemicals. The techniques are capable of learning data from given TCRCs with known MOA information and then making MOA classification for the unknown toxicity. A novel data processing step based on wavelet transform is introduced to extract important features from the original TCRC data. From the dose response curves, time interval leading to higher classification success rate can be selected as input to enhance the performance of the machine learning algorithm. This is particularly helpful when handling cases with limited and imbalanced data. The validation of the proposed method is demonstrated by the supervised learning algorithm applied to the exposure data of HepG2 cell line to 63 chemicals with 11 concentrations in each test case. Classification success rate in the range of 85 to 95 % are obtained using SVM for MOA classification with two clusters to cases up to four clusters. Wavelet transform is capable of capturing important features of TCRCs for MOA classification. The proposed SVM scheme incorporated with wavelet transform has a great potential for large scale MOA classification and high-through output chemical screening.

Numerical tests of local scale invariance in ageing q-state Potts models

NASA Astrophysics Data System (ADS)

Lorenz, E.; Janke, W.

2007-01-01

Much effort has been spent over the last years to achieve a coherent theoretical description of ageing as a non-linear dynamics process. Long supposed to be a consequence of the slow dynamics of glassy systems only, ageing phenomena could also be identified in the phase-ordering kinetics of simple ferromagnets. As a phenomenological approach Henkel et al. developed a group of local scale transformations under which two-time autocorrelation and response functions should transform covariantly. This work is to extend previous numerical tests of the predicted scaling functions for the Ising model by Monte Carlo simulations of two-dimensional q-state Potts models with q=3 and 8, which, in equilibrium, undergo temperature-driven phase transitions of second and first order, respectively.

Atomistic to Continuum Multiscale and Multiphysics Simulation of NiTi Shape Memory Alloy

NASA Astrophysics Data System (ADS)

Gur, Sourav

Shape memory alloys (SMAs) are materials that show reversible, thermo-elastic, diffusionless, displacive (solid to solid) phase transformation, due to the application of temperature and/ or stress (/strain). Among different SMAs, NiTi is a popular one. NiTi shows reversible phase transformation, the shape memory effect (SME), where irreversible deformations are recovered upon heating, and superelasticity (SE), where large strains imposed at high enough temperatures are fully recovered. Phase transformation process in NiTi SMA is a very complex process that involves the competition between developed internal strain and phonon dispersion instability. In NiTi SMA, phase transformation occurs over a wide range of temperature and/ or stress (strain) which involves, evolution of different crystalline phases (cubic austenite i.e. B2, different monoclinic variant of martensite i.e. B19', and orthorhombic B19 or BCO structures). Further, it is observed from experimental and computational studies that the evolution kinetics and growth rate of different phases in NiTi SMA vary significantly over a wide spectrum of spatio-temporal scales, especially with length scales. At nano-meter length scale, phase transformation temperatures, critical transformation stress (or strain) and phase fraction evolution change significantly with sample or simulation cell size and grain size. Even, below a critical length scale, the phase transformation process stops. All these aspects make NiTi SMA very interesting to the science and engineering research community and in this context, the present focuses on the following aspects. At first this study address the stability, evolution and growth kinetics of different phases (B2 and variants of B19'), at different length scales, starting from the atomic level and ending at the continuum macroscopic level. The effects of simulation cell size, grain size, and presence of free surface and grain boundary on the phase transformation process (transformation temperature, phase fraction evolution kinetics due to temperature) are also demonstrated herein. Next, to couple and transfer the statistical information of length scale dependent phase transformation process, multiscale/ multiphysics methods are used. Here, the computational difficulty from the fact that the representative governing equations (i.e. different sub-methods such as molecular dynamics simulations, phase field simulations and continuum level constitutive/ material models) are only valid or can be implemented over a range of spatiotemporal scales. Therefore, in the present study, a wavelet based multiscale coupling method is used, where simulation results (phase fraction evolution kinetics) from different sub-methods are linked via concurrent multiscale coupling fashion. Finally, these multiscale/ multiphysics simulation results are used to develop/ modify the macro/ continuum scale thermo-mechanical constitutive relations for NiTi SMA. Finally, the improved material model is used to model new devices, such as thermal diodes and smart dampers.

An experimental and theoretical study of reaction mechanisms between nitriles and hydroxylamine.

PubMed

Vörös, Attila; Mucsi, Zoltán; Baán, Zoltán; Timári, Géza; Hermecz, István; Mizsey, Péter; Finta, Zoltán

2014-10-28

The industrially relevant reaction between nitriles and hydroxylamine yielding amidoximes was studied in different molecular solvents and in ionic liquids. In industry, this procedure is carried out on the ton scale in alcohol solutions and the above transformation produces a significant amount of unexpected amide by-product, depending on the nature of the nitrile, which can cause further analytical and purification issues. Although there were earlier attempts to propose mechanisms for this transformation, the real reaction pathway is still under discussion. A new detailed reaction mechanistic explanation, based on theoretical and experimental proof, is given to augment the former mechanisms, which allowed us to find a more efficient, side-product free procedure. Interpreting the theoretical results obtained, it was shown that the application of specific imidazolium, phosphonium and quaternary ammonium based ionic liquids could decrease simultaneously the reaction time while eliminating the amide side-product, leading to the targeted product selectively. This robust and economic procedure now affords a fast, selective amide free synthesis of amidoximes.

A method of real-time fault diagnosis for power transformers based on vibration analysis

NASA Astrophysics Data System (ADS)

Hong, Kaixing; Huang, Hai; Zhou, Jianping; Shen, Yimin; Li, Yujie

2015-11-01

In this paper, a novel probability-based classification model is proposed for real-time fault detection of power transformers. First, the transformer vibration principle is introduced, and two effective feature extraction techniques are presented. Next, the details of the classification model based on support vector machine (SVM) are shown. The model also includes a binary decision tree (BDT) which divides transformers into different classes according to health state. The trained model produces posterior probabilities of membership to each predefined class for a tested vibration sample. During the experiments, the vibrations of transformers under different conditions are acquired, and the corresponding feature vectors are used to train the SVM classifiers. The effectiveness of this model is illustrated experimentally on typical in-service transformers. The consistency between the results of the proposed model and the actual condition of the test transformers indicates that the model can be used as a reliable method for transformer fault detection.

Estimate of standard deviation for a log-transformed variable using arithmetic means and standard deviations.

PubMed

Quan, Hui; Zhang, Ji

2003-09-15

Analyses of study variables are frequently based on log transformations. To calculate the power for detecting the between-treatment difference in the log scale, we need an estimate of the standard deviation of the log-transformed variable. However, in many situations a literature search only provides the arithmetic means and the corresponding standard deviations. Without individual log-transformed data to directly calculate the sample standard deviation, we need alternative methods to estimate it. This paper presents methods for estimating and constructing confidence intervals for the standard deviation of a log-transformed variable given the mean and standard deviation of the untransformed variable. It also presents methods for estimating the standard deviation of change from baseline in the log scale given the means and standard deviations of the untransformed baseline value, on-treatment value and change from baseline. Simulations and examples are provided to assess the performance of these estimates. Copyright 2003 John Wiley & Sons, Ltd.

Pemetrexed degradation by photocatalytic process: Kinetics, identification of transformation products and estimation of toxicity.

PubMed

Secrétan, Philippe-Henri; Karoui, Maher; Levi, Yves; Sadou-Yayé, Hassane; Tortolano, Lionel; Solgadi, Audrey; Yagoubi, Najet; Do, Bernard

2018-05-15

This study employed a UV-A/visible/TiO 2 system to investigate the degradation of pemetrexed, an antifolate agent used in chemotherapy. The laboratory-scale method employed a photostability chamber that could be used to study multiple samples. Reversed-phase HPLC coupled with high-resolution ESI-LTQ-Orbitrap mass spectrometry was used to determine the transformation products (TPs) of PEME. Based on the identified TPs and existing chemical knowledge, the mechanism of degradation of the target compound was proposed. Concentrations were monitored as a function of time, and the degradation kinetics were compared. The structures of seven TPs, four of which have not been described to date, were proposed. Most of the TPs stemmed from OH radical additions to the dihydropyrrole moiety and oxidative decarboxylation of the glutamate residue. Based on the elucidated structures, a computational toxicity assessment was performed, showing that the TPs with higher log D values than the parent compound are more toxic than the PEME itself. To support these findings, the toxicities of irradiated samples on Vibrio fischeri were monitored over time. The experimental results corresponded well with the results of previous computational studies. Copyright © 2017 Elsevier B.V. All rights reserved.

Communication: A reduced scaling J-engine based reformulation of SOS-MP2 using graphics processing units.

PubMed

Maurer, S A; Kussmann, J; Ochsenfeld, C

2014-08-07

We present a low-prefactor, cubically scaling scaled-opposite-spin second-order Møller-Plesset perturbation theory (SOS-MP2) method which is highly suitable for massively parallel architectures like graphics processing units (GPU). The scaling is reduced from O(N⁵) to O(N³) by a reformulation of the MP2-expression in the atomic orbital basis via Laplace transformation and the resolution-of-the-identity (RI) approximation of the integrals in combination with efficient sparse algebra for the 3-center integral transformation. In contrast to previous works that employ GPUs for post Hartree-Fock calculations, we do not simply employ GPU-based linear algebra libraries to accelerate the conventional algorithm. Instead, our reformulation allows to replace the rate-determining contraction step with a modified J-engine algorithm, that has been proven to be highly efficient on GPUs. Thus, our SOS-MP2 scheme enables us to treat large molecular systems in an accurate and efficient manner on a single GPU-server.

Automatic detection of muscle activity from mechanomyogram signals: a comparison of amplitude and wavelet-based methods.

PubMed

Alves, Natasha; Chau, Tom

2010-04-01

Knowledge of muscle activity timing is critical to many clinical applications, such as the assessment of muscle coordination and the prescription of muscle-activated switches for individuals with disabilities. In this study, we introduce a continuous wavelet transform (CWT) algorithm for the detection of muscle activity via mechanomyogram (MMG) signals. CWT coefficients of the MMG signal were compared to scale-specific thresholds derived from the baseline signal to estimate the timing of muscle activity. Test signals were recorded from the flexor carpi radialis muscles of 15 able-bodied participants as they squeezed and released a hand dynamometer. Using the dynamometer signal as a reference, the proposed CWT detection algorithm was compared against a global-threshold CWT detector as well as amplitude-based event detection for sensitivity and specificity to voluntary contractions. The scale-specific CWT-based algorithm exhibited superior detection performance over the other detectors. CWT detection also showed good muscle selectivity during hand movement, particularly when a given muscle was the primary facilitator of the contraction. This may suggest that, during contraction, the compound MMG signal has a recurring morphological pattern that is not prevalent in the baseline signal. The ability of CWT analysis to be implemented in real time makes it a candidate for muscle-activity detection in clinical applications.

Hierarchical coarse-graining transform.

PubMed

Pancaldi, Vera; King, Peter R; Christensen, Kim

2009-03-01

We present a hierarchical transform that can be applied to Laplace-like differential equations such as Darcy's equation for single-phase flow in a porous medium. A finite-difference discretization scheme is used to set the equation in the form of an eigenvalue problem. Within the formalism suggested, the pressure field is decomposed into an average value and fluctuations of different kinds and at different scales. The application of the transform to the equation allows us to calculate the unknown pressure with a varying level of detail. A procedure is suggested to localize important features in the pressure field based only on the fine-scale permeability, and hence we develop a form of adaptive coarse graining. The formalism and method are described and demonstrated using two synthetic toy problems.

Long memory analysis by using maximal overlapping discrete wavelet transform

NASA Astrophysics Data System (ADS)

Shafie, Nur Amalina binti; Ismail, Mohd Tahir; Isa, Zaidi

2015-05-01

Long memory process is the asymptotic decay of the autocorrelation or spectral density around zero. The main objective of this paper is to do a long memory analysis by using the Maximal Overlapping Discrete Wavelet Transform (MODWT) based on wavelet variance. In doing so, stock market of Malaysia, China, Singapore, Japan and United States of America are used. The risk of long term and short term investment are also being looked into. MODWT can be analyzed with time domain and frequency domain simultaneously and decomposing wavelet variance to different scales without loss any information. All countries under studied show that they have long memory. Subprime mortgage crisis in 2007 is occurred in the United States of America are possible affect to the major trading countries. Short term investment is more risky than long term investment.

Wavelet based free-form deformations for nonrigid registration

NASA Astrophysics Data System (ADS)

Sun, Wei; Niessen, Wiro J.; Klein, Stefan

2014-03-01

In nonrigid registration, deformations may take place on the coarse and fine scales. For the conventional B-splines based free-form deformation (FFD) registration, these coarse- and fine-scale deformations are all represented by basis functions of a single scale. Meanwhile, wavelets have been proposed as a signal representation suitable for multi-scale problems. Wavelet analysis leads to a unique decomposition of a signal into its coarse- and fine-scale components. Potentially, this could therefore be useful for image registration. In this work, we investigate whether a wavelet-based FFD model has advantages for nonrigid image registration. We use a B-splines based wavelet, as defined by Cai and Wang.1 This wavelet is expressed as a linear combination of B-spline basis functions. Derived from the original B-spline function, this wavelet is smooth, differentiable, and compactly supported. The basis functions of this wavelet are orthogonal across scales in Sobolev space. This wavelet was previously used for registration in computer vision, in 2D optical flow problems,2 but it was not compared with the conventional B-spline FFD in medical image registration problems. An advantage of choosing this B-splines based wavelet model is that the space of allowable deformation is exactly equivalent to that of the traditional B-spline. The wavelet transformation is essentially a (linear) reparameterization of the B-spline transformation model. Experiments on 10 CT lung and 18 T1-weighted MRI brain datasets show that wavelet based registration leads to smoother deformation fields than traditional B-splines based registration, while achieving better accuracy.

CLAST: CUDA implemented large-scale alignment search tool.

PubMed

Yano, Masahiro; Mori, Hiroshi; Akiyama, Yutaka; Yamada, Takuji; Kurokawa, Ken

2014-12-11

Metagenomics is a powerful methodology to study microbial communities, but it is highly dependent on nucleotide sequence similarity searching against sequence databases. Metagenomic analyses with next-generation sequencing technologies produce enormous numbers of reads from microbial communities, and many reads are derived from microbes whose genomes have not yet been sequenced, limiting the usefulness of existing sequence similarity search tools. Therefore, there is a clear need for a sequence similarity search tool that can rapidly detect weak similarity in large datasets. We developed a tool, which we named CLAST (CUDA implemented large-scale alignment search tool), that enables analyses of millions of reads and thousands of reference genome sequences, and runs on NVIDIA Fermi architecture graphics processing units. CLAST has four main advantages over existing alignment tools. First, CLAST was capable of identifying sequence similarities ~80.8 times faster than BLAST and 9.6 times faster than BLAT. Second, CLAST executes global alignment as the default (local alignment is also an option), enabling CLAST to assign reads to taxonomic and functional groups based on evolutionarily distant nucleotide sequences with high accuracy. Third, CLAST does not need a preprocessed sequence database like Burrows-Wheeler Transform-based tools, and this enables CLAST to incorporate large, frequently updated sequence databases. Fourth, CLAST requires <2 GB of main memory, making it possible to run CLAST on a standard desktop computer or server node. CLAST achieved very high speed (similar to the Burrows-Wheeler Transform-based Bowtie 2 for long reads) and sensitivity (equal to BLAST, BLAT, and FR-HIT) without the need for extensive database preprocessing or a specialized computing platform. Our results demonstrate that CLAST has the potential to be one of the most powerful and realistic approaches to analyze the massive amount of sequence data from next-generation sequencing technologies.

A scale-invariant change detection method for land use/cover change research

NASA Astrophysics Data System (ADS)

Xing, Jin; Sieber, Renee; Caelli, Terrence

2018-07-01

Land Use/Cover Change (LUCC) detection relies increasingly on comparing remote sensing images with different spatial and spectral scales. Based on scale-invariant image analysis algorithms in computer vision, we propose a scale-invariant LUCC detection method to identify changes from scale heterogeneous images. This method is composed of an entropy-based spatial decomposition, two scale-invariant feature extraction methods, Maximally Stable Extremal Region (MSER) and Scale-Invariant Feature Transformation (SIFT) algorithms, a spatial regression voting method to integrate MSER and SIFT results, a Markov Random Field-based smoothing method, and a support vector machine classification method to assign LUCC labels. We test the scale invariance of our new method with a LUCC case study in Montreal, Canada, 2005-2012. We found that the scale-invariant LUCC detection method provides similar accuracy compared with the resampling-based approach but this method avoids the LUCC distortion incurred by resampling.

Using the Sociocognitive-Transformative Approach in Writing Classrooms: Effects on L2 Learners' Writing Performance

ERIC Educational Resources Information Center

Barrot, Jessie S.

2018-01-01

The current study used a scale-based approach and complexity, accuracy, and fluency (CAF) analysis to comprehensively capture the effects of the sociocognitive-transformative approach on 2nd language (L2) learners' writing performance. The study involved 66 preuniversity intermediate L2 students from 4 different English classes. I randomly…

Computer-aided mass detection in mammography: False positive reduction via gray-scale invariant ranklet texture features

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

Masotti, Matteo; Lanconelli, Nico; Campanini, Renato

In this work, gray-scale invariant ranklet texture features are proposed for false positive reduction (FPR) in computer-aided detection (CAD) of breast masses. Two main considerations are at the basis of this proposal. First, false positive (FP) marks surviving our previous CAD system seem to be characterized by specific texture properties that can be used to discriminate them from masses. Second, our previous CAD system achieves invariance to linear/nonlinear monotonic gray-scale transformations by encoding regions of interest into ranklet images through the ranklet transform, an image transformation similar to the wavelet transform, yet dealing with pixels' ranks rather than with theirmore » gray-scale values. Therefore, the new FPR approach proposed herein defines a set of texture features which are calculated directly from the ranklet images corresponding to the regions of interest surviving our previous CAD system, hence, ranklet texture features; then, a support vector machine (SVM) classifier is used for discrimination. As a result of this approach, texture-based information is used to discriminate FP marks surviving our previous CAD system; at the same time, invariance to linear/nonlinear monotonic gray-scale transformations of the new CAD system is guaranteed, as ranklet texture features are calculated from ranklet images that have this property themselves by construction. To emphasize the gray-scale invariance of both the previous and new CAD systems, training and testing are carried out without any in-between parameters' adjustment on mammograms having different gray-scale dynamics; in particular, training is carried out on analog digitized mammograms taken from a publicly available digital database, whereas testing is performed on full-field digital mammograms taken from an in-house database. Free-response receiver operating characteristic (FROC) curve analysis of the two CAD systems demonstrates that the new approach achieves a higher reduction of FP marks when compared to the previous one. Specifically, at 60%, 65%, and 70% per-mammogram sensitivity, the new CAD system achieves 0.50, 0.68, and 0.92 FP marks per mammogram, whereas at 70%, 75%, and 80% per-case sensitivity it achieves 0.37, 0.48, and 0.71 FP marks per mammogram, respectively. Conversely, at the same sensitivities, the previous CAD system reached 0.71, 0.87, and 1.15 FP marks per mammogram, and 0.57, 0.73, and 0.92 FPs per mammogram. Also, statistical significance of the difference between the two per-mammogram and per-case FROC curves is demonstrated by the p-value<0.001 returned by jackknife FROC analysis performed on the two CAD systems.« less

Smoothing analysis of slug tests data for aquifer characterization at laboratory scale

NASA Astrophysics Data System (ADS)

Aristodemo, Francesco; Ianchello, Mario; Fallico, Carmine

2018-07-01

The present paper proposes a smoothing analysis of hydraulic head data sets obtained by means of different slug tests introduced in a confined aquifer. Laboratory experiments were performed through a 3D large-scale physical model built at the University of Calabria. The hydraulic head data were obtained by a pressure transducer placed in the injection well and subjected to a processing operation to smooth out the high-frequency noise occurring in the recorded signals. The adopted smoothing techniques working in time, frequency and time-frequency domain are the Savitzky-Golay filter modeled by third-order polynomial, the Fourier Transform and two types of Wavelet Transform (Mexican hat and Morlet). The performances of the filtered time series of the hydraulic heads for different slug volumes and measurement frequencies were statistically analyzed in terms of optimal fitting of the classical Cooper's equation. For practical purposes, the hydraulic heads smoothed by the involved techniques were used to determine the hydraulic conductivity of the aquifer. The energy contents and the frequency oscillations of the hydraulic head variations in the aquifer were exploited in the time-frequency domain by means of Wavelet Transform as well as the non-linear features of the observed hydraulic head oscillations around the theoretical Cooper's equation.

Preventive overhaul time for power transformers

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

Sarmadi, M.; Rouhi, J.; Fayyaz, A.

Power transformers are the major piece of equipment in high-voltage substations. A considerable number of these transformers exist in Iran`s integrated network. Due to the climate diversity and improper usage, many of these transformers age rapidly, suffer failure and are taken out of service before half their useful life. At the present time the utility companies have no specific time-frame and plan for preventive overhaul. Detection of preventive overhaul time will increase the remaining life of transformers and improve the reliability of substations. An exact check of the remaining lifetime of transformers is not yet possible by available diagnostic techniques.more » In this paper, the authors present a method of identifying the right time for preventive overhaul in 63 kV power transformers. This method is developed based on 25 year transformer performance records in Northern Iran (subtropical climate) and with the utilization of studies done by electrical engineering communities world-wide.« less

Fast traffic sign recognition with a rotation invariant binary pattern based feature.

PubMed

Yin, Shouyi; Ouyang, Peng; Liu, Leibo; Guo, Yike; Wei, Shaojun

2015-01-19

Robust and fast traffic sign recognition is very important but difficult for safe driving assistance systems. This study addresses fast and robust traffic sign recognition to enhance driving safety. The proposed method includes three stages. First, a typical Hough transformation is adopted to implement coarse-grained location of the candidate regions of traffic signs. Second, a RIBP (Rotation Invariant Binary Pattern) based feature in the affine and Gaussian space is proposed to reduce the time of traffic sign detection and achieve robust traffic sign detection in terms of scale, rotation, and illumination. Third, the techniques of ANN (Artificial Neutral Network) based feature dimension reduction and classification are designed to reduce the traffic sign recognition time. Compared with the current work, the experimental results in the public datasets show that this work achieves robustness in traffic sign recognition with comparable recognition accuracy and faster processing speed, including training speed and recognition speed.

Fast Traffic Sign Recognition with a Rotation Invariant Binary Pattern Based Feature

PubMed Central

Yin, Shouyi; Ouyang, Peng; Liu, Leibo; Guo, Yike; Wei, Shaojun

2015-01-01

Robust and fast traffic sign recognition is very important but difficult for safe driving assistance systems. This study addresses fast and robust traffic sign recognition to enhance driving safety. The proposed method includes three stages. First, a typical Hough transformation is adopted to implement coarse-grained location of the candidate regions of traffic signs. Second, a RIBP (Rotation Invariant Binary Pattern) based feature in the affine and Gaussian space is proposed to reduce the time of traffic sign detection and achieve robust traffic sign detection in terms of scale, rotation, and illumination. Third, the techniques of ANN (Artificial Neutral Network) based feature dimension reduction and classification are designed to reduce the traffic sign recognition time. Compared with the current work, the experimental results in the public datasets show that this work achieves robustness in traffic sign recognition with comparable recognition accuracy and faster processing speed, including training speed and recognition speed. PMID:25608217

Frequency conversion by the transformation-optical analogue of the cosmological redshift

NASA Astrophysics Data System (ADS)

Ginis, Vincent; Tassin, Philippe; Craps, Ben; Veretennicoff, Irina

2011-10-01

Recently, there has been a lot of interest in electromagnetic analogues of general relativistic effects. Using the techniques of transformation optics, the material parameters of table-top devices have been calculated such that they implement several effects that occur in outer space, e.g., the implementation of an artificial event horizon inside an optical fiber, an inhomogeneous refractive index profile to mimic celestial mechanics, or an omnidirectional absorber based on an equivalence with black holes. In this communication, we show how we have extended the framework of transformation optics to a time-dependent metric-the Robertson-Walker metric, a popular model for our universe describing the cosmological redshift. This redshift occurs due to the expansion of the universe, where a photon of frequency ωem emitted at instance tem, will be measured at a different frequency ωobs at time tobs. The relation between these two frequencies is given by ωobsa(tobs) = ωema(tem), where a(t) is the time-dependent scale factor of the expanding universe. Our results show that the transformation-optical analogue of the Robertson-Walker metric is a medium with linear, isotropic, and homogeneous material parameters that evolve as a given function of time. The electromagnetic solutions inside such a medium are frequency shifted according to the cosmological redshift formula. Furthermore, we have demonstrated that a finite slab of such a material allows for the frequency conversion of an optical signal without the creation of unwanted sidebands. Because the medium is linear, the superposition principle remains applicable and arbitrary wavepackets can be converted [V. Ginis, P. Tassin, B. Craps, and I. Veretennicoff Opt. Express 18, 5350-5355 (2010)1].

Transient analysis of intercalation electrodes for parameter estimation

NASA Astrophysics Data System (ADS)

Devan, Sheba

An essential part of integrating batteries as power sources in any application, be it a large scale automotive application or a small scale portable application, is an efficient Battery Management System (BMS). The combination of a battery with the microprocessor based BMS (called "smart battery") helps prolong the life of the battery by operating in the optimal regime and provides accurate information regarding the battery to the end user. The main purposes of BMS are cell protection, monitoring and control, and communication between different components. These purposes are fulfilled by tracking the change in the parameters of the intercalation electrodes in the batteries. Consequently, the functions of the BMS should be prompt, which requires the methodology of extracting the parameters to be efficient in time. The traditional transient techniques applied so far may not be suitable due to reasons such as the inability to apply these techniques when the battery is under operation, long experimental time, etc. The primary aim of this research work is to design a fast, accurate and reliable technique that can be used to extract parameter values of the intercalation electrodes. A methodology based on analysis of the short time response to a sinusoidal input perturbation, in the time domain is demonstrated using a porous electrode model for an intercalation electrode. It is shown that the parameters associated with the interfacial processes occurring in the electrode can be determined rapidly, within a few milliseconds, by measuring the response in the transient region. The short time analysis in the time domain is then extended to a single particle model that involves bulk diffusion in the solid phase in addition to interfacial processes. A systematic procedure for sequential parameter estimation using sensitivity analysis is described. Further, the short time response and the input perturbation are transformed into the frequency domain using Fast Fourier Transform (FFT) to generate impedance spectra to derive immediate qualitative information regarding the nature of the system. The short time analysis technique gives the ability to perform both time domain and frequency domain analysis using data measured within short durations.

Real-time contaminant detection and classification in a drinking water pipe using conventional water quality sensors: techniques and experimental results.

PubMed

Jeffrey Yang, Y; Haught, Roy C; Goodrich, James A

2009-06-01

Accurate detection and identification of natural or intentional contamination events in a drinking water pipe is critical to drinking water supply security and health risk management. To use conventional water quality sensors for the purpose, we have explored a real-time event adaptive detection, identification and warning (READiw) methodology and examined it using pilot-scale pipe flow experiments of 11 chemical and biological contaminants each at three concentration levels. The tested contaminants include pesticide and herbicides (aldicarb, glyphosate and dicamba), alkaloids (nicotine and colchicine), E. coli in terrific broth, biological growth media (nutrient broth, terrific broth, tryptic soy broth), and inorganic chemical compounds (mercuric chloride and potassium ferricyanide). First, through adaptive transformation of the sensor outputs, contaminant signals were enhanced and background noise was reduced in time-series plots leading to detection and identification of all simulated contamination events. The improved sensor detection threshold was 0.1% of the background for pH and oxidation-reduction potential (ORP), 0.9% for free chlorine, 1.6% for total chlorine, and 0.9% for chloride. Second, the relative changes calculated from adaptively transformed residual chlorine measurements were quantitatively related to contaminant-chlorine reactivity in drinking water. We have shown that based on these kinetic and chemical differences, the tested contaminants were distinguishable in forensic discrimination diagrams made of adaptively transformed sensor measurements.

A Lagging Model for Describing Drawdown Induced by a Constant-Rate Pumping in a Leaky Confined Aquifer

NASA Astrophysics Data System (ADS)

Lin, Ye-Chen; Yeh, Hund-Der

2017-10-01

This study proposes a generalized Darcy's law with considering phase lags in both the water flux and drawdown gradient to develop a lagging flow model for describing drawdown induced by constant-rate pumping (CRP) in a leaky confined aquifer. The present model has a mathematical formulation similar to the dual-porosity model. The Laplace-domain solution of the model with the effect of wellbore storage is derived by the Laplace transform method. The time-domain solution for the case of neglecting the wellbore storage and well radius is developed by the use of Laplace transform and Weber transform. The results of sensitivity analysis based on the solution indicate that the drawdown is very sensitive to the change in each of the transmissivity and storativity. Also, a study for the lagging effect on the drawdown indicates that its influence is significant associated with the lag times. The present solution is also employed to analyze a data set taken from a CRP test conducted in a fractured aquifer in South Dakota, USA. The results show the prediction of this new solution with considering the phase lags has very good fit to the field data, especially at early pumping time. In addition, the phase lags seem to have a scale effect as indicated in the results. In other words, the lagging behavior is positively correlated with the observed distance in the Madison aquifer.

Accurate complex scaling of three dimensional numerical potentials

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

Cerioni, Alessandro; Genovese, Luigi; Duchemin, Ivan

2013-05-28

The complex scaling method, which consists in continuing spatial coordinates into the complex plane, is a well-established method that allows to compute resonant eigenfunctions of the time-independent Schroedinger operator. Whenever it is desirable to apply the complex scaling to investigate resonances in physical systems defined on numerical discrete grids, the most direct approach relies on the application of a similarity transformation to the original, unscaled Hamiltonian. We show that such an approach can be conveniently implemented in the Daubechies wavelet basis set, featuring a very promising level of generality, high accuracy, and no need for artificial convergence parameters. Complex scalingmore » of three dimensional numerical potentials can be efficiently and accurately performed. By carrying out an illustrative resonant state computation in the case of a one-dimensional model potential, we then show that our wavelet-based approach may disclose new exciting opportunities in the field of computational non-Hermitian quantum mechanics.« less

A wireless soil moisture sensor powered by solar energy.

PubMed

Jiang, Mingliang; Lv, Mouchao; Deng, Zhong; Zhai, Guoliang

2017-01-01

In a variety of agricultural activities, such as irrigation scheduling and nutrient management, soil water content is regarded as an essential parameter. Either power supply or long-distance cable is hardly available within field scale. For the necessity of monitoring soil water dynamics at field scale, this study presents a wireless soil moisture sensor based on the impedance transform of the frequency domain. The sensor system is powered by solar energy, and the data can be instantly transmitted by wireless communication. The sensor electrodes are embedded into the bottom of a supporting rod so that the sensor can measure soil water contents at different depths. An optimal design with time executing sequence is considered to reduce the energy consumption. The experimental results showed that the sensor is a promising tool for monitoring moisture in large-scale farmland using solar power and wireless communication.

Relativistic time transfer for a Mars lander: from proper time to Areocentric Coordinate Time

NASA Astrophysics Data System (ADS)

Xu, De-Wang; Yu, Qing-Shan; Xie, Yi

2016-10-01

As the first step in relativistic time transfer for a Mars lander from its proper time to the time scale at the ground station, we investigate the transformation between proper time and Areocentric Coordinate Time (TCA) in the framework of IAU Resolutions. TCA is a local time scale for Mars, which is analogous to the Geocentric Coordinate Time (TCG) for Earth. This transformation contains two contributions: internal and external. The internal contribution comes from the gravitational potential and the rotation of Mars. The external contribution is due to the gravitational fields of other bodies (except Mars) in the Solar System. When the (in)stability of an onboard clock is assumed to be at the level of 10-13, we find that the internal contribution is dominated by the gravitational potential of spherical Mars with necessary corrections associated with the height of the lander on the areoid, the dynamic form factor of Mars, the flattening of the areoid and the spin rate of Mars. For the external contribution, we find the gravitational effects from other bodies in the Solar System can be safely neglected in this case after calculating their maximum values.

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

Faizal, Mir, E-mail: f2mir@uwaterloo.ca; Majumder, Barun, E-mail: barunbasanta@iitgn.ac.in

In this paper, we will incorporate the generalized uncertainty principle into field theories with Lifshitz scaling. We will first construct both bosonic and fermionic theories with Lifshitz scaling based on generalized uncertainty principle. After that we will incorporate the generalized uncertainty principle into a non-abelian gauge theory with Lifshitz scaling. We will observe that even though the action for this theory is non-local, it is invariant under local gauge transformations. We will also perform the stochastic quantization of this Lifshitz fermionic theory based generalized uncertainty principle.

Array magnetics modal analysis for the DIII-D tokamak based on localized time-series modelling

DOE PAGES

Olofsson, K. Erik J.; Hanson, Jeremy M.; Shiraki, Daisuke; ...

2014-07-14

Here, time-series analysis of magnetics data in tokamaks is typically done using block-based fast Fourier transform methods. This work presents the development and deployment of a new set of algorithms for magnetic probe array analysis. The method is based on an estimation technique known as stochastic subspace identification (SSI). Compared with the standard coherence approach or the direct singular value decomposition approach, the new technique exhibits several beneficial properties. For example, the SSI method does not require that frequencies are orthogonal with respect to the timeframe used in the analysis. Frequencies are obtained directly as parameters of localized time-series models.more » The parameters are extracted by solving small-scale eigenvalue problems. Applications include maximum-likelihood regularized eigenmode pattern estimation, detection of neoclassical tearing modes, including locked mode precursors, and automatic clustering of modes, and magnetics-pattern characterization of sawtooth pre- and postcursors, edge harmonic oscillations and fishbones.« less

Target matching based on multi-view tracking

NASA Astrophysics Data System (ADS)

Liu, Yahui; Zhou, Changsheng

2011-01-01

A feature matching method is proposed based on Maximally Stable Extremal Regions (MSER) and Scale Invariant Feature Transform (SIFT) to solve the problem of the same target matching in multiple cameras. Target foreground is extracted by using frame difference twice and bounding box which is regarded as target regions is calculated. Extremal regions are got by MSER. After fitted into elliptical regions, those regions will be normalized into unity circles and represented with SIFT descriptors. Initial matching is obtained from the ratio of the closest distance to second distance less than some threshold and outlier points are eliminated in terms of RANSAC. Experimental results indicate the method can reduce computational complexity effectively and is also adapt to affine transformation, rotation, scale and illumination.

Extended AIC model based on high order moments and its application in the financial market

NASA Astrophysics Data System (ADS)

Mao, Xuegeng; Shang, Pengjian

2018-07-01

In this paper, an extended method of traditional Akaike Information Criteria(AIC) is proposed to detect the volatility of time series by combining it with higher order moments, such as skewness and kurtosis. Since measures considering higher order moments are powerful in many aspects, the properties of asymmetry and flatness can be observed. Furthermore, in order to reduce the effect of noise and other incoherent features, we combine the extended AIC algorithm with multiscale wavelet analysis, in which the newly extended AIC algorithm is applied to wavelet coefficients at several scales and the time series are reconstructed by wavelet transform. After that, we create AIC planes to derive the relationship among AIC values using variance, skewness and kurtosis respectively. When we test this technique on the financial market, the aim is to analyze the trend and volatility of the closing price of stock indices and classify them. And we also adapt multiscale analysis to measure complexity of time series over a range of scales. Empirical results show that the singularity of time series in stock market can be detected via extended AIC algorithm.

A scaling theory for linear systems

NASA Technical Reports Server (NTRS)

Brockett, R. W.; Krishnaprasad, P. S.

1980-01-01

A theory of scaling for rational (transfer) functions in terms of transformation groups is developed. Two different four-parameter scaling groups which play natural roles in studying linear systems are identified and the effect of scaling on Fisher information and related statistical measures in system identification are studied. The scalings considered include change of time scale, feedback, exponential scaling, magnitude scaling, etc. The scaling action of the groups studied is tied to the geometry of transfer functions in a rather strong way as becomes apparent in the examination of the invariants of scaling. As a result, the scaling process also provides new insight into the parameterization question for rational functions.

Poisson denoising on the sphere: application to the Fermi gamma ray space telescope

NASA Astrophysics Data System (ADS)

Schmitt, J.; Starck, J. L.; Casandjian, J. M.; Fadili, J.; Grenier, I.

2010-07-01

The Large Area Telescope (LAT), the main instrument of the Fermi gamma-ray Space telescope, detects high energy gamma rays with energies from 20 MeV to more than 300 GeV. The two main scientific objectives, the study of the Milky Way diffuse background and the detection of point sources, are complicated by the lack of photons. That is why we need a powerful Poisson noise removal method on the sphere which is efficient on low count Poisson data. This paper presents a new multiscale decomposition on the sphere for data with Poisson noise, called multi-scale variance stabilizing transform on the sphere (MS-VSTS). This method is based on a variance stabilizing transform (VST), a transform which aims to stabilize a Poisson data set such that each stabilized sample has a quasi constant variance. In addition, for the VST used in the method, the transformed data are asymptotically Gaussian. MS-VSTS consists of decomposing the data into a sparse multi-scale dictionary like wavelets or curvelets, and then applying a VST on the coefficients in order to get almost Gaussian stabilized coefficients. In this work, we use the isotropic undecimated wavelet transform (IUWT) and the curvelet transform as spherical multi-scale transforms. Then, binary hypothesis testing is carried out to detect significant coefficients, and the denoised image is reconstructed with an iterative algorithm based on hybrid steepest descent (HSD). To detect point sources, we have to extract the Galactic diffuse background: an extension of the method to background separation is then proposed. In contrary, to study the Milky Way diffuse background, we remove point sources with a binary mask. The gaps have to be interpolated: an extension to inpainting is then proposed. The method, applied on simulated Fermi LAT data, proves to be adaptive, fast and easy to implement.

Spatial transformation abilities and their relation to later mathematics performance.

PubMed

Frick, Andrea

2018-04-10

Using a longitudinal approach, this study investigated the relational structure of different spatial transformation skills at kindergarten age, and how these spatial skills relate to children's later mathematics performance. Children were tested at three time points, in kindergarten, first grade, and second grade (N = 119). Exploratory factor analyses revealed two subcomponents of spatial transformation skills: one representing egocentric transformations (mental rotation and spatial scaling), and one representing allocentric transformations (e.g., cross-sectioning, perspective taking). Structural equation modeling suggested that egocentric transformation skills showed their strongest relation to the part of the mathematics test tapping arithmetic operations, whereas allocentric transformations were strongly related to Numeric-Logical and Spatial Functions as well as geometry. The present findings point to a tight connection between early mental transformation skills, particularly the ones requiring a high level of spatial flexibility and a strong sense for spatial magnitudes, and children's mathematics performance at the beginning of their school career.

Time-frequency techniques in biomedical signal analysis. a tutorial review of similarities and differences.

PubMed

Wacker, M; Witte, H

2013-01-01

This review outlines the methodological fundamentals of the most frequently used non-parametric time-frequency analysis techniques in biomedicine and their main properties, as well as providing decision aids concerning their applications. The short-term Fourier transform (STFT), the Gabor transform (GT), the S-transform (ST), the continuous Morlet wavelet transform (CMWT), and the Hilbert transform (HT) are introduced as linear transforms by using a unified concept of the time-frequency representation which is based on a standardized analytic signal. The Wigner-Ville distribution (WVD) serves as an example of the 'quadratic transforms' class. The combination of WVD and GT with the matching pursuit (MP) decomposition and that of the HT with the empirical mode decomposition (EMD) are explained; these belong to the class of signal-adaptive approaches. Similarities between linear transforms are demonstrated and differences with regard to the time-frequency resolution and interference (cross) terms are presented in detail. By means of simulated signals the effects of different time-frequency resolutions of the GT, CMWT, and WVD as well as the resolution-related properties of the interference (cross) terms are shown. The method-inherent drawbacks and their consequences for the application of the time-frequency techniques are demonstrated by instantaneous amplitude, frequency and phase measures and related time-frequency representations (spectrogram, scalogram, time-frequency distribution, phase-locking maps) of measured magnetoencephalographic (MEG) signals. The appropriate selection of a method and its parameter settings will ensure readability of the time-frequency representations and reliability of results. When the time-frequency characteristics of a signal strongly correspond with the time-frequency resolution of the analysis then a method may be considered 'optimal'. The MP-based signal-adaptive approaches are preferred as these provide an appropriate time-frequency resolution for all frequencies while simultaneously reducing interference (cross) terms.

3-D surface profilometry based on modulation measurement by applying wavelet transform method

NASA Astrophysics Data System (ADS)

Zhong, Min; Chen, Feng; Xiao, Chao; Wei, Yongchao

2017-01-01

A new analysis of 3-D surface profilometry based on modulation measurement technique by the application of Wavelet Transform method is proposed. As a tool excelling for its multi-resolution and localization in the time and frequency domains, Wavelet Transform method with good localized time-frequency analysis ability and effective de-noizing capacity can extract the modulation distribution more accurately than Fourier Transform method. Especially for the analysis of complex object, more details of the measured object can be well remained. In this paper, the theoretical derivation of Wavelet Transform method that obtains the modulation values from a captured fringe pattern is given. Both computer simulation and elementary experiment are used to show the validity of the proposed method by making a comparison with the results of Fourier Transform method. The results show that the Wavelet Transform method has a better performance than the Fourier Transform method in modulation values retrieval.

SNR enhancement for downhole microseismic data based on scale classification shearlet transform

NASA Astrophysics Data System (ADS)

Li, Juan; Ji, Shuo; Li, Yue; Qian, Zhihong; Lu, Weili

2018-06-01

Shearlet transform (ST) can be effective in 2D signal processing, due to its parabolic scaling, high directional sensitivity, and optimal sparsity. ST combined with thresholding has been successfully applied to suppress random noise. However, because of the low magnitude and high frequency of a downhole microseismic signal, the coefficient values of valid signals and noise are similar in the shearlet domain. As a result, it is difficult to use for denoising. In this paper, we present a scale classification ST to solve this problem. The ST is used to decompose noisy microseismic data into serval scales. By analyzing the spectrum and energy distribution of the shearlet coefficients of microseismic data, we divide the scales into two types: low-frequency scales which contain less useful signal and high-frequency scales which contain more useful signal. After classification, we use two different methods to deal with the coefficients on different scales. For the low-frequency scales, the noise is attenuated using a thresholding method. As for the high-frequency scales, we propose to use a generalized Gauss distribution model based a non-local means filter, which takes advantage of the temporal and spatial similarity of microseismic data. The experimental results on both synthetic records and field data illustrate that our proposed method preserves the useful components and attenuates the noise well.

New fast DCT algorithms based on Loeffler's factorization

NASA Astrophysics Data System (ADS)

Hong, Yoon Mi; Kim, Il-Koo; Lee, Tammy; Cheon, Min-Su; Alshina, Elena; Han, Woo-Jin; Park, Jeong-Hoon

2012-10-01

This paper proposes a new 32-point fast discrete cosine transform (DCT) algorithm based on the Loeffler's 16-point transform. Fast integer realizations of 16-point and 32-point transforms are also provided based on the proposed transform. For the recent development of High Efficiency Video Coding (HEVC), simplified quanti-zation and de-quantization process are proposed. Three different forms of implementation with the essentially same performance, namely matrix multiplication, partial butterfly, and full factorization can be chosen accord-ing to the given platform. In terms of the number of multiplications required for the realization, our proposed full-factorization is 3~4 times faster than a partial butterfly, and about 10 times faster than direct matrix multiplication.

Watermarking scheme based on singular value decomposition and homomorphic transform

NASA Astrophysics Data System (ADS)

Verma, Deval; Aggarwal, A. K.; Agarwal, Himanshu

2017-10-01

A semi-blind watermarking scheme based on singular-value-decomposition (SVD) and homomorphic transform is pro-posed. This scheme ensures the digital security of an eight bit gray scale image by inserting an invisible eight bit gray scale wa-termark into it. The key approach of the scheme is to apply the homomorphic transform on the host image to obtain its reflectance component. The watermark is embedded into the singular values that are obtained by applying the singular value decomposition on the reflectance component. Peak-signal-to-noise-ratio (PSNR), normalized-correlation-coefficient (NCC) and mean-structural-similarity-index-measure (MSSIM) are used to evaluate the performance of the scheme. Invisibility of watermark is ensured by visual inspection and high value of PSNR of watermarked images. Presence of watermark is ensured by visual inspection and high values of NCC and MSSIM of extracted watermarks. Robustness of the scheme is verified by high values of NCC and MSSIM for attacked watermarked images.

Inferring infection hazard in wildlife populations by linking data across individual and population scales.

PubMed

Pepin, Kim M; Kay, Shannon L; Golas, Ben D; Shriner, Susan S; Gilbert, Amy T; Miller, Ryan S; Graham, Andrea L; Riley, Steven; Cross, Paul C; Samuel, Michael D; Hooten, Mevin B; Hoeting, Jennifer A; Lloyd-Smith, James O; Webb, Colleen T; Buhnerkempe, Michael G

2017-03-01

Our ability to infer unobservable disease-dynamic processes such as force of infection (infection hazard for susceptible hosts) has transformed our understanding of disease transmission mechanisms and capacity to predict disease dynamics. Conventional methods for inferring FOI estimate a time-averaged value and are based on population-level processes. Because many pathogens exhibit epidemic cycling and FOI is the result of processes acting across the scales of individuals and populations, a flexible framework that extends to epidemic dynamics and links within-host processes to FOI is needed. Specifically, within-host antibody kinetics in wildlife hosts can be short-lived and produce patterns that are repeatable across individuals, suggesting individual-level antibody concentrations could be used to infer time since infection and hence FOI. Using simulations and case studies (influenza A in lesser snow geese and Yersinia pestis in coyotes), we argue that with careful experimental and surveillance design, the population-level FOI signal can be recovered from individual-level antibody kinetics, despite substantial individual-level variation. In addition to improving inference, the cross-scale quantitative antibody approach we describe can reveal insights into drivers of individual-based variation in disease response, and the role of poorly understood processes such as secondary infections, in population-level dynamics of disease. © 2017 John Wiley & Sons Ltd/CNRS.

Inferring infection hazard in wildlife populations by linking data across individual and population scales

USGS Publications Warehouse

Pepin, Kim M.; Kay, Shannon L.; Golas, Ben D.; Shriner, Susan A.; Gilbert, Amy T.; Miller, Ryan S.; Graham, Andrea L.; Riley, Steven; Cross, Paul C.; Samuel, Michael D.; Hooten, Mevin B.; Hoeting, Jennifer A.; Lloyd-Smith, James O.; Webb, Colleen T.; Buhnerkempe, Michael G.

2017-01-01

Our ability to infer unobservable disease-dynamic processes such as force of infection (infection hazard for susceptible hosts) has transformed our understanding of disease transmission mechanisms and capacity to predict disease dynamics. Conventional methods for inferring FOI estimate a time-averaged value and are based on population-level processes. Because many pathogens exhibit epidemic cycling and FOI is the result of processes acting across the scales of individuals and populations, a flexible framework that extends to epidemic dynamics and links within-host processes to FOI is needed. Specifically, within-host antibody kinetics in wildlife hosts can be short-lived and produce patterns that are repeatable across individuals, suggesting individual-level antibody concentrations could be used to infer time since infection and hence FOI. Using simulations and case studies (influenza A in lesser snow geese and Yersinia pestis in coyotes), we argue that with careful experimental and surveillance design, the population-level FOI signal can be recovered from individual-level antibody kinetics, despite substantial individual-level variation. In addition to improving inference, the cross-scale quantitative antibody approach we describe can reveal insights into drivers of individual-based variation in disease response, and the role of poorly understood processes such as secondary infections, in population-level dynamics of disease.

Defining Simple nD Operations Based on Prosmatic nD Objects

NASA Astrophysics Data System (ADS)

Arroyo Ohori, K.; Ledoux, H.; Stoter, J.

2016-10-01

An alternative to the traditional approaches to model separately 2D/3D space, time, scale and other parametrisable characteristics in GIS lies in the higher-dimensional modelling of geographic information, in which a chosen set of non-spatial characteristics, e.g. time and scale, are modelled as extra geometric dimensions perpendicular to the spatial ones, thus creating a higher-dimensional model. While higher-dimensional models are undoubtedly powerful, they are also hard to create and manipulate due to our lack of an intuitive understanding in dimensions higher than three. As a solution to this problem, this paper proposes a methodology that makes nD object generation easier by splitting the creation and manipulation process into three steps: (i) constructing simple nD objects based on nD prismatic polytopes - analogous to prisms in 3D -, (ii) defining simple modification operations at the vertex level, and (iii) simple postprocessing to fix errors introduced in the model. As a use case, we show how two sets of operations can be defined and implemented in a dimension-independent manner using this methodology: the most common transformations (i.e. translation, scaling and rotation) and the collapse of objects. The nD objects generated in this manner can then be used as a basis for an nD GIS.

Research about vibration characteristics of timing chain system based on short-time Fourier transform

NASA Astrophysics Data System (ADS)

Xi, Jiaxin; Liu, Ning

2017-09-01

Vibration characteristic of timing chain system is very important for an engine. In this study, we used a bush roller chain drive system as an example to explain how to use mulitybody dynamic techniques and short-time Fourier transform to investigate vibration characteristics of timing chain system. Multibody dynamic simulation data as chain tension force and external excitation sources curves were provided for short-time Fourier transform study. The study results of short-time Fourier transform illustrate that there are two main vibration frequency domain of timing chain system, one is the low frequency vibration caused by crankshaft sprocket velocity and camshaft sprocket torque. Another is vibration around 1000Hz lead by hydraulic tensioner. Hence, short-time Fourier transform method is useful for basic research of vibration characteristics for timing chain system.

A result about scale transformation families in approximation

NASA Astrophysics Data System (ADS)

Apprato, Dominique; Gout, Christian

2000-06-01

Scale transformations are common in approximation. In surface approximation from rapidly varying data, one wants to suppress, or at least dampen the oscillations of the approximation near steep gradients implied by the data. In that case, scale transformations can be used to give some control over overshoot when the surface has large variations of its gradient. Conversely, in image analysis, scale transformations are used in preprocessing to enhance some features present on the image or to increase jumps of grey levels before segmentation of the image. In this paper, we establish the convergence of an approximation method which allows some control over the behavior of the approximation. More precisely, we study the convergence of an approximation from a data set of , while using scale transformations on the values before and after classical approximation. In addition, the construction of scale transformations is also given. The algorithm is presented with some numerical examples.

3D reconstruction based on light field images

NASA Astrophysics Data System (ADS)

Zhu, Dong; Wu, Chunhong; Liu, Yunluo; Fu, Dongmei

2018-04-01

This paper proposed a method of reconstructing three-dimensional (3D) scene from two light field images capture by Lytro illium. The work was carried out by first extracting the sub-aperture images from light field images and using the scale-invariant feature transform (SIFT) for feature registration on the selected sub-aperture images. Structure from motion (SFM) algorithm is further used on the registration completed sub-aperture images to reconstruct the three-dimensional scene. 3D sparse point cloud was obtained in the end. The method shows that the 3D reconstruction can be implemented by only two light field camera captures, rather than at least a dozen times captures by traditional cameras. This can effectively solve the time-consuming, laborious issues for 3D reconstruction based on traditional digital cameras, to achieve a more rapid, convenient and accurate reconstruction.

Multiscale image contrast amplification (MUSICA)

NASA Astrophysics Data System (ADS)

Vuylsteke, Pieter; Schoeters, Emile P.

1994-05-01

This article presents a novel approach to the problem of detail contrast enhancement, based on multiresolution representation of the original image. The image is decomposed into a weighted sum of smooth, localized, 2D basis functions at multiple scales. Each transform coefficient represents the amount of local detail at some specific scale and at a specific position in the image. Detail contrast is enhanced by non-linear amplification of the transform coefficients. An inverse transform is then applied to the modified coefficients. This yields a uniformly contrast- enhanced image without artefacts. The MUSICA-algorithm is being applied routinely to computed radiography images of chest, skull, spine, shoulder, pelvis, extremities, and abdomen examinations, with excellent acceptance. It is useful for a wide range of applications in the medical, graphical, and industrial area.

Time-Variable Transit Time Distributions in the Hyporheic Zone of a Headwater Mountain Stream

NASA Astrophysics Data System (ADS)

Ward, Adam S.; Schmadel, Noah M.; Wondzell, Steven M.

2018-03-01

Exchange of water between streams and their hyporheic zones is known to be dynamic in response to hydrologic forcing, variable in space, and to exist in a framework with nested flow cells. The expected result of heterogeneous geomorphic setting, hydrologic forcing, and between-feature interaction is hyporheic transit times that are highly variable in both space and time. Transit time distributions (TTDs) are important as they reflect the potential for hyporheic processes to impact biogeochemical transformations and ecosystems. In this study we simulate time-variable transit time distributions based on dynamic vertical exchange in a headwater mountain stream with observed, heterogeneous step-pool morphology. Our simulations include hyporheic exchange over a 600 m river corridor reach driven by continuously observed, time-variable hydrologic conditions for more than 1 year. We found that spatial variability at an instance in time is typically larger than temporal variation for the reach. Furthermore, we found reach-scale TTDs were marginally variable under all but the most extreme hydrologic conditions, indicating that TTDs are highly transferable in time. Finally, we found that aggregation of annual variation in space and time into a "master TTD" reasonably represents most of the hydrologic dynamics simulated, suggesting that this aggregation approach may provide a relevant basis for scaling from features or short reaches to entire networks.

Time-oriented hierarchical method for computation of principal components using subspace learning algorithm.

PubMed

Jankovic, Marko; Ogawa, Hidemitsu

2004-10-01

Principal Component Analysis (PCA) and Principal Subspace Analysis (PSA) are classic techniques in statistical data analysis, feature extraction and data compression. Given a set of multivariate measurements, PCA and PSA provide a smaller set of "basis vectors" with less redundancy, and a subspace spanned by them, respectively. Artificial neurons and neural networks have been shown to perform PSA and PCA when gradient ascent (descent) learning rules are used, which is related to the constrained maximization (minimization) of statistical objective functions. Due to their low complexity, such algorithms and their implementation in neural networks are potentially useful in cases of tracking slow changes of correlations in the input data or in updating eigenvectors with new samples. In this paper we propose PCA learning algorithm that is fully homogeneous with respect to neurons. The algorithm is obtained by modification of one of the most famous PSA learning algorithms--Subspace Learning Algorithm (SLA). Modification of the algorithm is based on Time-Oriented Hierarchical Method (TOHM). The method uses two distinct time scales. On a faster time scale PSA algorithm is responsible for the "behavior" of all output neurons. On a slower scale, output neurons will compete for fulfillment of their "own interests". On this scale, basis vectors in the principal subspace are rotated toward the principal eigenvectors. At the end of the paper it will be briefly analyzed how (or why) time-oriented hierarchical method can be used for transformation of any of the existing neural network PSA method, into PCA method.

Computer-Aided Diagnosis System for Alzheimer's Disease Using Different Discrete Transform Techniques.

PubMed

Dessouky, Mohamed M; Elrashidy, Mohamed A; Taha, Taha E; Abdelkader, Hatem M

2016-05-01

The different discrete transform techniques such as discrete cosine transform (DCT), discrete sine transform (DST), discrete wavelet transform (DWT), and mel-scale frequency cepstral coefficients (MFCCs) are powerful feature extraction techniques. This article presents a proposed computer-aided diagnosis (CAD) system for extracting the most effective and significant features of Alzheimer's disease (AD) using these different discrete transform techniques and MFCC techniques. Linear support vector machine has been used as a classifier in this article. Experimental results conclude that the proposed CAD system using MFCC technique for AD recognition has a great improvement for the system performance with small number of significant extracted features, as compared with the CAD system based on DCT, DST, DWT, and the hybrid combination methods of the different transform techniques. © The Author(s) 2015.

Enzymes in removal of pharmaceuticals from wastewater: A critical review of challenges, applications and screening methods for their selection.

PubMed

Stadlmair, Lara F; Letzel, Thomas; Drewes, Jörg E; Grassmann, Johanna

2018-08-01

At present, the removal of trace organic chemicals such as pharmaceuticals in wastewater treatment plants is often incomplete resulting in a continuous discharge into the aqueous environment. To overcome this issue, bioremediation approaches gained significant importance in recent times, since they might have a lower carbon footprint than chemical or physical treatment methods. In this context, enzyme-based technologies represent a promising alternative since they are able to specifically target certain chemicals. For this purpose, versatile monitoring of enzymatic reactions is of great importance in order to understand underlying transformation mechanisms and estimate the suitability of various enzymes exhibiting different specificities for bioremediation purposes. This study provides a comprehensive review, summarizing research on enzymatic transformation of pharmaceuticals in water treatment applications using traditional and state-of-the-art enzyme screening approaches with a special focus on mass spectrometry (MS)-based and high-throughput tools. MS-based enzyme screening represents an approach that allows a comprehensive mechanistic understanding of enzymatic reactions and, in particular, the identification of transformation products. A critical discussion of these approaches for implementation in wastewater treatment processes is also presented. So far, there are still major gaps between laboratory- and field-scale research that need to be overcome in order to assess the viability for real applications. Copyright © 2018 Elsevier Ltd. All rights reserved.

Exploration of EEG features of Alzheimer's disease using continuous wavelet transform.

PubMed

Ghorbanian, Parham; Devilbiss, David M; Hess, Terry; Bernstein, Allan; Simon, Adam J; Ashrafiuon, Hashem

2015-09-01

We have developed a novel approach to elucidate several discriminating EEG features of Alzheimer's disease. The approach is based on the use of a variety of continuous wavelet transforms, pairwise statistical tests with multiple comparison correction, and several decision tree algorithms, in order to choose the most prominent EEG features from a single sensor. A pilot study was conducted to record EEG signals from Alzheimer's disease (AD) patients and healthy age-matched control (CTL) subjects using a single dry electrode device during several eyes-closed (EC) and eyes-open (EO) resting conditions. We computed the power spectrum distribution properties and wavelet and sample entropy of the wavelet coefficients time series at scale ranges approximately corresponding to the major brain frequency bands. A predictive index was developed using the results from statistical tests and decision tree algorithms to identify the most reliable significant features of the AD patients when compared to healthy controls. The three most dominant features were identified as larger absolute mean power and larger standard deviation of the wavelet scales corresponding to 4-8 Hz (θ) during EO and lower wavelet entropy of the wavelet scales corresponding to 8-12 Hz (α) during EC, respectively. The fourth reliable set of distinguishing features of AD patients was lower relative power of the wavelet scales corresponding to 12-30 Hz (β) followed by lower skewness of the wavelet scales corresponding to 2-4 Hz (upper δ), both during EO. In general, the results indicate slowing and lower complexity of EEG signal in AD patients using a very easy-to-use and convenient single dry electrode device.

Parameter estimation in 3D affine and similarity transformation: implementation of variance component estimation

NASA Astrophysics Data System (ADS)

Amiri-Simkooei, A. R.

2018-01-01

Three-dimensional (3D) coordinate transformations, generally consisting of origin shifts, axes rotations, scale changes, and skew parameters, are widely used in many geomatics applications. Although in some geodetic applications simplified transformation models are used based on the assumption of small transformation parameters, in other fields of applications such parameters are indeed large. The algorithms of two recent papers on the weighted total least-squares (WTLS) problem are used for the 3D coordinate transformation. The methodology can be applied to the case when the transformation parameters are generally large of which no approximate values of the parameters are required. Direct linearization of the rotation and scale parameters is thus not required. The WTLS formulation is employed to take into consideration errors in both the start and target systems on the estimation of the transformation parameters. Two of the well-known 3D transformation methods, namely affine (12, 9, and 8 parameters) and similarity (7 and 6 parameters) transformations, can be handled using the WTLS theory subject to hard constraints. Because the method can be formulated by the standard least-squares theory with constraints, the covariance matrix of the transformation parameters can directly be provided. The above characteristics of the 3D coordinate transformation are implemented in the presence of different variance components, which are estimated using the least squares variance component estimation. In particular, the estimability of the variance components is investigated. The efficacy of the proposed formulation is verified on two real data sets.

Use of Fourier transforms for asynoptic mapping: Applications to the Upper Atmosphere Research Satellite microwave limb sounder

NASA Technical Reports Server (NTRS)

Elson, Lee S.; Froidevaux, Lucien

1993-01-01

Fourier analysis has been applied to data obtained from limb viewing instruments on the Upper Atmosphere Research Satellite. A coordinate system rotation facilitates the efficient computation of Fourier transforms in the temporal and longitudinal domains. Fields such as ozone (O3), chlorine monoxide (ClO), temperature, and water vapor have been transformed by this process. The transforms have been inverted to provide maps of these quantities at selected times, providing a method of accurate time interpolation. Maps obtained by this process show evidence of both horizontal and vertical transport of important trace species such as O3 and ClO. An examination of the polar regions indicates that large-scale planetary variations are likely to play a significant role in transporting midstratospheric O3 into the polar regions. There is also evidence that downward transport occurs, providing a means of moving O3 into the polar vortex at lower altitudes. The transforms themselves show the structure and propagation characteristics of wave variations.

Supersampling and Network Reconstruction of Urban Mobility.

PubMed

Sagarra, Oleguer; Szell, Michael; Santi, Paolo; Díaz-Guilera, Albert; Ratti, Carlo

2015-01-01

Understanding human mobility is of vital importance for urban planning, epidemiology, and many other fields that draw policies from the activities of humans in space. Despite the recent availability of large-scale data sets of GPS traces or mobile phone records capturing human mobility, typically only a subsample of the population of interest is represented, giving a possibly incomplete picture of the entire system under study. Methods to reliably extract mobility information from such reduced data and to assess their sampling biases are lacking. To that end, we analyzed a data set of millions of taxi movements in New York City. We first show that, once they are appropriately transformed, mobility patterns are highly stable over long time scales. Based on this observation, we develop a supersampling methodology to reliably extrapolate mobility records from a reduced sample based on an entropy maximization procedure, and we propose a number of network-based metrics to assess the accuracy of the predicted vehicle flows. Our approach provides a well founded way to exploit temporal patterns to save effort in recording mobility data, and opens the possibility to scale up data from limited records when information on the full system is required.

Unscented Kalman filter assimilation of time-lapse self-potential data for monitoring solute transport

NASA Astrophysics Data System (ADS)

Cui, Yi-an; Liu, Lanbo; Zhu, Xiaoxiong

2017-08-01

Monitoring the extent and evolution of contaminant plumes in local and regional groundwater systems from existing landfills is critical in contamination control and remediation. The self-potential survey is an efficient and economical nondestructive geophysical technique that can be used to investigate underground contaminant plumes. Based on the unscented transform, we have built a Kalman filtering cycle to conduct time-lapse data assimilation for monitoring the transport of solute based on the solute transport experiment using a bench-scale physical model. The data assimilation was formed by modeling the evolution based on the random walk model and observation correcting based on the self-potential forward. Thus, monitoring self-potential data can be inverted by the data assimilation technique. As a result, we can reconstruct the dynamic process of the contaminant plume instead of using traditional frame-to-frame static inversion, which may cause inversion artifacts. The data assimilation inversion algorithm was evaluated through noise-added synthetic time-lapse self-potential data. The result of the numerical experiment shows validity, accuracy and tolerance to the noise of the dynamic inversion. To validate the proposed algorithm, we conducted a scaled-down sandbox self-potential observation experiment to generate time-lapse data that closely mimics the real-world contaminant monitoring setup. The results of physical experiments support the idea that the data assimilation method is a potentially useful approach for characterizing the transport of contamination plumes using the unscented Kalman filter (UKF) data assimilation technique applied to field time-lapse self-potential data.

The Impact of Seed Layer Structure on the Recrystallization of ECD Cu and its Alloys

NASA Astrophysics Data System (ADS)

O'Brien, Brendan B.

Despite the significant improvements originally offered by the use of Cu over Al as the interconnect material for semiconductor devices, the continued down-scaling of interconnects has presented significant challenges for semiconductor engineers. As the metal line widths shrink, both the conductivity and reliability of lines decrease due to a stubbornly fine-grained microstructure in narrow lines. Understanding microstructural transformation of the ECD Cu in narrow features which leads to this polygranular microstructure is the first focus of this dissertation. As in the case of Cu films, the underlying seed layer strongly influences progress of transformation. Unlike films, however, the seed layer is not homogenous in patterned substrates, but differs according to the size of the trench and the location within the trench (field, bottom, and sidewall). Based on these findings, and the known influence of texture on the transformation of ECD Cu, a rapid trench initiated transformation process was posited for narrow interconnect lines. Time-resolved TEM observation of the ECD Cu in 48 nm lines during the transformation process confirmed the hypothesis. In fact, the TEM images revealed that the transformation was even faster than anticipated, and that the microstructure of the Cu inside the lines was stagnant after a mere 1.5 hours at room temperature. Studies of the transformation at elevated temperatures found that, despite anneals at 250°C for up to an hour, the grain size distribution for the Cu in narrow lines for all times converged, whether annealed at room temperature or 250°C. These data suggest that process was being driven by the 'consumable' internal energy stored in the as-plated microstructure. This is different than the transformation of the overburden, which is driven by a competition between surface energy and internal stress buildup due to film densification and relief due to the secondary growth of a 200 texture component. Based on these findings, two methods for manipulating the microstructure of the ECD Cu in the narrow lines were explored, including changes to the seed layer through ion implantation, and altering the as-plated Cu microstructure through co-ECD of alloys. The influence on the microstructure and applicability of both of these techniques to BEOL processing will also be discussed.

The allometry of coarse root biomass: log-transformed linear regression or nonlinear regression?

PubMed

Lai, Jiangshan; Yang, Bo; Lin, Dunmei; Kerkhoff, Andrew J; Ma, Keping

2013-01-01

Precise estimation of root biomass is important for understanding carbon stocks and dynamics in forests. Traditionally, biomass estimates are based on allometric scaling relationships between stem diameter and coarse root biomass calculated using linear regression (LR) on log-transformed data. Recently, it has been suggested that nonlinear regression (NLR) is a preferable fitting method for scaling relationships. But while this claim has been contested on both theoretical and empirical grounds, and statistical methods have been developed to aid in choosing between the two methods in particular cases, few studies have examined the ramifications of erroneously applying NLR. Here, we use direct measurements of 159 trees belonging to three locally dominant species in east China to compare the LR and NLR models of diameter-root biomass allometry. We then contrast model predictions by estimating stand coarse root biomass based on census data from the nearby 24-ha Gutianshan forest plot and by testing the ability of the models to predict known root biomass values measured on multiple tropical species at the Pasoh Forest Reserve in Malaysia. Based on likelihood estimates for model error distributions, as well as the accuracy of extrapolative predictions, we find that LR on log-transformed data is superior to NLR for fitting diameter-root biomass scaling models. More importantly, inappropriately using NLR leads to grossly inaccurate stand biomass estimates, especially for stands dominated by smaller trees.

Europe's Preparation For GOCE Gravity Field Recovery

NASA Astrophysics Data System (ADS)

Suenkel, H.; Suenkel, H.

2001-12-01

The European Space Agency ESA is preparing for its first dedicated gravity field mission GOCE (Gravity Field and Steady-state Ocean Circulation Explorer) with a proposed launch in fall 2005. The mission's goal is the mapping of the Earth's static gravity field with very high resolution and utmost accuracy on a global scale. GOCE is a drag-free mission, flown in a circular and sun-synchronous orbit at an altitude between 240 and 250 km. Each of the two operational phases will last for 6 months. GOCE is based on a sensor fusion concept combining high-low satellite-to-satellite tracking (SST) and satellite gravity gradiometry (SGG). The transformation of the GOCE sensor data into a scientific product of utmost quality and reliability requires a well-coordinated effort of experts in satellite geodesy, applied mathematics and computer science. Several research groups in Europe do have this expertise and decided to form the "European GOCE Gravity Consortium (EGG-C)". The EGG-C activities are subdivided into tasks such as standard and product definition, data base and data dissemination, precise orbit determination, global gravity field model solutions and regional solutions, solution validation, communication and documentation, and the interfacing to level 3 product scientific users. The central issue of GOCE data processing is, of course, the determination of the global gravity field model using three independent mathematical-numerical techniques which had been designed and pre-developed in the course of several scientific preparatory studies of ESA: 1. The direct solution which is a least squares adjustment technique based on a pre-conditioned conjugated gradient method (PCGM). The method is capable of efficiently transforming the calibrated and validated SST and SGG observations directly or via lumped coefficients into harmonic coefficients of the gravitational potential. 2. The time-wise approach considers both SST and SGG data as a time series. For an idealized repeat mission such a time series can be very efficiently transformed into lumped coefficients using fast Fourier techniques. For a realistic mission scenario this transformation has to be extended by an iteration process. 3. The space-wise approach which, after having transformed the original observations onto a spatial geographical grid, transforms the pseudo-observations into harmonic coefficients using a fast collocation technique. A successful mission presupposed, GOCE will finally deliver the Earth's gravity field with a resolution of about 70 km half wavelength and a global geoid with an accuracy of about 1 cm.

Metal-induced rapid transformation of diamond into single and multilayer graphene on wafer scale

DOE PAGES

Berman, Diana; Deshmukh, Sanket; Narayanan, Badri; ...

2016-07-04

The degradation of intrinsic properties of graphene during the transfer process constitutes a major challenge in graphene device fabrication, stimulating the need for direct growth of graphene on dielectric substrates. Previous attempts of metal-induced transformation of diamond and silicon carbide into graphene suffers from metal contamination and inability to scale graphene growth over large area. Here in this article, we introduce a direct approach to transform polycrystalline diamond into high-quality graphene layers on wafer scale (4 inch in diameter) using a rapid thermal annealing process facilitated by a nickel, Ni thin film catalyst on top. We show that the processmore » can be tuned to grow single or multilayer graphene with good electronic properties. Molecular dynamics simulations elucidate the mechanism of graphene growth on polycrystalline diamond. Additionally, we demonstrate the lateral growth of free-standing graphene over micron-sized pre-fabricated holes, opening exciting opportunities for future graphene/diamond-based electronics.« less

Metal-induced rapid transformation of diamond into single and multilayer graphene on wafer scale

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

Berman, Diana; Deshmukh, Sanket; Narayanan, Badri

The degradation of intrinsic properties of graphene during the transfer process constitutes a major challenge in graphene device fabrication, stimulating the need for direct growth of graphene on dielectric substrates. Previous attempts of metal-induced transformation of diamond and silicon carbide into graphene suffers from metal contamination and inability to scale graphene growth over large area. Here in this article, we introduce a direct approach to transform polycrystalline diamond into high-quality graphene layers on wafer scale (4 inch in diameter) using a rapid thermal annealing process facilitated by a nickel, Ni thin film catalyst on top. We show that the processmore » can be tuned to grow single or multilayer graphene with good electronic properties. Molecular dynamics simulations elucidate the mechanism of graphene growth on polycrystalline diamond. Additionally, we demonstrate the lateral growth of free-standing graphene over micron-sized pre-fabricated holes, opening exciting opportunities for future graphene/diamond-based electronics.« less

Direct Transformation of Amorphous Silicon Carbide into Graphene under Low Temperature and Ambient Pressure

PubMed Central

Peng, Tao; Lv, Haifeng; He, Daping; Pan, Mu; Mu, Shichun

2013-01-01

A large-scale availability of the graphene is critical to the successful application of graphene-based electronic devices. The growth of epitaxial graphene (EG) on insulating silicon carbide (SiC) surfaces has opened a new promising route for large-scale high-quality graphene production. However, two key obstacles to epitaxial growth are extremely high requirements for almost perfectly ordered crystal SiC and harsh process conditions. Here, we report that the amorphous SiC (a-Si1−xCx) nano-shell (nano-film) can be directly transformed into graphene by using chlorination method under very mild reaction conditions of relative low temperature (800°C) and the ambient pressure in chlorine (Cl2) atmosphere. Therefore, our finding, the direct transformation of a-Si1−xCx into graphene under much milder condition, will open a door to apply this new method to the large-scale production of graphene at low costs. PMID:23359349

A Robust Linear Feature-Based Procedure for Automated Registration of Point Clouds

PubMed Central

Poreba, Martyna; Goulette, François

2015-01-01

With the variety of measurement techniques available on the market today, fusing multi-source complementary information into one dataset is a matter of great interest. Target-based, point-based and feature-based methods are some of the approaches used to place data in a common reference frame by estimating its corresponding transformation parameters. This paper proposes a new linear feature-based method to perform accurate registration of point clouds, either in 2D or 3D. A two-step fast algorithm called Robust Line Matching and Registration (RLMR), which combines coarse and fine registration, was developed. The initial estimate is found from a triplet of conjugate line pairs, selected by a RANSAC algorithm. Then, this transformation is refined using an iterative optimization algorithm. Conjugates of linear features are identified with respect to a similarity metric representing a line-to-line distance. The efficiency and robustness to noise of the proposed method are evaluated and discussed. The algorithm is valid and ensures valuable results when pre-aligned point clouds with the same scale are used. The studies show that the matching accuracy is at least 99.5%. The transformation parameters are also estimated correctly. The error in rotation is better than 2.8% full scale, while the translation error is less than 12.7%. PMID:25594589

Transient Point Infiltration In The Unsaturated Zone

NASA Astrophysics Data System (ADS)

Buecker-Gittel, M.; Mohrlok, U.

The risk assessment of leaking sewer pipes gets more and more important due to urban groundwater management and environmental as well as health safety. This requires the quantification and balancing of transport and transformation processes based on the water flow in the unsaturated zone. The water flow from a single sewer leakage could be described as a point infiltration with time varying hydraulic conditions externally and internally. External variations are caused by the discharge in the sewer pipe as well as the state of the leakage itself. Internal variations are the results of microbiological clogging effects associated with the transformation processes. Technical as well as small scale laboratory experiments were conducted in order to investigate the water transport from an transient point infiltration. From the technical scale experiment there was evidence that the water flow takes place under transient conditions when sewage infiltrates into an unsaturated soil. Whereas the small scale experiments investigated the hydraulics of the water transport and the associated so- lute and particle transport in unsaturated soils in detail. The small scale experiment was a two-dimensional representation of such a point infiltration source where the distributed water transport could be measured by several tensiometers in the soil as well as by a selective measurement of the discharge at the bottom of the experimental setup. Several series of experiments were conducted varying the boundary and initial con- ditions in order to derive the important parameters controlling the infiltration of pure water from the point source. The results showed that there is a significant difference between the infiltration rate in the point source and the discharge rate at the bottom, that could be explained by storage processes due to an outflow resistance at the bottom. This effect is overlayn by a decreasing water content decreases over time correlated with a decreasing infiltration rate. As expected the initial conditions mainly affects the time scale for the water transport. Additionally, the influence of preferential flow paths on the discharge distribution could be found due to the heterogenieties caused by the filling and compaction process of the sandy soil.

Real-time modeling of primitive environments through wavelet sensors and Hebbian learning

NASA Astrophysics Data System (ADS)

Vaccaro, James M.; Yaworsky, Paul S.

1999-06-01

Modeling the world through sensory input necessarily provides a unique perspective for the observer. Given a limited perspective, objects and events cannot always be encoded precisely but must involve crude, quick approximations to deal with sensory information in a real- time manner. As an example, when avoiding an oncoming car, a pedestrian needs to identify the fact that a car is approaching before ascertaining the model or color of the vehicle. In our methodology, we use wavelet-based sensors with self-organized learning to encode basic sensory information in real-time. The wavelet-based sensors provide necessary transformations while a rank-based Hebbian learning scheme encodes a self-organized environment through translation, scale and orientation invariant sensors. Such a self-organized environment is made possible by combining wavelet sets which are orthonormal, log-scale with linear orientation and have automatically generated membership functions. In earlier work we used Gabor wavelet filters, rank-based Hebbian learning and an exponential modulation function to encode textural information from images. Many different types of modulation are possible, but based on biological findings the exponential modulation function provided a good approximation of first spike coding of `integrate and fire' neurons. These types of Hebbian encoding schemes (e.g., exponential modulation, etc.) are useful for quick response and learning, provide several advantages over contemporary neural network learning approaches, and have been found to quantize data nonlinearly. By combining wavelets with Hebbian learning we can provide a real-time front-end for modeling an intelligent process, such as the autonomous control of agents in a simulated environment.

An Alternate Definition of the ETS Delta Scale of Item Difficulty. Program Statistics Research.

ERIC Educational Resources Information Center

Holland, Paul W.; Thayer, Dorothy T.

An alternative definition has been developed of the delta scale of item difficulty used at Educational Testing Service. The traditional delta scale uses an inverse normal transformation based on normal ogive models developed years ago. However, no use is made of this fact in typical uses of item deltas. It is simply one way to make the probability…

The Delicate Analysis of Short-Term Load Forecasting

NASA Astrophysics Data System (ADS)

Song, Changwei; Zheng, Yuan

2017-05-01

This paper proposes a new method for short-term load forecasting based on the similar day method, correlation coefficient and Fast Fourier Transform (FFT) to achieve the precision analysis of load variation from three aspects (typical day, correlation coefficient, spectral analysis) and three dimensions (time dimension, industry dimensions, the main factors influencing the load characteristic such as national policies, regional economic, holidays, electricity and so on). First, the branch algorithm one-class-SVM is adopted to selection the typical day. Second, correlation coefficient method is used to obtain the direction and strength of the linear relationship between two random variables, which can reflect the influence caused by the customer macro policy and the scale of production to the electricity price. Third, Fourier transform residual error correction model is proposed to reflect the nature of load extracting from the residual error. Finally, simulation result indicates the validity and engineering practicability of the proposed method.

Wavelet based analysis of multi-electrode EEG-signals in epilepsy

NASA Astrophysics Data System (ADS)

Hein, Daniel A.; Tetzlaff, Ronald

2005-06-01

For many epilepsy patients seizures cannot sufficiently be controlled by an antiepileptic pharmacatherapy. Furthermore, only in small number of cases a surgical treatment may be possible. The aim of this work is to contribute to the realization of an implantable seizure warning device. By using recordings of electroenzephalographical(EEG) signals obtained from the department of epileptology of the University of Bonn we studied a recently proposed algorithm for the detection of parameter changes in nonlinear systems. Firstly, after calculating the crosscorrelation function between the signals of two electrodes near the epileptic focus, a wavelet-analysis follows using a sliding window with the so called Mexican-Hat wavelet. Then the Shannon-Entropy of the wavelet-transformed data has been determined providing the information content on a time scale in subject to the dilation of the wavelet-transformation. It shows distinct changes at the seizure onset for all dilations and for all patients.

Current Saturation Avoidance with Real-Time Control using DPCS

NASA Astrophysics Data System (ADS)

Ferrara, M.; Hutchinson, I.; Wolfe, S.; Stillerman, J.; Fredian, T.

2008-11-01

Tokamak ohmic-transformer and equilibrium-field coils need to be able to operate near their maximum current capabilities. However if they reach their upper limit during high-performance discharges or in the presence of a strong off-normal event, shape control is compromised, and instability, even plasma disruptions can result. On Alcator C-Mod we designed and tested an anti-saturation routine which detects the impending saturation of OH and EF currents and interpolates to a neighboring safe equilibrium in real-time. The routine was implemented with a multi-processor, multi-time-scale control scheme, which is based on a master process and multiple asynchronous slave processes. The scheme is general and can be used for any computationally-intensive algorithm. USDoE award DE- FC02-99ER545512.

Unprecedented rates of land-use transformation in modeled climate change mitigation pathways

NASA Astrophysics Data System (ADS)

Turner, P. A.; Field, C. B.; Lobell, D. B.; Sanchez, D.; Mach, K. J.

2017-12-01

Integrated assessment models (IAMs) generate climate change mitigation scenarios consistent with global temperature targets. To limit warming to 2°, stylized cost-effective mitigation pathways rely on extensive deployments of carbon dioxide (CO2) removal (CDR) technologies, including multi-gigatonne yearly carbon removal from the atmosphere through bioenergy with carbon capture and storage (BECCS) and afforestation/reforestation. These assumed CDR deployments keep ambitious temperature limits in reach, but associated rates of land-use transformation have not been evaluated. For IAM scenarios from the IPCC Fifth Assessment Report, we compare rates of modeled land-use conversion to recent observed commodity crop expansions. In scenarios with a likely chance of limiting warming to 2° in 2100, the rate of energy cropland expansion supporting BECCS exceeds past commodity crop rates by several fold. In some cases, mitigation scenarios include abrupt reversal of deforestation, paired with massive afforestation/reforestation. Specifically, energy cropland in <2° scenarios expands, on average, by 8.2 Mha yr-1 and 11.7% p.a. across scenarios. This rate exceeds, by more than 3-fold, the observed expansion of soybean, the most rapidly expanding commodity crop. If energy cropland instead increases at rates equal to recent soybean and oil palm expansions, the scale of CO2 removal possible with BECCS is 2.6 to 10-times lower, respectively, than the deployments <2° IAM scenarios rely upon in 2100. IAM mitigation pathways may favor multi-gigatonne biomass-based CDR given undervalued sociopolitical and techno-economic deployment barriers. Heroic modeled rates for land-use transformation imply that large-scale biomass-based CDR is not an easy solution to the climate challenge.

A Fast Framework for Abrupt Change Detection Based on Binary Search Trees and Kolmogorov Statistic

PubMed Central

Qi, Jin-Peng; Qi, Jie; Zhang, Qing

2016-01-01

Change-Point (CP) detection has attracted considerable attention in the fields of data mining and statistics; it is very meaningful to discuss how to quickly and efficiently detect abrupt change from large-scale bioelectric signals. Currently, most of the existing methods, like Kolmogorov-Smirnov (KS) statistic and so forth, are time-consuming, especially for large-scale datasets. In this paper, we propose a fast framework for abrupt change detection based on binary search trees (BSTs) and a modified KS statistic, named BSTKS (binary search trees and Kolmogorov statistic). In this method, first, two binary search trees, termed as BSTcA and BSTcD, are constructed by multilevel Haar Wavelet Transform (HWT); second, three search criteria are introduced in terms of the statistic and variance fluctuations in the diagnosed time series; last, an optimal search path is detected from the root to leaf nodes of two BSTs. The studies on both the synthetic time series samples and the real electroencephalograph (EEG) recordings indicate that the proposed BSTKS can detect abrupt change more quickly and efficiently than KS, t-statistic (t), and Singular-Spectrum Analyses (SSA) methods, with the shortest computation time, the highest hit rate, the smallest error, and the highest accuracy out of four methods. This study suggests that the proposed BSTKS is very helpful for useful information inspection on all kinds of bioelectric time series signals. PMID:27413364

A Fast Framework for Abrupt Change Detection Based on Binary Search Trees and Kolmogorov Statistic.

PubMed

Qi, Jin-Peng; Qi, Jie; Zhang, Qing

2016-01-01

Change-Point (CP) detection has attracted considerable attention in the fields of data mining and statistics; it is very meaningful to discuss how to quickly and efficiently detect abrupt change from large-scale bioelectric signals. Currently, most of the existing methods, like Kolmogorov-Smirnov (KS) statistic and so forth, are time-consuming, especially for large-scale datasets. In this paper, we propose a fast framework for abrupt change detection based on binary search trees (BSTs) and a modified KS statistic, named BSTKS (binary search trees and Kolmogorov statistic). In this method, first, two binary search trees, termed as BSTcA and BSTcD, are constructed by multilevel Haar Wavelet Transform (HWT); second, three search criteria are introduced in terms of the statistic and variance fluctuations in the diagnosed time series; last, an optimal search path is detected from the root to leaf nodes of two BSTs. The studies on both the synthetic time series samples and the real electroencephalograph (EEG) recordings indicate that the proposed BSTKS can detect abrupt change more quickly and efficiently than KS, t-statistic (t), and Singular-Spectrum Analyses (SSA) methods, with the shortest computation time, the highest hit rate, the smallest error, and the highest accuracy out of four methods. This study suggests that the proposed BSTKS is very helpful for useful information inspection on all kinds of bioelectric time series signals.

Sea Ice Drift Monitoring in the Bohai Sea Based on GF4 Satellite

NASA Astrophysics Data System (ADS)

Zhao, Y.; Wei, P.; Zhu, H.; Xing, B.

2018-04-01

The Bohai Sea is the inland sea with the highest latitude in China. In winter, the phenomenon of freezing occurs in the Bohai Sea due to frequent cold wave influx. According to historical records, there have been three serious ice packs in the Bohai Sea in the past 50 years which caused heavy losses to our economy. Therefore, it is of great significance to monitor the drift of sea ice and sea ice in the Bohai Sea. The GF4 image has the advantages of short imaging time and high spatial resolution. Based on the GF4 satellite images, the three methods of SIFT (Scale invariant feature - the transform and Scale invariant feature transform), MCC (maximum cross-correlation method) and sift combined with MCC are used to monitor sea ice drift and calculate the speed and direction of sea ice drift, the three calculation results are compared and analyzed by using expert interpretation and historical statistical data to carry out remote sensing monitoring of sea ice drift results. The experimental results show that the experimental results of the three methods are in accordance with expert interpretation and historical statistics. Therefore, the GF4 remote sensing satellite images have the ability to monitor sea ice drift and can be used for drift monitoring of sea ice in the Bohai Sea.

Index-based Crop Insurance for Climate Adaptation in the Developing World

NASA Astrophysics Data System (ADS)

Brown, M. E.; Osgood, D. E.; Carriquiry, M. A.

2011-12-01

Weather has always presented a challenge to small-scale farmers, particularly in regions where poverty and lack of infrastructure has restricted the development of financial instruments to limit risk. New 'index' insurance innovations in agriculture are beginning to enable even the poorest farmers to unlock major productivity gains (e.g. insuring loans for improved seeds). Although index insurance has the potential to greatly improve productivity in developing country agriculture, the principal technical challenge to up-scaling this product is "data poverty," the absence of weather data in low-income areas needed to design robust and affordable insurance products. Earth science, particularly remote sensing, has the potential to ameliorate data poverty. However, raw use of earth science model output leads to non-optimal indexes and many obstacles remain to transform earth science products into insurance solutions. Estimation uncertainty, limited availability of consistent time series, and difficulties of predicting loses based on remote observations are reviewed in this article. The importance of multidisciplinary approaches addressing the needs of stakeholders in simple to understand indexes is highlighted. The successful use of Earth science data to support the index insurance industry in currently poor and isolated communities in the developing world would transform the ability of small farmers to increase yields, household incomes and regional economies, if the growing gap between earth science and index insurance can be closed.

Facilitating the Specification Capture and Transformation Process in the Development of Multi-Agent Systems

NASA Technical Reports Server (NTRS)

Filho, Aluzio Haendehen; Caminada, Numo; Haeusler, Edward Hermann; vonStaa, Arndt

2004-01-01

To support the development of flexible and reusable MAS, we have built a framework designated MAS-CF. MAS-CF is a component framework that implements a layered architecture based on contextual composition. Interaction rules, controlled by architecture mechanisms, ensure very low coupling, making possible the sharing of distributed services in a transparent, dynamic and independent way. These properties propitiate large-scale reuse, since organizational abstractions can be reused and propagated to all instances created from a framework. The objective is to reduce complexity and development time of multi-agent systems through the reuse of generic organizational abstractions.

Oxidation mechanism of T91 steel in liquid lead-bismuth eutectic: with consideration of internal oxidation

PubMed Central

Ye, Zhongfei; Wang, Pei; Dong, Hong; Li, Dianzhong; Zhang, Yutuo; Li, Yiyi

2016-01-01

Clarification of the microscopic events that occur during oxidation is of great importance for understanding and consequently controlling the oxidation process. In this study the oxidation product formed on T91 ferritic/martensitic steel in oxygen saturated liquid lead-bismuth eutectic (LBE) at 823 K was characterized at the nanoscale using focused-ion beam and transmission electron microscope. An internal oxidation zone (IOZ) under the duplex oxide scale has been confirmed and characterized systematically. Through the microscopic characterization of the IOZ and the inner oxide layer, the micron-scale and nano-scale diffusion of Cr during the oxidation in LBE has been determined for the first time. The micron-scale diffusion of Cr ensures the continuous advancement of IOZ and inner oxide layer, and nano-scale diffusion of Cr gives rise to the typical appearance of the IOZ. Finally, a refined oxidation mechanism including the internal oxidation and the transformation of IOZ to inner oxide layer is proposed based on the discussion. The proposed oxidation mechanism succeeds in bridging the gap between the existing models and experimental observations. PMID:27734928

Stabilizing Conditional Standard Errors of Measurement in Scale Score Transformations

ERIC Educational Resources Information Center

Moses, Tim; Kim, YoungKoung

2017-01-01

The focus of this article is on scale score transformations that can be used to stabilize conditional standard errors of measurement (CSEMs). Three transformations for stabilizing the estimated CSEMs are reviewed, including the traditional arcsine transformation, a recently developed general variance stabilization transformation, and a new method…

Scale relativity: from quantum mechanics to chaotic dynamics.

NASA Astrophysics Data System (ADS)

Nottale, L.

Scale relativity is a new approach to the problem of the origin of fundamental scales and of scaling laws in physics, which consists in generalizing Einstein's principle of relativity to the case of scale transformations of resolutions. We recall here how it leads one to the concept of fractal space-time, and to introduce a new complex time derivative operator which allows to recover the Schrödinger equation, then to generalize it. In high energy quantum physics, it leads to the introduction of a Lorentzian renormalization group, in which the Planck length is reinterpreted as a lowest, unpassable scale, invariant under dilatations. These methods are successively applied to two problems: in quantum mechanics, that of the mass spectrum of elementary particles; in chaotic dynamics, that of the distribution of planets in the Solar System.

A global strategy based on experiments and simulations for squeal prediction on industrial railway brakes

NASA Astrophysics Data System (ADS)

Sinou, J.-J.; Loyer, A.; Chiello, O.; Mogenier, G.; Lorang, X.; Cocheteux, F.; Bellaj, S.

2013-09-01

This paper presents an overview of recent experimental and numerical investigations on industrial railway brakes. The goal of the present study is to discuss the relevance of the mechanical modeling strategy for squeal prediction. Specific experimental set-ups based on transient and controlled braking tests are designed for this purpose. Measurements are performed on it to investigate the dynamic behavior of TGV squeal noise and its squeal characterization through experiments. It will be demonstrated that it is possible to build consistent and efficient finite element models to simulate squeal events in TGV brake systems. The numerical strategy will be presented, including not only the modeling of the TGV brake system and the stability analysis, but also the transient nonlinear dynamic and computational process based on efficient reduced basis. This complete numerical strategy allows us to perform relevance squeal prediction on industrial railway brakes. This study comes within the scope of a research program AcouFren that is supported by ADEME (Agence De l'Environnement et de la Maîtrise de l'Energie) concerning the reduction of the squeal noise generated by high power railway disc brakes. experiments with an evolution of the rotational speed of the disc: these tests are called "transient braking tests" and correspond to real braking tests, experiments with a controlled steady rotational speed (i.e. dynamic fluctuations in rotational speed are not significant): these tests are called "controlled braking tests". In the present study, the Continuous Wavelet Transform (CWT) [20] is used to study the time-history responses of the TGV brake system. So, a brief basic theory of the wavelet analysis that transforms a signal into wavelets that are well localized both in frequency and time is presented in this part of the paper. Considering a function f(t), the associated Continuous Wavelet Transform (CWT) corresponds to a wavelet transform given by W(a,b)=∫-∞+∞f(t)ψa,b*(t) dt where ψ(t)={1}/{√{a}}ψ({t-b}/{a}) where a and b define the scale parameter and the time translation factor, respectively. The asterisk ψa,b* indicates the complex conjugate of ψ that are the daughter wavelets (i.e. the dilated and shifted versions of the "'mother"' wavelet ψ that is continuous in both time and frequency). The mother wavelet must satisfy an admissibility criterion in order to get a stably invertible transform.

Frequency hopping signal detection based on wavelet decomposition and Hilbert-Huang transform

NASA Astrophysics Data System (ADS)

Zheng, Yang; Chen, Xihao; Zhu, Rui

2017-07-01

Frequency hopping (FH) signal is widely adopted by military communications as a kind of low probability interception signal. Therefore, it is very important to research the FH signal detection algorithm. The existing detection algorithm of FH signals based on the time-frequency analysis cannot satisfy the time and frequency resolution requirement at the same time due to the influence of window function. In order to solve this problem, an algorithm based on wavelet decomposition and Hilbert-Huang transform (HHT) was proposed. The proposed algorithm removes the noise of the received signals by wavelet decomposition and detects the FH signals by Hilbert-Huang transform. Simulation results show the proposed algorithm takes into account both the time resolution and the frequency resolution. Correspondingly, the accuracy of FH signals detection can be improved.

A Tesla-type repetitive nanosecond pulse generator for solid dielectric breakdown research.

PubMed

Zhao, Liang; Pan, Ya Feng; Su, Jian Cang; Zhang, Xi Bo; Wang, Li Min; Fang, Jin Peng; Sun, Xu; Lui, Rui

2013-10-01

A Tesla-type repetitive nanosecond pulse generator including a pair of electrode and a matched absorption resistor is established for the application of solid dielectric breakdown research. As major components, a built-in Tesla transformer and a gas-gap switch are designed to boost and shape the output pulse, respectively; the electrode is to form the anticipated electric field; the resistor is parallel to the electrode to absorb the reflected energy from the test sample. The parameters of the generator are a pulse width of 10 ns, a rise and fall time of 3 ns, and a maximum amplitude of 300 kV. By modifying the primary circuit of the Tesla transformer, the generator can produce both positive and negative pulses at a repetition rate of 1-50 Hz. In addition, a real-time measurement and control system is established based on the solid dielectric breakdown requirements for this generator. With this system, experiments on test samples made of common insulation materials in pulsed power systems are conducted. The preliminary experimental results show that the constructed generator is capable to research the solid dielectric breakdown phenomenon on a nanosecond time scale.

A window-based time series feature extraction method.

PubMed

Katircioglu-Öztürk, Deniz; Güvenir, H Altay; Ravens, Ursula; Baykal, Nazife

2017-10-01

This study proposes a robust similarity score-based time series feature extraction method that is termed as Window-based Time series Feature ExtraCtion (WTC). Specifically, WTC generates domain-interpretable results and involves significantly low computational complexity thereby rendering itself useful for densely sampled and populated time series datasets. In this study, WTC is applied to a proprietary action potential (AP) time series dataset on human cardiomyocytes and three precordial leads from a publicly available electrocardiogram (ECG) dataset. This is followed by comparing WTC in terms of predictive accuracy and computational complexity with shapelet transform and fast shapelet transform (which constitutes an accelerated variant of the shapelet transform). The results indicate that WTC achieves a slightly higher classification performance with significantly lower execution time when compared to its shapelet-based alternatives. With respect to its interpretable features, WTC has a potential to enable medical experts to explore definitive common trends in novel datasets. Copyright © 2017 Elsevier Ltd. All rights reserved.

Communication: A reduced scaling J-engine based reformulation of SOS-MP2 using graphics processing units

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

Maurer, S. A.; Kussmann, J.; Ochsenfeld, C., E-mail: Christian.Ochsenfeld@cup.uni-muenchen.de

2014-08-07

We present a low-prefactor, cubically scaling scaled-opposite-spin second-order Møller-Plesset perturbation theory (SOS-MP2) method which is highly suitable for massively parallel architectures like graphics processing units (GPU). The scaling is reduced from O(N{sup 5}) to O(N{sup 3}) by a reformulation of the MP2-expression in the atomic orbital basis via Laplace transformation and the resolution-of-the-identity (RI) approximation of the integrals in combination with efficient sparse algebra for the 3-center integral transformation. In contrast to previous works that employ GPUs for post Hartree-Fock calculations, we do not simply employ GPU-based linear algebra libraries to accelerate the conventional algorithm. Instead, our reformulation allows tomore » replace the rate-determining contraction step with a modified J-engine algorithm, that has been proven to be highly efficient on GPUs. Thus, our SOS-MP2 scheme enables us to treat large molecular systems in an accurate and efficient manner on a single GPU-server.« less

On the Assessment of Global Terrestrial Reference Frame Temporal Variations

NASA Astrophysics Data System (ADS)

Ampatzidis, Dimitrios; Koenig, Rolf; Zhu, Shengyuan

2015-04-01

Global Terrestrial Reference Frames (GTRFs) as the International Terrestrial Reference Frame (ITRF) provide reliable 4-D position information (3-D coordinates and their evolution through time). The given 3-D velocities play a significant role in precise position acquisition and are estimated from long term coordinate time series from the space-geodetic techniques DORIS, GNSS, SLR, and VLBI. GTRFs temporal evolution is directly connected with their internal stability: The more intense and inhomogeneous velocity field, the less stable TRF is derived. The assessment of the quality of the GTRF is mainly realized by comparing it to each individual technique's reference frame. E.g the comparison of GTRFs to SLR-only based TRF gives the sense of the ITRF stability with respect to the Geocenter and scale and their associated rates respectively. In addition, the comparison of ITRF to the VLBI-only based TRF can be used for the scale validation. However, till now there is not any specified methodology for the total assessment (in terms of origin, orientation and scale respectively) of the temporal evolution and GTRFs associated accuracy. We present a new alternative diagnostic tool for the assessment of GTRFs temporal evolution based on the well-known time-dependent Helmert type transformation formula (three shifts, three rotations and scale rates respectively). The advantage of the new methodology relies on the fact that it uses the full velocity field of the TRF and therefore all points not just the ones common to different techniques. It also examines simultaneously rates of origin, orientation and scale. The methodology is presented and implemented to the two existing GTRFs on the market (ITRF and DTRF which is computed from DGFI) , the results are discussed. The results also allow to compare directly each GTRF dynamic behavior. Furthermore, the correlations of the estimated parameters can also provide useful information to the proposed GTRFs assessment scheme.

Mapping soil total nitrogen of cultivated land at county scale by using hyperspectral image

NASA Astrophysics Data System (ADS)

Gu, Xiaohe; Zhang, Li Yan; Shu, Meiyan; Yang, Guijun

2018-02-01

Monitoring total nitrogen content (TNC) in the soil of cultivated land quantitively and mastering its spatial distribution are helpful for crop growing, soil fertility adjustment and sustainable development of agriculture. The study aimed to develop a universal method to map total nitrogen content in soil of cultivated land by HSI image at county scale. Several mathematical transformations were used to improve the expression ability of HSI image. The correlations between soil TNC and the reflectivity and its mathematical transformations were analyzed. Then the susceptible bands and its transformations were screened to develop the optimizing model of map soil TNC in the Anping County based on the method of multiple linear regression. Results showed that the bands of 14th, 16th, 19th, 37th and 60th with different mathematical transformations were screened as susceptible bands. Differential transformation was helpful for reducing the noise interference to the diagnosis ability of the target spectrum. The determination coefficient of the first order differential of logarithmic transformation was biggest (0.505), while the RMSE was lowest. The study confirmed the first order differential of logarithm transformation as the optimal inversion model for soil TNC, which was used to map soil TNC of cultivated land in the study area.

An investigation of ride quality rating scales

NASA Technical Reports Server (NTRS)

Dempsey, T. K.; Coates, G. D.; Leatherwood, J. D.

1977-01-01

An experimental investigation was conducted for the combined purposes of determining the relative merits of various category scales for the prediction of human discomfort response to vibration and for determining the mathematical relationships whereby subjective data are transformed from one scale to other scales. There were 16 category scales analyzed representing various parametric combinations of polarity, that is, unipolar and bipolar, scale type, and number of scalar points. Results indicated that unipolar continuous-type scales containing either seven or nine scalar points provide the greatest reliability and discriminability. Transformations of subjective data between category scales were found to be feasible with unipolar scales of a larger number of scalar points providing the greatest accuracy of transformation. The results contain coefficients for transformation of subjective data between the category scales investigated. A result of particular interest was that the comfort half of a bipolar scale was seldom used by subjects to describe their subjective reaction to vibration.

Interacting particle systems in time-dependent geometries

NASA Astrophysics Data System (ADS)

Ali, A.; Ball, R. C.; Grosskinsky, S.; Somfai, E.

2013-09-01

Many complex structures and stochastic patterns emerge from simple kinetic rules and local interactions, and are governed by scale invariance properties in combination with effects of the global geometry. We consider systems that can be described effectively by space-time trajectories of interacting particles, such as domain boundaries in two-dimensional growth or river networks. We study trajectories embedded in time-dependent geometries, and the main focus is on uniformly expanding or decreasing domains for which we obtain an exact mapping to simple fixed domain systems while preserving the local scale invariance properties. This approach was recently introduced in Ali et al (2013 Phys. Rev. E 87 020102(R)) and here we provide a detailed discussion on its applicability for self-affine Markovian models, and how it can be adapted to self-affine models with memory or explicit time dependence. The mapping corresponds to a nonlinear time transformation which converges to a finite value for a large class of trajectories, enabling an exact analysis of asymptotic properties in expanding domains. We further provide a detailed discussion of different particle interactions and generalized geometries. All our findings are based on exact computations and are illustrated numerically for various examples, including Lévy processes and fractional Brownian motion.

A robust algorithm for optimisation and customisation of fractal dimensions of time series modified by nonlinearly scaling their time derivatives: mathematical theory and practical applications.

PubMed

Fuss, Franz Konstantin

2013-01-01

Standard methods for computing the fractal dimensions of time series are usually tested with continuous nowhere differentiable functions, but not benchmarked with actual signals. Therefore they can produce opposite results in extreme signals. These methods also use different scaling methods, that is, different amplitude multipliers, which makes it difficult to compare fractal dimensions obtained from different methods. The purpose of this research was to develop an optimisation method that computes the fractal dimension of a normalised (dimensionless) and modified time series signal with a robust algorithm and a running average method, and that maximises the difference between two fractal dimensions, for example, a minimum and a maximum one. The signal is modified by transforming its amplitude by a multiplier, which has a non-linear effect on the signal's time derivative. The optimisation method identifies the optimal multiplier of the normalised amplitude for targeted decision making based on fractal dimensions. The optimisation method provides an additional filter effect and makes the fractal dimensions less noisy. The method is exemplified by, and explained with, different signals, such as human movement, EEG, and acoustic signals.

A Robust Algorithm for Optimisation and Customisation of Fractal Dimensions of Time Series Modified by Nonlinearly Scaling Their Time Derivatives: Mathematical Theory and Practical Applications

PubMed Central

2013-01-01

Standard methods for computing the fractal dimensions of time series are usually tested with continuous nowhere differentiable functions, but not benchmarked with actual signals. Therefore they can produce opposite results in extreme signals. These methods also use different scaling methods, that is, different amplitude multipliers, which makes it difficult to compare fractal dimensions obtained from different methods. The purpose of this research was to develop an optimisation method that computes the fractal dimension of a normalised (dimensionless) and modified time series signal with a robust algorithm and a running average method, and that maximises the difference between two fractal dimensions, for example, a minimum and a maximum one. The signal is modified by transforming its amplitude by a multiplier, which has a non-linear effect on the signal's time derivative. The optimisation method identifies the optimal multiplier of the normalised amplitude for targeted decision making based on fractal dimensions. The optimisation method provides an additional filter effect and makes the fractal dimensions less noisy. The method is exemplified by, and explained with, different signals, such as human movement, EEG, and acoustic signals. PMID:24151522

Multi-scale pixel-based image fusion using multivariate empirical mode decomposition.

PubMed

Rehman, Naveed ur; Ehsan, Shoaib; Abdullah, Syed Muhammad Umer; Akhtar, Muhammad Jehanzaib; Mandic, Danilo P; McDonald-Maier, Klaus D

2015-05-08

A novel scheme to perform the fusion of multiple images using the multivariate empirical mode decomposition (MEMD) algorithm is proposed. Standard multi-scale fusion techniques make a priori assumptions regarding input data, whereas standard univariate empirical mode decomposition (EMD)-based fusion techniques suffer from inherent mode mixing and mode misalignment issues, characterized respectively by either a single intrinsic mode function (IMF) containing multiple scales or the same indexed IMFs corresponding to multiple input images carrying different frequency information. We show that MEMD overcomes these problems by being fully data adaptive and by aligning common frequency scales from multiple channels, thus enabling their comparison at a pixel level and subsequent fusion at multiple data scales. We then demonstrate the potential of the proposed scheme on a large dataset of real-world multi-exposure and multi-focus images and compare the results against those obtained from standard fusion algorithms, including the principal component analysis (PCA), discrete wavelet transform (DWT) and non-subsampled contourlet transform (NCT). A variety of image fusion quality measures are employed for the objective evaluation of the proposed method. We also report the results of a hypothesis testing approach on our large image dataset to identify statistically-significant performance differences.

Multi-Scale Pixel-Based Image Fusion Using Multivariate Empirical Mode Decomposition

PubMed Central

Rehman, Naveed ur; Ehsan, Shoaib; Abdullah, Syed Muhammad Umer; Akhtar, Muhammad Jehanzaib; Mandic, Danilo P.; McDonald-Maier, Klaus D.

2015-01-01

A novel scheme to perform the fusion of multiple images using the multivariate empirical mode decomposition (MEMD) algorithm is proposed. Standard multi-scale fusion techniques make a priori assumptions regarding input data, whereas standard univariate empirical mode decomposition (EMD)-based fusion techniques suffer from inherent mode mixing and mode misalignment issues, characterized respectively by either a single intrinsic mode function (IMF) containing multiple scales or the same indexed IMFs corresponding to multiple input images carrying different frequency information. We show that MEMD overcomes these problems by being fully data adaptive and by aligning common frequency scales from multiple channels, thus enabling their comparison at a pixel level and subsequent fusion at multiple data scales. We then demonstrate the potential of the proposed scheme on a large dataset of real-world multi-exposure and multi-focus images and compare the results against those obtained from standard fusion algorithms, including the principal component analysis (PCA), discrete wavelet transform (DWT) and non-subsampled contourlet transform (NCT). A variety of image fusion quality measures are employed for the objective evaluation of the proposed method. We also report the results of a hypothesis testing approach on our large image dataset to identify statistically-significant performance differences. PMID:26007714

Scale-up of a Luminescent Solar Concentrator-Based Photomicroreactor via Numbering-up.

PubMed

Zhao, Fang; Cambié, Dario; Janse, Jeroen; Wieland, Eric W; Kuijpers, Koen P L; Hessel, Volker; Debije, Michael G; Noël, Timothy

2018-01-02

The use of solar energy to power chemical reactions is a long-standing dream of the chemical community. Recently, visible-light-mediated photoredox catalysis has been recognized as the ideal catalytic transformation to convert solar energy into chemical bonds. However, scaling photochemical transformations has been extremely challenging due to Bouguer-Lambert-Beer law. Recently, we have pioneered the development of luminescent solar concentrator photomicroreactors (LSC-PMs), which display an excellent energy efficiency. These devices harvest solar energy, convert the broad solar energy spectrum to a narrow-wavelength region, and subsequently waveguide the re-emitted photons to the reaction channels. Herein, we report on the scalability of such LSC-PMs via a numbering-up strategy. Paramount in our work was the use of molds that were fabricated via 3D printing. This allowed us to rapidly produce many different prototypes and to optimize experimentally key design aspects in a time-efficient fashion. Reactors up to 32 parallel channels have been fabricated that display an excellent flow distribution using a bifurcated flow distributor (standard deviations below 10%). This excellent flow distribution was crucial to scale up a model reaction efficiently, displaying yields comparable to those obtained in a single-channel device. We also found that interchannel spacing is an important and unique design parameter for numbered-up LSC-PMs, which influences greatly the photon flux experienced within the reaction channels.

Scale-up of a Luminescent Solar Concentrator-Based Photomicroreactor via Numbering-up

PubMed Central

2017-01-01

The use of solar energy to power chemical reactions is a long-standing dream of the chemical community. Recently, visible-light-mediated photoredox catalysis has been recognized as the ideal catalytic transformation to convert solar energy into chemical bonds. However, scaling photochemical transformations has been extremely challenging due to Bouguer–Lambert–Beer law. Recently, we have pioneered the development of luminescent solar concentrator photomicroreactors (LSC-PMs), which display an excellent energy efficiency. These devices harvest solar energy, convert the broad solar energy spectrum to a narrow-wavelength region, and subsequently waveguide the re-emitted photons to the reaction channels. Herein, we report on the scalability of such LSC-PMs via a numbering-up strategy. Paramount in our work was the use of molds that were fabricated via 3D printing. This allowed us to rapidly produce many different prototypes and to optimize experimentally key design aspects in a time-efficient fashion. Reactors up to 32 parallel channels have been fabricated that display an excellent flow distribution using a bifurcated flow distributor (standard deviations below 10%). This excellent flow distribution was crucial to scale up a model reaction efficiently, displaying yields comparable to those obtained in a single-channel device. We also found that interchannel spacing is an important and unique design parameter for numbered-up LSC-PMs, which influences greatly the photon flux experienced within the reaction channels. PMID:29333350

It's All Relative: A Validation of Radiation Quality Comparison Metrics

NASA Technical Reports Server (NTRS)

Chappell, L. J.; Milder, C. M.; Elgart, S. R.; Semones, E. J.

2017-01-01

Historically, the relative biological effectiveness (RBE) has been calculated to quantify the difference between heavy ion and gamma ray radiation. The RBE is then applied to gamma ray data to predict the effects of heavy ions in humans. The RBE is an iso-effect dose-to-dose ratio which, due to its counterintuitive nature, has been commonly miscalculated as an iso-dose effect-to-effect ratio. A paper recently published by Shuryak et al described this second measure intentionally for the first time in 2017, referring to it as the radiation effects ratio (RER). In this study, we utilized simulations to test the ability of both the RBE and the RER to predict known heavy ion effects. RBEs and RERs were calculated using mouse data from Chang et al, and the ability of the RBE and RER to predict the heavy ion data from which they were calculated was verified. Statistical transformations often utilized during data analysis were applied to the gamma and heavy ion data to determine whether RBE and RER are each uniquely defined measures. Scale changes are expected when translating effects from mice to humans and between human populations; gamma and heavy ion data were transformed to represent potential scale changes. The ability of the RBE and RER to predict the transformed heavy ion data from the transformed gamma data was then tested. The RBE but not the RER was uniquely defined after all statistical transformations. The RBE correctly predicted the scale-transformed heavy ion data, while the RER did not. This presentation describes potential implications for both metrics in light of these findings.

Adaptive multiscale processing for contrast enhancement

NASA Astrophysics Data System (ADS)

Laine, Andrew F.; Song, Shuwu; Fan, Jian; Huda, Walter; Honeyman, Janice C.; Steinbach, Barbara G.

1993-07-01

This paper introduces a novel approach for accomplishing mammographic feature analysis through overcomplete multiresolution representations. We show that efficient representations may be identified from digital mammograms within a continuum of scale space and used to enhance features of importance to mammography. Choosing analyzing functions that are well localized in both space and frequency, results in a powerful methodology for image analysis. We describe methods of contrast enhancement based on two overcomplete (redundant) multiscale representations: (1) Dyadic wavelet transform (2) (phi) -transform. Mammograms are reconstructed from transform coefficients modified at one or more levels by non-linear, logarithmic and constant scale-space weight functions. Multiscale edges identified within distinct levels of transform space provide a local support for enhancement throughout each decomposition. We demonstrate that features extracted from wavelet spaces can provide an adaptive mechanism for accomplishing local contrast enhancement. We suggest that multiscale detection and local enhancement of singularities may be effectively employed for the visualization of breast pathology without excessive noise amplification.

EnvironmentalWaveletTool: Continuous and discrete wavelet analysis and filtering for environmental time series

NASA Astrophysics Data System (ADS)

Galiana-Merino, J. J.; Pla, C.; Fernandez-Cortes, A.; Cuezva, S.; Ortiz, J.; Benavente, D.

2014-10-01

A MATLAB-based computer code has been developed for the simultaneous wavelet analysis and filtering of several environmental time series, particularly focused on the analyses of cave monitoring data. The continuous wavelet transform, the discrete wavelet transform and the discrete wavelet packet transform have been implemented to provide a fast and precise time-period examination of the time series at different period bands. Moreover, statistic methods to examine the relation between two signals have been included. Finally, the entropy of curves and splines based methods have also been developed for segmenting and modeling the analyzed time series. All these methods together provide a user-friendly and fast program for the environmental signal analysis, with useful, practical and understandable results.

Dynamics of ligand substitution in labile cobalt complexes resolved by ultrafast T-jump

PubMed Central

Ma, Hairong; Wan, Chaozhi; Zewail, Ahmed H.

2008-01-01

Ligand exchange of hydrated metal complexes is common in chemical and biological systems. Using the ultrafast T-jump, we examined this process, specifically the transformation of aqua cobalt (II) complexes to their fully halogenated species. The results reveal a stepwise mechanism with time scales varying from hundreds of picoseconds to nanoseconds. The dynamics are significantly faster when the structure is retained but becomes rate-limited when the octahedral-to-tetrahedral structural change bottlenecks the transformation. Evidence is presented, from bimolecular kinetics and energetics (enthalpic and entropic), for a reaction in which the ligand assists the displacement of water molecules, with the retention of the entering ligand in the activated state. The reaction time scale deviates by one to two orders of magnitude from that of ionic diffusion, suggesting the involvement of a collisional barrier between the ion and the much larger complex. PMID:18725628

Automatic Brain Portion Segmentation From Magnetic Resonance Images of Head Scans Using Gray Scale Transformation and Morphological Operations.

PubMed

Somasundaram, Karuppanagounder; Ezhilarasan, Kamalanathan

2015-01-01

To develop an automatic skull stripping method for magnetic resonance imaging (MRI) of human head scans. The proposed method is based on gray scale transformation and morphological operations. The proposed method has been tested with 20 volumes of normal T1-weighted images taken from Internet Brain Segmentation Repository. Experimental results show that the proposed method gives better results than the popular skull stripping methods Brain Extraction Tool and Brain Surface Extractor. The average value of Jaccard and Dice coefficients are 0.93 and 0.962 respectively. In this article, we have proposed a novel skull stripping method using intensity transformation and morphological operations. This is a low computational complexity method but gives competitive or better results than that of the popular skull stripping methods Brain Surface Extractor and Brain Extraction Tool.

Cahokia's boom and bust in the context of climate change

USGS Publications Warehouse

Benson, L.V.; Pauketat, T.R.; Cook, E.R.

2009-01-01

During the early Mississippian Lohmann phase (A.D. 1050-1100), the American Bottom experienced a political and economic transformation. This transformation included the abrupt planned construction of central Cahokia, a large-scale influx of people to "downtown Cahokia," the abandonment of pre-Mississippian village settlements, the reorganization of farming in the Mississippi River floodplain, and the founding of the Richland farming complex in the Illinois uplands. New tree-ring-based records of climate change indicate that this rapid development occurred during one of the wettest 50-year periods during the last millennium. During the next 150 years, a series of persistent droughts occurred in the Cahokian area which may be related to the eventual abandonment of the American Bottom. By A.D. 1150, in the latter part of a severe 15-year drought, the Richland farming complex was mostly abandoned, eliminating an integral part of Cahokia's agricultural base. At about the same time, a 20,000-log palisade was erected around Monks Mound and the Grand Plaza, indicating increased social unrest. During this time, people began exiting Cahokia and, by the end of the Stirling phase (A.D. 1200), Cahokia's population had decreased by about 50 percent and by A.D. 1350, Cahokia and much of the central Mississippi valley had been abandoned. ??2009 by the Society for American Archaeology.

Two Point Space-Time Correlation of Density Fluctuations Measured in High Velocity Free Jets

NASA Technical Reports Server (NTRS)

Panda, Jayanta

2006-01-01

Two-point space-time correlations of air density fluctuations in unheated, fully-expanded free jets at Mach numbers M(sub j) = 0.95, 1.4, and 1.8 were measured using a Rayleigh scattering based diagnostic technique. The molecular scattered light from two small probe volumes of 1.03 mm length was measured for a completely non-intrusive means of determining the turbulent density fluctuations. The time series of density fluctuations were analyzed to estimate the integral length scale L in a moving frame of reference and the convective Mach number M(sub c) at different narrow Strouhal frequency (St) bands. It was observed that M(sub c) and the normalized moving frame length scale L*St/D, where D is the jet diameter, increased with Strouhal frequency before leveling off at the highest resolved frequency. Significant differences were observed between data obtained from the lip shear layer and the centerline of the jet. The wave number frequency transform of the correlation data demonstrated progressive increase in the radiative part of turbulence fluctuations with increasing jet Mach number.

Atmospheric transformation of multispectral remote sensor data. [Great Lakes

NASA Technical Reports Server (NTRS)

Turner, R. E. (Principal Investigator)

1977-01-01

The author has identified the following significant results. The effects of earth's atmosphere were accounted for, and a simple algorithm, based upon a radiative transfer model, was developed to determine the radiance at earth's surface free of atmospheric effects. Acutal multispectral remote sensor data for Lake Erie and associated optical thickness data were used to demonstrate the effectiveness of the atmospheric transformation algorithm. The basic transformation was general in nature and could be applied to the large scale processing of multispectral aircraft or satellite remote sensor data.

A 0.5 MV magnetically self-insulated pulsed transformer

NASA Astrophysics Data System (ADS)

Istenic, M.; Novac, B. M.; Luo, J.; Kumar, R.; Smith, I. R.

2006-11-01

This paper describes the successful development of a light and compact 0.5 MV spiral-strip transformer, with the secondary winding contained in vacuum and based on magnetic self-insulation. Ensuring trouble-free operation required the use of conductive elastomers in electric field grading techniques and the adoption in the secondary winding of glass/ceramic conductor spacers. It is demonstrated that the primary-current/secondary breakdown-voltage characteristic is a function of the vacuum pressure, with only 52 kA being necessary to produce 0.5 MV at 10-6 Torr. The difficult task of modelling the transformer required 3D electric and magnetic field computation, together with state-of-the-art calculation of the electron flow in the vacuum. Based on the results obtained to date, scaling up to multi-megavolt transformers can readily be envisaged.

Sensing Surveillance & Navigation

DTIC Science & Technology

2012-03-07

Removing Atmospheric Turbulence Goal: to restore a single high quality image from the observed sequence Prof. Peyman...Computer Sciences – Higher wavelet studies , time-scale, time-frequency transformations, Reduced Signature Targets, Low Probability of Intercept...Range Dependent Beam -patterns •Electronic Steering with Frequency Offsets •Inherent Countermeasure Capability Why? W1(t) W2(t) W3

Short-term data forecasting based on wavelet transformation and chaos theory

NASA Astrophysics Data System (ADS)

Wang, Yi; Li, Cunbin; Zhang, Liang

2017-09-01

A sketch of wavelet transformation and its application was given. Concerning the characteristics of time sequence, Haar wavelet was used to do data reduction. After processing, the effect of “data nail” on forecasting was reduced. Chaos theory was also introduced, a new chaos time series forecasting flow based on wavelet transformation was proposed. The largest Lyapunov exponent was larger than zero from small data sets, it verified the data change behavior still met chaotic behavior. Based on this, chaos time series to forecast short-term change behavior could be used. At last, the example analysis of the price from a real electricity market showed that the forecasting method increased the precision of the forecasting more effectively and steadily.

Kinetics of the coesite to quartz transformation

USGS Publications Warehouse

Mosenfelder, J.L.; Bohlen, S.R.

1997-01-01

The survival of coesite in ultrahigh-pressure (UHP) rocks has important implications for the exhumation of subducted crustal rocks. We have conducted experiments to study the mechanism and rate of the coesite ??? quartz transformation using polycrystalline coesite aggregates, fabricated by devitrifying silica glass cylinders containing 2850H/106 Si at 1000??C and 3.6 GPa for 24h. Conditions were adjusted following synthesis to transform the samples at 700-1000??C at pressures 190-410 MPa below the quartz-coesite equilibrium boundary. Reaction proceeds via grain-boundary nucleation and interface-controlled growth, with characteristic reaction textures remarkably similar to those seen in natural UHP rocks. We infer that the experimental reaction mechanism is identical to that in nature, a prerequisite for reliable extrapolation of the rate data. Growth rates obtained by direct measurement differ by up to two orders of magnitude from those estimated by fitting a rate equation to the transformation-time data. Fitting the rates to Turnbull's equation for growth therefore yields two distinct sets of parameters with similar activation energies (242 or 269 kJ/mol) but significantly different pre-exponential constants. Extrapolation based on either set of growth rates suggests that coesite should not be preserved on geologic time scales if it reaches the quartz stability field at temperatures above 375-400??C. The survival of coesite has previously been linked to its inclusion in strong phases, such as garnet, that can sustain a high internal pressure during decompression. Other factors that may play a crucial role in preservation are low fluid availability - possibly even less than that of our nominally "dry" experiments - and the development of transformation stress, which inhibits nucleation and growth. These issues are discussed in the context of our experiments as well as recent observations from natural rocks. ?? 1997 Elsevier Science B.V.

Discrete diffusion models to study the effects of Mg2+ concentration on the PhoPQ signal transduction system

PubMed Central

2010-01-01

Background The challenge today is to develop a modeling and simulation paradigm that integrates structural, molecular and genetic data for a quantitative understanding of physiology and behavior of biological processes at multiple scales. This modeling method requires techniques that maintain a reasonable accuracy of the biological process and also reduces the computational overhead. This objective motivates the use of new methods that can transform the problem from energy and affinity based modeling to information theory based modeling. To achieve this, we transform all dynamics within the cell into a random event time, which is specified through an information domain measure like probability distribution. This allows us to use the “in silico” stochastic event based modeling approach to find the molecular dynamics of the system. Results In this paper, we present the discrete event simulation concept using the example of the signal transduction cascade triggered by extra-cellular Mg2+ concentration in the two component PhoPQ regulatory system of Salmonella Typhimurium. We also present a model to compute the information domain measure of the molecular transport process by estimating the statistical parameters of inter-arrival time between molecules/ions coming to a cell receptor as external signal. This model transforms the diffusion process into the information theory measure of stochastic event completion time to get the distribution of the Mg2+ departure events. Using these molecular transport models, we next study the in-silico effects of this external trigger on the PhoPQ system. Conclusions Our results illustrate the accuracy of the proposed diffusion models in explaining the molecular/ionic transport processes inside the cell. Also, the proposed simulation framework can incorporate the stochasticity in cellular environments to a certain degree of accuracy. We expect that this scalable simulation platform will be able to model more complex biological systems with reasonable accuracy to understand their temporal dynamics. PMID:21143785

Discrete diffusion models to study the effects of Mg2+ concentration on the PhoPQ signal transduction system.

PubMed

Ghosh, Preetam; Ghosh, Samik; Basu, Kalyan; Das, Sajal K; Zhang, Chaoyang

2010-12-01

The challenge today is to develop a modeling and simulation paradigm that integrates structural, molecular and genetic data for a quantitative understanding of physiology and behavior of biological processes at multiple scales. This modeling method requires techniques that maintain a reasonable accuracy of the biological process and also reduces the computational overhead. This objective motivates the use of new methods that can transform the problem from energy and affinity based modeling to information theory based modeling. To achieve this, we transform all dynamics within the cell into a random event time, which is specified through an information domain measure like probability distribution. This allows us to use the "in silico" stochastic event based modeling approach to find the molecular dynamics of the system. In this paper, we present the discrete event simulation concept using the example of the signal transduction cascade triggered by extra-cellular Mg2+ concentration in the two component PhoPQ regulatory system of Salmonella Typhimurium. We also present a model to compute the information domain measure of the molecular transport process by estimating the statistical parameters of inter-arrival time between molecules/ions coming to a cell receptor as external signal. This model transforms the diffusion process into the information theory measure of stochastic event completion time to get the distribution of the Mg2+ departure events. Using these molecular transport models, we next study the in-silico effects of this external trigger on the PhoPQ system. Our results illustrate the accuracy of the proposed diffusion models in explaining the molecular/ionic transport processes inside the cell. Also, the proposed simulation framework can incorporate the stochasticity in cellular environments to a certain degree of accuracy. We expect that this scalable simulation platform will be able to model more complex biological systems with reasonable accuracy to understand their temporal dynamics.

A New Method for Nonlinear and Nonstationary Time Series Analysis and Its Application to the Earthquake and Building Response Records

NASA Technical Reports Server (NTRS)

Huang, Norden E.

1999-01-01

A new method for analyzing nonlinear and nonstationary data has been developed. The key part of the method is the Empirical Mode Decomposition method with which any complicated data set can be decomposed into a finite and often small number of Intrinsic Mode Functions (IMF). An IMF is defined as any function having the same numbers of zero-crossing and extrema, and also having symmetric envelopes defined by the local maxima and minima respectively. The IMF also admits well-behaved Hilbert transform. This decomposition method is adaptive, and, therefore, highly efficient. Since the decomposition is based on the local characteristic time scale of the data, it is applicable to nonlinear and nonstationary processes. With the Hilbert transform, the Intrinsic Mode Functions yield instantaneous frequencies as functions of time that give sharp identifications of imbedded structures. The final presentation of the results is an energy-frequency-time distribution, designated as the Hilbert Spectrum, Example of application of this method to earthquake and building response will be given. The results indicate those low frequency components, totally missed by the Fourier analysis, are clearly identified by the new method. Comparisons with Wavelet and window Fourier analysis show the new method offers much better temporal and frequency resolutions.

The FBI wavelet/scalar quantization standard for gray-scale fingerprint image compression

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

Bradley, J.N.; Brislawn, C.M.; Hopper, T.

1993-05-01

The FBI has recently adopted a standard for the compression of digitized 8-bit gray-scale fingerprint images. The standard is based on scalar quantization of a 64-subband discrete wavelet transform decomposition of the images, followed by Huffman coding. Novel features of the algorithm include the use of symmetric boundary conditions for transforming finite-length signals and a subband decomposition tailored for fingerprint images scanned at 500 dpi. The standard is intended for use in conjunction with ANSI/NBS-CLS 1-1993, American National Standard Data Format for the Interchange of Fingerprint Information, and the FBI`s Integrated Automated Fingerprint Identification System.

The FBI wavelet/scalar quantization standard for gray-scale fingerprint image compression

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

Bradley, J.N.; Brislawn, C.M.; Hopper, T.

1993-01-01

The FBI has recently adopted a standard for the compression of digitized 8-bit gray-scale fingerprint images. The standard is based on scalar quantization of a 64-subband discrete wavelet transform decomposition of the images, followed by Huffman coding. Novel features of the algorithm include the use of symmetric boundary conditions for transforming finite-length signals and a subband decomposition tailored for fingerprint images scanned at 500 dpi. The standard is intended for use in conjunction with ANSI/NBS-CLS 1-1993, American National Standard Data Format for the Interchange of Fingerprint Information, and the FBI's Integrated Automated Fingerprint Identification System.

Structures, Phase Transitions and Tricritical Behavior of the Hybrid Perovskite Methyl Ammonium Lead Iodide

DOE PAGES

Whitfield, P. S.; Herron, N.; Guise, W. E.; ...

2016-10-21

Here, we examine the crystal structures and structural phase transitions of the deuterated, partially deuterated and hydrogenous organic-inorganic hybrid perovskite methyl ammonium lead iodide (MAPbI 3) using time-of-flight neutron and synchrotron X-ray powder diffraction. Near 330 K the high temperature cubic phases transformed to a body-centered tetragonal phase. The variation of the order parameter Q for this transition scaled with temperature T as Q (T c-T) , where T c is the critical temperature and the exponent was close to , as predicted for a tricritical phase transition. We also observed coexistence of the cubic and tetragonal phases over amore » range of temperature in all cases, demonstrating that the phase transition was in fact first-order, although still very close to tricritical. Upon cooling further, all the tetragonal phases transformed into a low temperature orthorhombic phase around 160 K, again via a first-order phase transition. Finally, based upon these results, we discuss the impact of the structural phase transitions upon photovoltaic performance of MAPbI 3 based solar cells.« less

Chemistry Based on Renewable Raw Materials: Perspectives for a Sugar Cane-Based Biorefinery

PubMed Central

Villela Filho, Murillo; Araujo, Carlos; Bonfá, Alfredo; Porto, Weber

2011-01-01

Carbohydrates are nowadays a very competitive feedstock for the chemical industry because their availability is compatible with world-scale chemical production and their price, based on the carbon content, is comparable to that of petrochemicals. At the same time, demand is rising for biobased products. Brazilian sugar cane is a competitive feedstock source that is opening the door to a wide range of bio-based products. This essay begins with the importance of the feedstock for the chemical industry and discusses developments in sugar cane processing that lead to low cost feedstocks. Thus, sugar cane enables a new chemical industry, as it delivers a competitive raw material and a source of energy. As a result, sugar mills are being transformed into sustainable biorefineries that fully exploit the potential of sugar cane. PMID:21637329

Chemistry based on renewable raw materials: perspectives for a sugar cane-based biorefinery.

PubMed

Villela Filho, Murillo; Araujo, Carlos; Bonfá, Alfredo; Porto, Weber

2011-01-01

Carbohydrates are nowadays a very competitive feedstock for the chemical industry because their availability is compatible with world-scale chemical production and their price, based on the carbon content, is comparable to that of petrochemicals. At the same time, demand is rising for biobased products. Brazilian sugar cane is a competitive feedstock source that is opening the door to a wide range of bio-based products. This essay begins with the importance of the feedstock for the chemical industry and discusses developments in sugar cane processing that lead to low cost feedstocks. Thus, sugar cane enables a new chemical industry, as it delivers a competitive raw material and a source of energy. As a result, sugar mills are being transformed into sustainable biorefineries that fully exploit the potential of sugar cane.

Stability and chaos in Kustaanheimo-Stiefel space induced by the Hopf fibration

NASA Astrophysics Data System (ADS)

Roa, Javier; Urrutxua, Hodei; Peláez, Jesús

2016-07-01

The need for the extra dimension in Kustaanheimo-Stiefel (KS) regularization is explained by the topology of the Hopf fibration, which defines the geometry and structure of KS space. A trajectory in Cartesian space is represented by a four-dimensional manifold called the fundamental manifold. Based on geometric and topological aspects classical concepts of stability are translated to KS language. The separation between manifolds of solutions generalizes the concept of Lyapunov stability. The dimension-raising nature of the fibration transforms fixed points, limit cycles, attractive sets, and Poincaré sections to higher dimensional subspaces. From these concepts chaotic systems are studied. In strongly perturbed problems, the numerical error can break the topological structure of KS space: points in a fibre are no longer transformed to the same point in Cartesian space. An observer in three dimensions will see orbits departing from the same initial conditions but diverging in time. This apparent randomness of the integration can only be understood in four dimensions. The concept of topological stability results in a simple method for estimating the time-scale in which numerical simulations can be trusted. Ideally, all trajectories departing from the same fibre should be KS transformed to a unique trajectory in three-dimensional space, because the fundamental manifold that they constitute is unique. By monitoring how trajectories departing from one fibre separate from the fundamental manifold a critical time, equivalent to the Lyapunov time, is estimated. These concepts are tested on N-body examples: the Pythagorean problem, and an example of field stars interacting with a binary.

Ultrafast compression of graphite observed with sub-ps time resolution diffraction on LCLS

NASA Astrophysics Data System (ADS)

Armstrong, Michael; Goncharov, A.; Crowhurst, J.; Zaug, J.; Radousky, H.; Grivickas, P.; Bastea, S.; Goldman, N.; Stavrou, E.; Belof, J.; Gleason, A.; Lee, H. J.; Nagler, R.; Holtgrewe, N.; Walter, P.; Pakaprenka, V.; Nam, I.; Granados, E.; Presher, C.; Koroglu, B.

2017-06-01

We will present ps time resolution pulsed x-ray diffraction measurements of rapidly compressed highly oriented pyrolytic graphite along its basal plane at the Materials under Extreme Conditions (MEC) sector of the Linac Coherent Light Source (LCLS). These experiments explore the possibility of rapid (<100 ps time scale) material transformations occurring under very highly anisotropic compression conditions. Under such conditions, non-equilibrium mechanisms may play a role in the transformation process. We will present experimental results and simulations which explore this possibility. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Security, LLC under Contract DE-AC52-07NA27344.

Can we derive an 'exchange rate' between descriptive and preference-based outcome measures for stroke? Results from the transfer to utility (TTU) technique

PubMed Central

Mortimer, Duncan; Segal, Leonie; Sturm, Jonathan

2009-01-01

Background Stroke-specific outcome measures and descriptive measures of health-related quality of life (HRQoL) are unsuitable for informing decision-makers of the broader consequences of increasing or decreasing funding for stroke interventions. The quality-adjusted life year (QALY) provides a common metric for comparing interventions over multiple dimensions of HRQoL and mortality differentials. There are, however, many circumstances when – because of timing, lack of foresight or cost considerations – only stroke-specific or descriptive measures of health status are available and some indirect means of obtaining QALY-weights becomes necessary. In such circumstances, the use of regression-based transformations or mappings can circumvent the failure to elicit QALY-weights by allowing predicted weights to proxy for observed weights. This regression-based approach has been dubbed 'Transfer to Utility' (TTU) regression. The purpose of the present study is to demonstrate the feasibility and value of TTU regression in stroke by deriving transformations or mappings from stroke-specific and generic but descriptive measures of health status to a generic preference-based measure of HRQoL in a sample of Australians with a diagnosis of acute stroke. Findings will quantify the additional error associated with the use of condition-specific to generic transformations in stroke. Methods We used TTU regression to derive empirical transformations from three commonly used descriptive measures of health status for stroke (NIHSS, Barthel and SF-36) to a preference-based measure (AQoL) suitable for attaching QALY-weights to stroke disease states; based on 2570 observations drawn from a sample of 859 patients with stroke. Results Transformations from the SF-36 to the AQoL explained up to 71.5% of variation in observed AQoL scores. Differences between mean predicted and mean observed AQoL scores from the 'severity-specific' item- and subscale-based SF-36 algorithms and from the 'moderate to severe' index- and item-based Barthel algorithm were neither clinically nor statistically significant when 'low severity' SF-36 transformations were used to predict AQoL scores for patients in the NIHSS = 0 and NIHSS = 1–5 subgroups and when 'moderate to severe severity' transformations were used to predict AQoL scores for patients in the NIHSS ≥ 6 subgroup. In contrast, the difference between mean predicted and mean observed AQoL scores from the NIHSS algorithms and from the 'low severity' Barthel algorithms reached levels that could mask minimally important differences on the AQoL scale. Conclusion While our NIHSS to AQoL transformations proved unsuitable for most applications, our findings demonstrate that stroke-relevant outcome measures such as the SF-36 and Barthel Index can be adequately transformed to preference-based measures for the purposes of economic evaluation. PMID:19371444

Determination of knock characteristics in spark ignition engines: an approach based on ensemble empirical mode decomposition

NASA Astrophysics Data System (ADS)

Li, Ning; Yang, Jianguo; Zhou, Rui; Liang, Caiping

2016-04-01

Knock is one of the major constraints to improve the performance and thermal efficiency of spark ignition (SI) engines. It can also result in severe permanent engine damage under certain operating conditions. Based on the ensemble empirical mode decomposition (EEMD), this paper proposes a new approach to determine the knock characteristics in SI engines. By adding a uniformly distributed and finite white Gaussian noise, the EEMD can preserve signal continuity in different scales and therefore alleviates the mode-mixing problem occurring in the classic empirical mode decomposition (EMD). The feasibilities of applying the EEMD to detect the knock signatures of a test SI engine via the pressure signal measured from combustion chamber and the vibration signal measured from cylinder head are investigated. Experimental results show that the EEMD-based method is able to detect the knock signatures from both the pressure signal and vibration signal, even in initial stage of knock. Finally, by comparing the application results with those obtained by short-time Fourier transform (STFT), Wigner-Ville distribution (WVD) and discrete wavelet transform (DWT), the superiority of the EEMD method in determining knock characteristics is demonstrated.

Self-organization, transformity, and information.

PubMed

Odum, H T

1988-11-25

Ecosystems and other self-organizing systems develop system designs and mathematics that reinforce energy use, characteristically with alternate pulsing of production and consumption, increasingly recognized as the new paradigm. Insights from the energetics of ecological food chains suggest the need to redefine work, distinguishing kinds of energy with a new quantity, the transformity (energy of one type required per unit of another). Transformities may be used as an energy-scaling factor for the hierarchies of the universe including information. Solar transformities in the biosphere, expressed as solar emjoules per joule, range from one for solar insolation to trillions for categories of shared information. Resource contributions multiplied by their transformities provide a scientifically based value system for human service, environmental mitigation, foreign trade equity, public policy alternatives, and economic vitality.

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

Hu, Qin; Zhao, Lichen; Wu, Jiang

Hybrid lead halide perovskites have emerged as high-performance photovoltaic materials with their extraordinary optoelectronic properties. In particular, the remarkable device efficiency is strongly influenced by the perovskite crystallinity and the film morphology. Here, we investigate the perovskites crystallisation kinetics and growth mechanism in real time from liquid precursor continually to the final uniform film. We utilize some advanced in situ characterisation techniques including synchrotron-based grazing incident X-ray diffraction to observe crystal structure and chemical transition of perovskites. The nano-assemble model from perovskite intermediated [PbI 6] 4– cage nanoparticles to bulk polycrystals is proposed to understand perovskites formation at a molecular-more » or nano-level. A crystallisation-depletion mechanism is developed to elucidate the periodic crystallisation and the kinetically trapped morphology at a mesoscopic level. Based on these in situ dynamics studies, the whole process of the perovskites formation and transformation from the molecular to the microstructure over relevant temperature and time scales is successfully demonstrated.« less

The transformed-stationary approach: a generic and simplified methodology for non-stationary extreme value analysis

NASA Astrophysics Data System (ADS)

Mentaschi, Lorenzo; Vousdoukas, Michalis; Voukouvalas, Evangelos; Sartini, Ludovica; Feyen, Luc; Besio, Giovanni; Alfieri, Lorenzo

2016-09-01

Statistical approaches to study extreme events require, by definition, long time series of data. In many scientific disciplines, these series are often subject to variations at different temporal scales that affect the frequency and intensity of their extremes. Therefore, the assumption of stationarity is violated and alternative methods to conventional stationary extreme value analysis (EVA) must be adopted. Using the example of environmental variables subject to climate change, in this study we introduce the transformed-stationary (TS) methodology for non-stationary EVA. This approach consists of (i) transforming a non-stationary time series into a stationary one, to which the stationary EVA theory can be applied, and (ii) reverse transforming the result into a non-stationary extreme value distribution. As a transformation, we propose and discuss a simple time-varying normalization of the signal and show that it enables a comprehensive formulation of non-stationary generalized extreme value (GEV) and generalized Pareto distribution (GPD) models with a constant shape parameter. A validation of the methodology is carried out on time series of significant wave height, residual water level, and river discharge, which show varying degrees of long-term and seasonal variability. The results from the proposed approach are comparable with the results from (a) a stationary EVA on quasi-stationary slices of non-stationary series and (b) the established method for non-stationary EVA. However, the proposed technique comes with advantages in both cases. For example, in contrast to (a), the proposed technique uses the whole time horizon of the series for the estimation of the extremes, allowing for a more accurate estimation of large return levels. Furthermore, with respect to (b), it decouples the detection of non-stationary patterns from the fitting of the extreme value distribution. As a result, the steps of the analysis are simplified and intermediate diagnostics are possible. In particular, the transformation can be carried out by means of simple statistical techniques such as low-pass filters based on the running mean and the standard deviation, and the fitting procedure is a stationary one with a few degrees of freedom and is easy to implement and control. An open-source MATLAB toolbox has been developed to cover this methodology, which is available at https://github.com/menta78/tsEva/ (Mentaschi et al., 2016).

Multirate-based fast parallel algorithms for 2-D DHT-based real-valued discrete Gabor transform.

PubMed

Tao, Liang; Kwan, Hon Keung

2012-07-01

Novel algorithms for the multirate and fast parallel implementation of the 2-D discrete Hartley transform (DHT)-based real-valued discrete Gabor transform (RDGT) and its inverse transform are presented in this paper. A 2-D multirate-based analysis convolver bank is designed for the 2-D RDGT, and a 2-D multirate-based synthesis convolver bank is designed for the 2-D inverse RDGT. The parallel channels in each of the two convolver banks have a unified structure and can apply the 2-D fast DHT algorithm to speed up their computations. The computational complexity of each parallel channel is low and is independent of the Gabor oversampling rate. All the 2-D RDGT coefficients of an image are computed in parallel during the analysis process and can be reconstructed in parallel during the synthesis process. The computational complexity and time of the proposed parallel algorithms are analyzed and compared with those of the existing fastest algorithms for 2-D discrete Gabor transforms. The results indicate that the proposed algorithms are the fastest, which make them attractive for real-time image processing.

Low current extended duration spark ignition system

DOEpatents

Waters, Stephen Howard; Chan, Anthony Kok-Fai

2005-08-30

A system for firing a spark plug is disclosed. The system includes a timing controller configured to send a first timing signal and a second timing signal. The system also includes an ignition transformer having a primary winding and a secondary winding and a spark-plug that is operably associated with the secondary winding. A first switching element is disposed between the timing controller and the primary winding of the ignition transformer. The first switching element controls a supply of power to the primary winding based on the first timing signal. Also, a second switching element is disposed between the timing controller and the primary winding of the ignition transformer. The second switching element controls the supply of power to the primary winding based on the second timing signal. A method for firing a spark plug is also disclosed.

Multi-time Scale Joint Scheduling Method Considering the Grid of Renewable Energy

NASA Astrophysics Data System (ADS)

Zhijun, E.; Wang, Weichen; Cao, Jin; Wang, Xin; Kong, Xiangyu; Quan, Shuping

2018-01-01

Renewable new energy power generation prediction error like wind and light, brings difficulties to dispatch the power system. In this paper, a multi-time scale robust scheduling method is set to solve this problem. It reduces the impact of clean energy prediction bias to the power grid by using multi-time scale (day-ahead, intraday, real time) and coordinating the dispatching power output of various power supplies such as hydropower, thermal power, wind power, gas power and. The method adopts the robust scheduling method to ensure the robustness of the scheduling scheme. By calculating the cost of the abandon wind and the load, it transforms the robustness into the risk cost and optimizes the optimal uncertainty set for the smallest integrative costs. The validity of the method is verified by simulation.

The potential of transgenic green microalgae; a robust photobioreactor to produce recombinant therapeutic proteins.

PubMed

Akbari, Fariba; Eskandani, Morteza; Khosroushahi, Ahmad Yari

2014-11-01

Microalgae have been used in food, cosmetic, and biofuel industries as a natural source of lipids, vitamins, pigments and antioxidants for a long time. Green microalgae, as potent photobioreactors, can be considered as an economical expression system to produce recombinant therapeutical proteins at large-scale due to low cost of production and scaling-up capitalization owning to the inexpensive medium requirement, fast growth rate, and the ease of manipulation. These microalgae possess all benefit eukaryotic expression systems including the ability of post-translational modifications required for proper folding and stability of active proteins. Among the many items regarded as recombinant protein production, this review compares the different expression systems with green microalgae like Dunaliella by viewing the nuclear/chloroplast transformation challenges/benefits, related selection markers/reporter genes, and crucial factors/strategies affecting the increase of foreign protein expression in microalgae transformants. Some important factors were discussed regarding the increase of protein yielding in microalgae transformants including: transformation-associated genotypic modifications, endogenous regulatory factors, promoters, codon optimization, enhancer elements, and milking of recombinant protein.

The Geostationary Fourier Transform Spectrometer

NASA Technical Reports Server (NTRS)

Key, Richard; Sander, Stanley; Eldering, Annmarie; Miller, Charles; Frankenberg, Christian; Natra, Vijay; Rider, David; Blavier, Jean-Francois; Bekker, Dmitriy; Wu, Yen-Hung

2012-01-01

The Geostationary Fourier Transform Spectrometer (GeoFTS) is an imaging spectrometer designed for an earth science mission to measure key atmospheric trace gases and process tracers related to climate change and human activity. The GeoFTS instrument is a half meter cube size instrument designed to operate in geostationary orbit as a secondary "hosted" payload on a commercial geostationary satellite mission. The advantage of GEO is the ability to continuously stare at a region of the earth, enabling frequent sampling to capture the diurnal variability of biogenic fluxes and anthropogenic emissions from city to continental scales. The science goal is to obtain a process-based understanding of the carbon cycle from simultaneous high spatial resolution measurements of carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), and chlorophyll fluorescence (CF) many times per day in the near infrared spectral region to capture their spatial and temporal variations on diurnal, synoptic, seasonal and interannual time scales. The GeoFTS instrument is based on a Michelson interferometer design with a number of advanced features incorporated. Two of the most important advanced features are the focal plane arrays and the optical path difference mechanism. A breadboard GeoFTS instrument has demonstrated functionality for simultaneous measurements in the visible and IR in the laboratory and subsequently in the field at the California Laboratory for Atmospheric Remote Sensing (CLARS) observatory on Mt. Wilson overlooking the Los Angeles basin. A GeoFTS engineering model instrument is being developed which will make simultaneous visible and IR measurements under space flight like environmental conditions (thermal-vacuum at 180 K). This will demonstrate critical instrument capabilities such as optical alignment stability, interferometer modulation efficiency, and high throughput FPA signal processing. This will reduce flight instrument development risk and show that the GeoFTS design is mature and flight ready.

Development of efficient time-evolution method based on three-term recurrence relation

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

Akama, Tomoko, E-mail: a.tomo---s-b-l-r@suou.waseda.jp; Kobayashi, Osamu; Nanbu, Shinkoh, E-mail: shinkoh.nanbu@sophia.ac.jp

The advantage of the real-time (RT) propagation method is a direct solution of the time-dependent Schrödinger equation which describes frequency properties as well as all dynamics of a molecular system composed of electrons and nuclei in quantum physics and chemistry. Its applications have been limited by computational feasibility, as the evaluation of the time-evolution operator is computationally demanding. In this article, a new efficient time-evolution method based on the three-term recurrence relation (3TRR) was proposed to reduce the time-consuming numerical procedure. The basic formula of this approach was derived by introducing a transformation of the operator using the arcsine function.more » Since this operator transformation causes transformation of time, we derived the relation between original and transformed time. The formula was adapted to assess the performance of the RT time-dependent Hartree-Fock (RT-TDHF) method and the time-dependent density functional theory. Compared to the commonly used fourth-order Runge-Kutta method, our new approach decreased computational time of the RT-TDHF calculation by about factor of four, showing the 3TRR formula to be an efficient time-evolution method for reducing computational cost.« less

Development of a Scale to Explore Technology Literacy Skills of Turkish 8th Graders

ERIC Educational Resources Information Center

Misirli, Zeynel A.; Akbulut, Yavuz

2013-01-01

The use of emerging technologies shape learners' knowledge creation and transformation processes. In this regard, this study aimed to develop a scale to investigate 8 th graders' competencies regarding the educational technology standards based on ISTE-NETS. After a review of relevant literature, an item pool was prepared. The pool was improved…

An Evaluation of a New Method of IRT Scaling

ERIC Educational Resources Information Center

Ragland, Shelley

2010-01-01

In order to be able to fairly compare scores derived from different forms of the same test within the Item Response Theory framework, all individual item parameters must be on the same scale. A new approach, the RPA method, which is based on transformations of predicted score distributions was evaluated here and was shown to produce results…

Deterministic object tracking using Gaussian ringlet and directional edge features

NASA Astrophysics Data System (ADS)

Krieger, Evan W.; Sidike, Paheding; Aspiras, Theus; Asari, Vijayan K.

2017-10-01

Challenges currently existing for intensity-based histogram feature tracking methods in wide area motion imagery (WAMI) data include object structural information distortions, background variations, and object scale change. These issues are caused by different pavement or ground types and from changing the sensor or altitude. All of these challenges need to be overcome in order to have a robust object tracker, while attaining a computation time appropriate for real-time processing. To achieve this, we present a novel method, Directional Ringlet Intensity Feature Transform (DRIFT), which employs Kirsch kernel filtering for edge features and a ringlet feature mapping for rotational invariance. The method also includes an automatic scale change component to obtain accurate object boundaries and improvements for lowering computation times. We evaluated the DRIFT algorithm on two challenging WAMI datasets, namely Columbus Large Image Format (CLIF) and Large Area Image Recorder (LAIR), to evaluate its robustness and efficiency. Additional evaluations on general tracking video sequences are performed using the Visual Tracker Benchmark and Visual Object Tracking 2014 databases to demonstrate the algorithms ability with additional challenges in long complex sequences including scale change. Experimental results show that the proposed approach yields competitive results compared to state-of-the-art object tracking methods on the testing datasets.

Power-law behaviour evaluation from foreign exchange market data using a wavelet transform method

NASA Astrophysics Data System (ADS)

Wei, H. L.; Billings, S. A.

2009-09-01

Numerous studies in the literature have shown that the dynamics of many time series including observations in foreign exchange markets exhibit scaling behaviours. A simple new statistical approach, derived from the concept of the continuous wavelet transform correlation function (WTCF), is proposed for the evaluation of power-law properties from observed data. The new method reveals that foreign exchange rates obey power-laws and thus belong to the class of self-similarity processes.

Functional transformations of odor inputs in the mouse olfactory bulb.

PubMed

Adam, Yoav; Livneh, Yoav; Miyamichi, Kazunari; Groysman, Maya; Luo, Liqun; Mizrahi, Adi

2014-01-01

Sensory inputs from the nasal epithelium to the olfactory bulb (OB) are organized as a discrete map in the glomerular layer (GL). This map is then modulated by distinct types of local neurons and transmitted to higher brain areas via mitral and tufted cells. Little is known about the functional organization of the circuits downstream of glomeruli. We used in vivo two-photon calcium imaging for large scale functional mapping of distinct neuronal populations in the mouse OB, at single cell resolution. Specifically, we imaged odor responses of mitral cells (MCs), tufted cells (TCs) and glomerular interneurons (GL-INs). Mitral cells population activity was heterogeneous and only mildly correlated with the olfactory receptor neuron (ORN) inputs, supporting the view that discrete input maps undergo significant transformations at the output level of the OB. In contrast, population activity profiles of TCs were dense, and highly correlated with the odor inputs in both space and time. Glomerular interneurons were also highly correlated with the ORN inputs, but showed higher activation thresholds suggesting that these neurons are driven by strongly activated glomeruli. Temporally, upon persistent odor exposure, TCs quickly adapted. In contrast, both MCs and GL-INs showed diverse temporal response patterns, suggesting that GL-INs could contribute to the transformations MCs undergo at slow time scales. Our data suggest that sensory odor maps are transformed by TCs and MCs in different ways forming two distinct and parallel information streams.

Laplace-transformed atomic orbital-based Møller–Plesset perturbation theory for relativistic two-component Hamiltonians

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

Helmich-Paris, Benjamin, E-mail: b.helmichparis@vu.nl; Visscher, Lucas, E-mail: l.visscher@vu.nl; Repisky, Michal, E-mail: michal.repisky@uit.no

2016-07-07

We present a formulation of Laplace-transformed atomic orbital-based second-order Møller–Plesset perturbation theory (MP2) energies for two-component Hamiltonians in the Kramers-restricted formalism. This low-order scaling technique can be used to enable correlated relativistic calculations for large molecular systems. We show that the working equations to compute the relativistic MP2 energy differ by merely a change of algebra (quaternion instead of real) from their non-relativistic counterparts. With a proof-of-principle implementation we study the effect of the nuclear charge on the magnitude of half-transformed integrals and show that for light elements spin-free and spin-orbit MP2 energies are almost identical. Furthermore, we investigate themore » effect of separation of charge distributions on the Coulomb and exchange energy contributions, which show the same long-range decay with the inter-electronic/atomic distance as for non-relativistic MP2. A linearly scaling implementation is possible if the proper distance behavior is introduced to the quaternion Schwarz-type estimates as for non-relativistic MP2.« less

A Methodology for the Parametric Reconstruction of Non-Steady and Noisy Meteorological Time Series

NASA Astrophysics Data System (ADS)

Rovira, F.; Palau, J. L.; Millán, M.

2009-09-01

Climatic and meteorological time series often show some persistence (in time) in the variability of certain features. One could regard annual, seasonal and diurnal time variability as trivial persistence in the variability of some meteorological magnitudes (as, e.g., global radiation, air temperature above surface, etc.). In these cases, the traditional Fourier transform into frequency space will show the principal harmonics as the components with the largest amplitude. Nevertheless, meteorological measurements often show other non-steady (in time) variability. Some fluctuations in measurements (at different time scales) are driven by processes that prevail on some days (or months) of the year but disappear on others. By decomposing a time series into time-frequency space through the continuous wavelet transformation, one is able to determine both the dominant modes of variability and how those modes vary in time. This study is based on a numerical methodology to analyse non-steady principal harmonics in noisy meteorological time series. This methodology combines both the continuous wavelet transform and the development of a parametric model that includes the time evolution of the principal and the most statistically significant harmonics of the original time series. The parameterisation scheme proposed in this study consists of reproducing the original time series by means of a statistically significant finite sum of sinusoidal signals (waves), each defined by using the three usual parameters: amplitude, frequency and phase. To ensure the statistical significance of the parametric reconstruction of the original signal, we propose a standard statistical t-student analysis of the confidence level of the amplitude in the parametric spectrum for the different wave components. Once we have assured the level of significance of the different waves composing the parametric model, we can obtain the statistically significant principal harmonics (in time) of the original time series by using the Fourier transform of the modelled signal. Acknowledgements The CEAM Foundation is supported by the Generalitat Valenciana and BANCAIXA (València, Spain). This study has been partially funded by the European Commission (FP VI, Integrated Project CIRCE - No. 036961) and by the Ministerio de Ciencia e Innovación, research projects "TRANSREG” (CGL2007-65359/CLI) and "GRACCIE” (CSD2007-00067, Program CONSOLIDER-INGENIO 2010).

Efficient regeneration and improved sonication-assisted Agrobacterium transformation (SAAT) method for Catharanthus roseus.

PubMed

Alam, Pravej; Khan, Zainul Abdeen; Abdin, Malik Zainul; Khan, Jawaid A; Ahmad, Parvaiz; Elkholy, Shereen F; Sharaf-Eldin, Mahmoud A

2017-05-01

Catharanthus roseus is an important medicinal plant known for its pharmacological qualities such as antimicrobial, anticancerous, antifeedant, antisterility, antidiabetic activities. More than 130 bioactive compounds like vinblastine, vindoline and vincristine have been synthesized in this plant. Extensive studies have been carried out for optimization regeneration and transformation protocols. Most of the protocol described are laborious and time-consuming. Due to sophisticated protocol of regeneration and genetic transformation, the production of these bioactive molecules is less and not feasible to be commercialized worldwide. Here we have optimized the efficient protocol for regeneration and transformation to minimize the time scale and enhance the transformation frequency through Agrobacterium and sonication-assisted transformation (SAAT) method. In this study, hypocotyl explants responded best for maximal production of transformed shoots. The callus percentage were recorded 52% with 1.0 mg L -1 (BAP) and 0.5 mg L -1 (NAA) while 80% shoot percentage obtained with 4.0 mg L -1 (BAP) and 0.05 mg L -1 (NAA). The microscopic studies revealed that the expression of GFP was clearly localized in leaf tissue of the C. roseus after transformation of pRepGFP0029 construct. Consequently, transformation efficiency was revealed on the basis of GFP localization. The transformation efficiency of SAAT method was 6.0% comparable to 3.5% as conventional method. Further, PCR analysis confirmed the integration of the nptII gene in the transformed plantlets of C. roseus.

Biomolecular surface construction by PDE transform

PubMed Central

Zheng, Qiong; Yang, Siyang; Wei, Guo-Wei

2011-01-01

This work proposes a new framework for the surface generation based on the partial differential equation (PDE) transform. The PDE transform has recently been introduced as a general approach for the mode decomposition of images, signals, and data. It relies on the use of arbitrarily high order PDEs to achieve the time-frequency localization, control the spectral distribution, and regulate the spatial resolution. The present work provides a new variational derivation of high order PDE transforms. The fast Fourier transform is utilized to accomplish the PDE transform so as to avoid stringent stability constraints in solving high order PDEs. As a consequence, the time integration of high order PDEs can be done efficiently with the fast Fourier transform. The present approach is validated with a variety of test examples in two and three-dimensional settings. We explore the impact of the PDE transform parameters, such as the PDE order and propagation time, on the quality of resulting surfaces. Additionally, we utilize a set of 10 proteins to compare the computational efficiency of the present surface generation method and the MSMS approach in Cartesian meshes. Moreover, we analyze the present method by examining some benchmark indicators of biomolecular surface, i.e., surface area, surface enclosed volume, solvation free energy and surface electrostatic potential. A test set of 13 protein molecules is used in the present investigation. The electrostatic analysis is carried out via the Poisson-Boltzmann equation model. To further demonstrate the utility of the present PDE transform based surface method, we solve the Poisson-Nernst-Planck (PNP) equations with a PDE transform surface of a protein. Second order convergence is observed for the electrostatic potential and concentrations. Finally, to test the capability and efficiency of the present PDE transform based surface generation method, we apply it to the construction of an excessively large biomolecule, a virus surface capsid. Virus surface morphologies of different resolutions are attained by adjusting the propagation time. Therefore, the present PDE transform provides a multiresolution analysis in the surface visualization. Extensive numerical experiment and comparison with an established surface model indicate that the present PDE transform is a robust, stable and efficient approach for biomolecular surface generation in Cartesian meshes. PMID:22582140

Length-Scale-Dependent Phase Transformation of LiFePO4 : An In situ and Operando Study Using Micro-Raman Spectroscopy and XRD.

PubMed

Siddique, N A; Salehi, Amir; Wei, Zi; Liu, Dong; Sajjad, Syed D; Liu, Fuqiang

2015-08-03

The charge and discharge of lithium ion batteries are often accompanied by electrochemically driven phase-transformation processes. In this work, two in situ and operando methods, that is, micro-Raman spectroscopy and X-ray diffraction (XRD), have been combined to study the phase-transformation process in LiFePO4 at two distinct length scales, namely, particle-level scale (∼1 μm) and macroscopic scale (∼several cm). In situ Raman studies revealed a discrete mode of phase transformation at the particle level. Besides, the preferred electrochemical transport network, particularly the carbon content, was found to govern the sequence of phase transformation among particles. In contrast, at the macroscopic level, studies conducted at four different discharge rates showed a continuous but delayed phase transformation. These findings uncovered the intricate phase transformation in LiFePO4 and potentially offer valuable insights into optimizing the length-scale-dependent properties of battery materials. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Pesticide fate on catchment scale: conceptual modelling of stream CSIA data

NASA Astrophysics Data System (ADS)

Lutz, Stefanie R.; van der Velde, Ype; Elsayed, Omniea F.; Imfeld, Gwenaël; Lefrancq, Marie; Payraudeau, Sylvain; van Breukelen, Boris M.

2017-10-01

Compound-specific stable isotope analysis (CSIA) has proven beneficial in the characterization of contaminant degradation in groundwater, but it has never been used to assess pesticide transformation on catchment scale. This study presents concentration and carbon CSIA data of the herbicides S-metolachlor and acetochlor from three locations (plot, drain, and catchment outlets) in a 47 ha agricultural catchment (Bas-Rhin, France). Herbicide concentrations at the catchment outlet were highest (62 µg L-1) in response to an intense rainfall event following herbicide application. Increasing δ13C values of S-metolachlor and acetochlor by more than 2 ‰ during the study period indicated herbicide degradation. To assist the interpretation of these data, discharge, concentrations, and δ13C values of S-metolachlor were modelled with a conceptual mathematical model using the transport formulation by travel-time distributions. Testing of different model setups supported the assumption that degradation half-lives (DT50) increase with increasing soil depth, which can be straightforwardly implemented in conceptual models using travel-time distributions. Moreover, model calibration yielded an estimate of a field-integrated isotopic enrichment factor as opposed to laboratory-based assessments of enrichment factors in closed systems. Thirdly, the Rayleigh equation commonly applied in groundwater studies was tested by our model for its potential to quantify degradation on catchment scale. It provided conservative estimates on the extent of degradation as occurred in stream samples. However, largely exceeding the simulated degradation within the entire catchment, these estimates were not representative of overall degradation on catchment scale. The conceptual modelling approach thus enabled us to upscale sample-based CSIA information on degradation to the catchment scale. Overall, this study demonstrates the benefit of combining monitoring and conceptual modelling of concentration and CSIA data and advocates the use of travel-time distributions for assessing pesticide fate and transport on catchment scale.

Detecting and characterizing high-frequency oscillations in epilepsy: a case study of big data analysis

NASA Astrophysics Data System (ADS)

Huang, Liang; Ni, Xuan; Ditto, William L.; Spano, Mark; Carney, Paul R.; Lai, Ying-Cheng

2017-01-01

We develop a framework to uncover and analyse dynamical anomalies from massive, nonlinear and non-stationary time series data. The framework consists of three steps: preprocessing of massive datasets to eliminate erroneous data segments, application of the empirical mode decomposition and Hilbert transform paradigm to obtain the fundamental components embedded in the time series at distinct time scales, and statistical/scaling analysis of the components. As a case study, we apply our framework to detecting and characterizing high-frequency oscillations (HFOs) from a big database of rat electroencephalogram recordings. We find a striking phenomenon: HFOs exhibit on-off intermittency that can be quantified by algebraic scaling laws. Our framework can be generalized to big data-related problems in other fields such as large-scale sensor data and seismic data analysis.

A singlechip-computer-controlled conductivity meter based on conductance-frequency transformation

NASA Astrophysics Data System (ADS)

Chen, Wenxiang; Hong, Baocai

2005-02-01

A portable conductivity meter controlled by singlechip computer was designed. The instrument uses conductance-frequency transformation method to measure the conductivity of solution. The circuitry is simple and reliable. Another feature of the instrument is that the temperature compensation is realised by changing counting time of the timing counter. The theoretical based and the usage of temperature compensation are narrated.

The crack detection algorithm of pavement image based on edge information

NASA Astrophysics Data System (ADS)

Yang, Chunde; Geng, Mingyue

2018-05-01

As the images of pavement cracks are affected by a large amount of complicated noises, such as uneven illumination and water stains, the detected cracks are discontinuous and the main body information at the edge of the cracks is easily lost. In order to solve the problem, a crack detection algorithm in pavement image based on edge information is proposed. Firstly, the image is pre-processed by the nonlinear gray-scale transform function and reconstruction filter to enhance the linear characteristic of the crack. At the same time, an adaptive thresholding method is designed to coarsely extract the cracks edge according to the gray-scale gradient feature and obtain the crack gradient information map. Secondly, the candidate edge points are obtained according to the gradient information, and the edge is detected based on the single pixel percolation processing, which is improved by using the local difference between pixels in the fixed region. Finally, complete crack is obtained by filling the crack edge. Experimental results show that the proposed method can accurately detect pavement cracks and preserve edge information.

Remote sensing of soil organic matter of farmland with hyperspectral image

NASA Astrophysics Data System (ADS)

Gu, Xiaohe; Wang, Lei; Yang, Guijun; Zhang, Liyan

2017-10-01

Monitoring soil organic matter (SOM) of cultivated land quantitively and mastering its spatial change are helpful for fertility adjustment and sustainable development of agriculture. The study aimed to analyze the response between SOM and reflectivity of hyperspectral image with different pixel size and develop the optimal model of estimating SOM with imaging spectral technology. The wavelet transform method was used to analyze the correlation between the hyperspectral reflectivity and SOM. Then the optimal pixel size and sensitive wavelet feature scale were screened to develop the inversion model of SOM. Result showed that wavelet transform of soil hyperspectrum was help to improve the correlation between the wavelet features and SOM. In the visible wavelength range, the susceptible wavelet features of SOM mainly concentrated 460 603 nm. As the wavelength increased, the wavelet scale corresponding correlation coefficient increased maximum and then gradually decreased. In the near infrared wavelength range, the susceptible wavelet features of SOM mainly concentrated 762 882 nm. As the wavelength increased, the wavelet scale gradually decreased. The study developed multivariate model of continuous wavelet transforms by the method of stepwise linear regression (SLR). The CWT-SLR models reached higher accuracies than those of univariate models. With the resampling scale increasing, the accuracies of CWT-SLR models gradually increased, while the determination coefficients (R2) fluctuated from 0.52 to 0.59. The R2 of 5*5 scale reached highest (0.5954), while the RMSE reached lowest (2.41 g/kg). It indicated that multivariate model based on continuous wavelet transform had better ability for estimating SOM than univariate model.

Power density of piezoelectric transformers improved using a contact heat transfer structure.

PubMed

Shao, Wei Wei; Chen, Li Juan; Pan, Cheng Liang; Liu, Yong Bin; Feng, Zhi Hua

2012-01-01

Based on contact heat transfer, a novel method to increase power density of piezoelectric transformers is proposed. A heat transfer structure is realized by directly attaching a dissipater to the piezoelectric transformer plate. By maintaining the vibration mode of the transformer and limiting additional energy losses from the contact interface, an appropriate design can improve power density of the transformer on a large scale, resulting from effective suppression of its working temperature rise. A prototype device was fabricated from a rectangular piezoelectric transformer, a copper heat transfer sheet, a thermal grease insulation pad, and an aluminum heat radiator. The experimental results show the transformer maintains a maximum power density of 135 W/cm(3) and an efficiency of 90.8% with a temperature rise of less than 10 °C after more than 36 h, without notable changes in performance. © 2012 IEEE

Sub-synchronous resonance damping using high penetration PV plant

NASA Astrophysics Data System (ADS)

Khayyatzadeh, M.; Kazemzadeh, R.

2017-02-01

The growing need to the clean and renewable energy has led to the fast development of transmission voltage-level photovoltaic (PV) plants all over the world. These large scale PV plants are going to be connected to power systems and one of the important subjects that should be investigated is the impact of these plants on the power system stability. Can large scale PV plants help to damp sub-synchronous resonance (SSR) and how? In this paper, this capability of a large scale PV plant is investigated. The IEEE Second Benchmark Model aggregated with a PV plant is utilized as the case study. A Wide Area Measurement System (WAMS) based conventional damping controller is designed and added to the main control loop of PV plant in order to damp the SSR and also investigation of the destructive effect of time delay in remote feedback signal. A new optimization algorithm called teaching-learning-based-optimization (TLBO) algorithm has been used for managing the optimization problems. Fast Furrier Transformer (FFT) analysis and also transient simulations of detailed nonlinear system are considered to investigate the performance of the controller. Robustness of the proposed system has been analyzed by facing the system with disturbances leading to significant changes in generator and power system operating point, fault duration time and PV plant generated power. All the simulations are carried out in MATLAB/SIMULINK environment.

Automatic location of L/H transition times for physical studies with a large statistical basis

NASA Astrophysics Data System (ADS)

González, S.; Vega, J.; Murari, A.; Pereira, A.; Dormido-Canto, S.; Ramírez, J. M.; contributors, JET-EFDA

2012-06-01

Completely automatic techniques to estimate and validate L/H transition times can be essential in L/H transition analyses. The generation of databases with hundreds of transition times and without human intervention is an important step to accomplish (a) L/H transition physics analysis, (b) validation of L/H theoretical models and (c) creation of L/H scaling laws. An entirely unattended methodology is presented in this paper to build large databases of transition times in JET using time series. The proposed technique has been applied to a dataset of 551 JET discharges between campaigns C21 and C26. A prediction with discharges that show a clear signature in time series is made through the locating properties of the wavelet transform. It is an accurate prediction and the uncertainty interval is ±3.2 ms. The discharges with a non-clear pattern in the time series use an L/H mode classifier based on discharges with a clear signature. In this case, the estimation error shows a distribution with mean and standard deviation of 27.9 ms and 37.62 ms, respectively. Two different regression methods have been applied to the measurements acquired at the transition times identified by the automatic system. The obtained scaling laws for the threshold power are not significantly different from those obtained using the data at the transition times determined manually by the experts. The automatic methods allow performing physical studies with a large number of discharges, showing, for example, that there are statistically different types of transitions characterized by different scaling laws.

Model-based occluded object recognition using Petri nets

NASA Astrophysics Data System (ADS)

Zhou, Chuan; Hura, Gurdeep S.

1998-09-01

This paper discusses the use of Petri nets to model the process of the object matching between an image and a model under different 2D geometric transformations. This transformation finds its applications in sensor-based robot control, flexible manufacturing system and industrial inspection, etc. A description approach for object structure is presented by its topological structure relation called Point-Line Relation Structure (PLRS). It has been shown how Petri nets can be used to model the matching process, and an optimal or near optimal matching can be obtained by tracking the reachability graph of the net. The experiment result shows that object can be successfully identified and located under 2D transformation such as translations, rotations, scale changes and distortions due to object occluded partially.

A New Minimum Trees-Based Approach for Shape Matching with Improved Time Computing: Application to Graphical Symbols Recognition

NASA Astrophysics Data System (ADS)

Franco, Patrick; Ogier, Jean-Marc; Loonis, Pierre; Mullot, Rémy

Recently we have developed a model for shape description and matching. Based on minimum spanning trees construction and specifics stages like the mixture, it seems to have many desirable properties. Recognition invariance in front shift, rotated and noisy shape was checked through median scale tests related to GREC symbol reference database. Even if extracting the topology of a shape by mapping the shortest path connecting all the pixels seems to be powerful, the construction of graph induces an expensive algorithmic cost. In this article we discuss on the ways to reduce time computing. An alternative solution based on image compression concepts is provided and evaluated. The model no longer operates in the image space but in a compact space, namely the Discrete Cosine space. The use of block discrete cosine transform is discussed and justified. The experimental results led on the GREC2003 database show that the proposed method is characterized by a good discrimination power, a real robustness to noise with an acceptable time computing.

Fast Gaussian kernel learning for classification tasks based on specially structured global optimization.

PubMed

Zhong, Shangping; Chen, Tianshun; He, Fengying; Niu, Yuzhen

2014-09-01

For a practical pattern classification task solved by kernel methods, the computing time is mainly spent on kernel learning (or training). However, the current kernel learning approaches are based on local optimization techniques, and hard to have good time performances, especially for large datasets. Thus the existing algorithms cannot be easily extended to large-scale tasks. In this paper, we present a fast Gaussian kernel learning method by solving a specially structured global optimization (SSGO) problem. We optimize the Gaussian kernel function by using the formulated kernel target alignment criterion, which is a difference of increasing (d.i.) functions. Through using a power-transformation based convexification method, the objective criterion can be represented as a difference of convex (d.c.) functions with a fixed power-transformation parameter. And the objective programming problem can then be converted to a SSGO problem: globally minimizing a concave function over a convex set. The SSGO problem is classical and has good solvability. Thus, to find the global optimal solution efficiently, we can adopt the improved Hoffman's outer approximation method, which need not repeat the searching procedure with different starting points to locate the best local minimum. Also, the proposed method can be proven to converge to the global solution for any classification task. We evaluate the proposed method on twenty benchmark datasets, and compare it with four other Gaussian kernel learning methods. Experimental results show that the proposed method stably achieves both good time-efficiency performance and good classification performance. Copyright © 2014 Elsevier Ltd. All rights reserved.

Design and performance of a pulse transformer based on Fe-based nanocrystalline core.

PubMed

Yi, Liu; Xibo, Feng; Lin, Fuchang

2011-08-01

A dry-type pulse transformer based on Fe-based nanocrystalline core with a load of 0.88 nF, output voltage of more than 65 kV, and winding ratio of 46 is designed and constructed. The dynamic characteristics of Fe-based nanocrystalline core under the impulse with the pulse width of several microseconds were studied. The pulse width and incremental flux density have an important effect on the pulse permeability, so the pulse permeability is measured under a certain pulse width and incremental flux density. The minimal volume of the toroidal pulse transformer core is determined by the coupling coefficient, the capacitors of the resonant charging circuit, incremental flux density, and pulse permeability. The factors of the charging time, ratio, and energy transmission efficiency in the resonant charging circuit based on magnetic core-type pulse transformer are analyzed. Experimental results of the pulse transformer are in good agreement with the theoretical calculation. When the primary capacitor is 3.17 μF and charge voltage is 1.8 kV, a voltage across the secondary capacitor of 0.88 nF with peak value of 68.5 kV, rise time (10%-90%) of 1.80 μs is obtained.

Numerical inverse Laplace transformation for determining the system response of linear systems in the time domain

NASA Technical Reports Server (NTRS)

Friedrich, R.; Drewelow, W.

1978-01-01

An algorithm is described that is based on the method of breaking the Laplace transform down into partial fractions which are then inverse-transformed separately. The sum of the resulting partial functions is the wanted time function. Any problems caused by equation system forms are largely limited by appropriate normalization using an auxiliary parameter. The practical limits of program application are reached when the degree of the denominator of the Laplace transform is seven to eight.

Detection and reconstruction of large scale flow structures in a river by means of empirical mode decomposition combined with Hilbert transform

NASA Astrophysics Data System (ADS)

Franca, Mário J.; Lemmin, Ulrich

2014-05-01

The occurrence of large scale flow structures (LSFS) coherently organized throughout the flow depth has been reported in field and laboratory experiments of flows over gravel beds, especially under low relative submergence conditions. In these, the instantaneous velocity is synchronized over the whole vertical profile oscillating at a low frequency above or below the time-averaged value. The detection of large scale coherently organized regions in the flow field is often difficult since it requires detailed simultaneous observations of the flow velocities at several levels. The present research avoids the detection problem by using an Acoustic Doppler Velocity Profiler (ADVP), which permits measuring three-dimensional velocities quasi-simultaneously over the full water column. Empirical mode decomposition (EMD) combined with the application of the Hilbert transform is then applied to the instantaneous velocity data to detect and isolate LSFS. The present research was carried out in a Swiss river with low relative submergence of 2.9, herein defined as h/D50, (where h is the mean flow depth and D50 the bed grain size diameter for which 50% of the grains have smaller diameters). 3D ADVP instantaneous velocity measurements were made on a 3x5 rectangular horizontal grid (x-y). Fifteen velocity profiles were equally spaced in the spanwise direction with a distance of 10 cm, and in the streamwise direction with a distance of 15 cm. The vertical resolution of the measurements is roughly 0.5 cm. A measuring grid covering a 3D control volume was defined. The instantaneous velocity profiles were measured for 3.5 min with a sampling frequency of 26 Hz. Oscillating LSFS are detected and isolated in the instantaneous velocity signal of the 15 measured profiles. Their 3D cycle geometry is reconstructed and investigated through phase averaging based on the identification of the instantaneous signal phase (related to the Hilbert transform) applied to the original raw signal. Results for all the profiles are consistent and indicate clearly the presence of LSFS throughout the flow depth with impact on the three components of the velocity profile and on the bed friction velocity. A high correlation of the movement is found throughout the flow depth, thus corroborating the hypothesis of large-scale coherent motion evolving over the whole water depth. These latter are characterized in terms of period, horizontal scale and geometry. The high spatial and temporal resolution of our ADVP was crucial for obtaining comprehensive results on coherent structures dynamics. EMD combined with the Hilbert transform have previously been successfully applied to geophysical flow studies. Here we show that this method can also be used for the analysis of river dynamics. In particular, we demonstrate that a clean, well-behaved intrinsic mode function can be obtained from a noisy velocity time series that allowed a precise determination of the vertical structure of the coherent structures. The phase unwrapping of the UMR and the identification of the phase related velocity components brings new insight into the flow dynamics Research supported by the Swiss National Science Foundation (2000-063818). KEY WORDS: large scale flow structures (LSFS); gravel-bed rivers; empirical mode decomposition; Hilbert transform

Ultrafast dynamic response of single-crystal β-HMX (octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine)

NASA Astrophysics Data System (ADS)

Zaug, Joseph M.; Austin, Ryan A.; Armstrong, Michael R.; Crowhurst, Jonathan C.; Goldman, Nir; Ferranti, Louis; Saw, Cheng K.; Swan, Raymond A.; Gross, Richard; Fried, Laurence E.

2018-05-01

We report experimental and computational studies of shock wave dynamics in single-crystal β-HMX on an ultrafast time scale. Here, a laser-based compression drive (˜1 ns in duration; stresses of up to ˜40 GPa) is used to propagate shock waves normal to the (110) and (010) lattice planes. Ultrafast time-domain interferometry measurements reveal distinct, time-dependent relationships between the shock wave velocity and particle velocity for each crystal orientation, which suggest evolving physical processes on a sub-nanosecond time scale. To help interpret the experimental data, elastic shock wave response was simulated using a finite-strain model of crystal thermoelasticity. At early propagation times (<500 ps), the model is in agreement with the data, which indicates that the mechanical response is dominated by thermoelastic deformation. The model agreement depends on the inclusion of nonlinear elastic effects in both the spherical and deviatoric stress-strain responses. This is achieved by employing an equation-of-state and a pressure-dependent stiffness tensor, which was computed via atomistic simulation. At later times (>500 ps), the crystal samples exhibit signatures of inelastic deformation, structural phase transformation, or chemical reaction, depending on the direction of wave propagation.

Final Report, University of California Merced: Uranium and strontium fate in waste-weathered sediments: Scaling of molecular processes to predict reactive transport

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

Chorover, Jon; Mueller, Karl; O'Day, Peggy Anne

2016-06-30

Objectives of the Project: 1. Determine the process coupling that occurs between mineral transformation and contaminant (U and Sr) speciation in acid-uranium waste weathered Hanford sediments. 2. Establish linkages between molecular-scale contaminant speciation and meso-scale contaminant lability, release and reactive transport. 3. Make conjunctive use of molecular- to bench-scale data to constrain the development of a mechanistic, reactive transport model that includes coupling of contaminant sorption-desorption and mineral transformation reactions. Hypotheses Tested: Uranium and strontium speciation in legacy sediments from the U-8 and U-12 Crib sites can be reproduced in bench-scale weathering experiments conducted on unimpacted Hanford sediments from themore » same formations; Reactive transport modeling of future uranium and strontium releases from the vadose zone of acid-waste weathered sediments can be effectively constrained by combining molecular-scale information on contaminant bonding environment with grain-scale information on contaminant phase partitioning, and meso-scale kinetic data on contaminant release from the waste-weathered porous media; Although field contamination and laboratory experiments differ in their diagenetic time scales (decades for field vs. months to years for lab), sediment dissolution, neophase nucleation, and crystal growth reactions that occur during the initial disequilibrium induced by waste-sediment interaction leave a strong imprint that persists over subsequent longer-term equilibration time scales and, therefore, give rise to long-term memory effects. Enabling Capabilities Developed: Our team developed an iterative measure-model approach that is broadly applicable to elucidate the mechanistic underpinnings of reactive contaminant transport in geomedia subject to active weathering.« less

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

Lue Xing; Sun Kun; Wang Pan

In the framework of Bell-polynomial manipulations, under investigation hereby are three single-field bilinearizable equations: the (1+1)-dimensional shallow water wave model, Boiti-Leon-Manna-Pempinelli model, and (2+1)-dimensional Sawada-Kotera model. Based on the concept of scale invariance, a direct and unifying Bell-polynomial scheme is employed to achieve the Baecklund transformations and Lax pairs associated with those three soliton equations. Note that the Bell-polynomial expressions and Bell-polynomial-typed Baecklund transformations for those three soliton equations can be, respectively, cast into the bilinear equations and bilinear Baecklund transformations with symbolic computation. Consequently, it is also shown that the Bell-polynomial-typed Baecklund transformations can be linearized into the correspondingmore » Lax pairs.« less

Real-time ultrasound image classification for spine anesthesia using local directional Hadamard features.

PubMed

Pesteie, Mehran; Abolmaesumi, Purang; Ashab, Hussam Al-Deen; Lessoway, Victoria A; Massey, Simon; Gunka, Vit; Rohling, Robert N

2015-06-01

Injection therapy is a commonly used solution for back pain management. This procedure typically involves percutaneous insertion of a needle between or around the vertebrae, to deliver anesthetics near nerve bundles. Most frequently, spinal injections are performed either blindly using palpation or under the guidance of fluoroscopy or computed tomography. Recently, due to the drawbacks of the ionizing radiation of such imaging modalities, there has been a growing interest in using ultrasound imaging as an alternative. However, the complex spinal anatomy with different wave-like structures, affected by speckle noise, makes the accurate identification of the appropriate injection plane difficult. The aim of this study was to propose an automated system that can identify the optimal plane for epidural steroid injections and facet joint injections. A multi-scale and multi-directional feature extraction system to provide automated identification of the appropriate plane is proposed. Local Hadamard coefficients are obtained using the sequency-ordered Hadamard transform at multiple scales. Directional features are extracted from local coefficients which correspond to different regions in the ultrasound images. An artificial neural network is trained based on the local directional Hadamard features for classification. The proposed method yields distinctive features for classification which successfully classified 1032 images out of 1090 for epidural steroid injection and 990 images out of 1052 for facet joint injection. In order to validate the proposed method, a leave-one-out cross-validation was performed. The average classification accuracy for leave-one-out validation was 94 % for epidural and 90 % for facet joint targets. Also, the feature extraction time for the proposed method was 20 ms for a native 2D ultrasound image. A real-time machine learning system based on the local directional Hadamard features extracted by the sequency-ordered Hadamard transform for detecting the laminae and facet joints in ultrasound images has been proposed. The system has the potential to assist the anesthesiologists in quickly finding the target plane for epidural steroid injections and facet joint injections.

Fourier-Domain Shift Matching: A Robust Time-of-Flight Approach for Shear Wave Speed Estimation.

PubMed

Rosen, David; Jiang, Jingfeng

2018-05-01

Our primary objective of this work was to design and test a new time-of-flight (TOF) method that allows measurements of shear wave speed (SWS) following impulsive excitation in soft tissues. Particularly, under the assumption of the local plane shear wave, this work named the Fourier-domain shift matching (FDSM) method, estimates SWS by aligning a series of shear waveforms either temporally or spatially using a solution space deduced by characteristic curves of the well-known 1-D wave equation. The proposed SWS estimation method was tested using computer-simulated data, and tissue-mimicking phantom and ex vivo tissue experiments. Its performance was then compared with three other known TOF methods: lateral time-to-peak (TTP) method with robust random sampling consensus (RANSAC) fitting method, Radon sum transformation method, and a modified cross correlation method. Hereafter, these three TOF methods are referred to as the TTP-RANSAC, Radon sum, and X-corr methods, respectively. In addition to an adapted form of the 2-D Fourier transform (2-D FT)-based method in which the (group) SWS was approximated by averaging phase SWS values was considered for comparison. Based on data evaluated, we found that the overall performance of the above-mentioned temporal implementation of the proposed FDSM method was most similar to the established Radon sum method (correlation = 0.99, scale factor = 1.03, and mean difference = 0.07 m/s), and the 2-D FT (correlation = 0.98, scale factor = 1.00, and mean difference = 0.10 m/s) at high signal quality. However, results obtained from the 2-D FT method diverged (correlation = 0.201) from these of the proposed temporal implementation in the presence of diminished signal quality, whereas the agreement between the Radon sum approach and the proposed temporal implementation largely remained the same (correlation = 0.98).

Diffractive optical elements for transformation of modes in lasers

DOEpatents

Sridharan, Arun K.; Pax, Paul H.; Heebner, John E.; Drachenberg, Derrek R.; Armstrong, James P.; Dawson, Jay W.

2015-09-01

Spatial mode conversion modules are described, with the capability of efficiently transforming a given optical beam profile, at one plane in space into another well-defined optical beam profile at a different plane in space, whose detailed spatial features and symmetry properties can, in general, differ significantly. The modules are comprised of passive, high-efficiency, low-loss diffractive optical elements, combined with Fourier transform optics. Design rules are described that employ phase retrieval techniques and associated algorithms to determine the necessary profiles of the diffractive optical components. System augmentations are described that utilize real-time adaptive optical techniques for enhanced performance as well as power scaling.

Diffractive optical elements for transformation of modes in lasers

DOEpatents

Sridharan, Arun K; Pax, Paul H; Heebner, John E; Drachenberg, Derrek R.; Armstrong, James P.; Dawson, Jay W.

2016-06-21

Spatial mode conversion modules are described, with the capability of efficiently transforming a given optical beam profile, at one plane in space into another well-defined optical beam profile at a different plane in space, whose detailed spatial features and symmetry properties can, in general, differ significantly. The modules are comprised of passive, high-efficiency, low-loss diffractive optical elements, combined with Fourier transform optics. Design rules are described that employ phase retrieval techniques and associated algorithms to determine the necessary profiles of the diffractive optical components. System augmentations are described that utilize real-time adaptive optical techniques for enhanced performance as well as power scaling.

2D non-separable linear canonical transform (2D-NS-LCT) based cryptography

NASA Astrophysics Data System (ADS)

Zhao, Liang; Muniraj, Inbarasan; Healy, John J.; Malallah, Ra'ed; Cui, Xiao-Guang; Ryle, James P.; Sheridan, John T.

2017-05-01

The 2D non-separable linear canonical transform (2D-NS-LCT) can describe a variety of paraxial optical systems. Digital algorithms to numerically evaluate the 2D-NS-LCTs are not only important in modeling the light field propagations but also of interest in various signal processing based applications, for instance optical encryption. Therefore, in this paper, for the first time, a 2D-NS-LCT based optical Double-random- Phase-Encryption (DRPE) system is proposed which offers encrypting information in multiple degrees of freedom. Compared with the traditional systems, i.e. (i) Fourier transform (FT); (ii) Fresnel transform (FST); (iii) Fractional Fourier transform (FRT); and (iv) Linear Canonical transform (LCT), based DRPE systems, the proposed system is more secure and robust as it encrypts the data with more degrees of freedom with an augmented key-space.

Streamflow record extension using power transformations and application to sediment transport

NASA Astrophysics Data System (ADS)

Moog, Douglas B.; Whiting, Peter J.; Thomas, Robert B.

1999-01-01

To obtain a representative set of flow rates for a stream, it is often desirable to fill in missing data or extend measurements to a longer time period by correlation to a nearby gage with a longer record. Linear least squares regression of the logarithms of the flows is a traditional and still common technique. However, its purpose is to generate optimal estimates of each day's discharge, rather than the population of discharges, for which it tends to underestimate variance. Maintenance-of-variance-extension (MOVE) equations [Hirsch, 1982] were developed to correct this bias. This study replaces the logarithmic transformation by the more general Box-Cox scaled power transformation, generating a more linear, constant-variance relationship for the MOVE extension. Combining the Box-Cox transformation with the MOVE extension is shown to improve accuracy in estimating order statistics of flow rate, particularly for the nonextreme discharges which generally govern cumulative transport over time. This advantage is illustrated by prediction of cumulative fractions of total bed load transport.

Quantitative 3D evolution of colloidal nanoparticle oxidation in solution

DOE PAGES

Sun, Yugang; Zuo, Xiaobing; Sankaranarayanan, Subramanian K. R. S.; ...

2017-04-21

Real-time tracking three-dimensional (3D) evolution of colloidal nanoparticles in solution is essential for understanding complex mechanisms involved in nanoparticle growth and transformation. We simultaneously use time-resolved small-angle and wide-angle x-ray scattering to monitor oxidation of highly uniform colloidal iron nanoparticles, enabling the reconstruction of intermediate 3D morphologies of the nanoparticles with a spatial resolution of ~5 Å. The in-situ probing combined with large-scale reactive molecular dynamics simulations reveals the transformational details from the solid metal nanoparticles to hollow metal oxide nanoshells via nanoscale Kirkendall process, for example, coalescence of voids upon their growth, reversing of mass diffusion direction depending onmore » crystallinity, and so forth. In conclusion, our results highlight the complex interplay between defect chemistry and defect dynamics in determining nanoparticle transformation and formation.« less

Quantitative 3D evolution of colloidal nanoparticle oxidation in solution

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

Sun, Yugang; Zuo, Xiaobing; Sankaranarayanan, Subramanian K. R. S.

Real-time tracking three-dimensional (3D) evolution of colloidal nanoparticles in solution is essential for understanding complex mechanisms involved in nanoparticle growth and transformation. We simultaneously use time-resolved small-angle and wide-angle x-ray scattering to monitor oxidation of highly uniform colloidal iron nanoparticles, enabling the reconstruction of intermediate 3D morphologies of the nanoparticles with a spatial resolution of ~5 Å. The in-situ probing combined with large-scale reactive molecular dynamics simulations reveals the transformational details from the solid metal nanoparticles to hollow metal oxide nanoshells via nanoscale Kirkendall process, for example, coalescence of voids upon their growth, reversing of mass diffusion direction depending onmore » crystallinity, and so forth. In conclusion, our results highlight the complex interplay between defect chemistry and defect dynamics in determining nanoparticle transformation and formation.« less

A transformation-aware perceptual image metric

NASA Astrophysics Data System (ADS)

Kellnhofer, Petr; Ritschel, Tobias; Myszkowski, Karol; Seidel, Hans-Peter

2015-03-01

Predicting human visual perception has several applications such as compression, rendering, editing and retargeting. Current approaches however, ignore the fact that the human visual system compensates for geometric transformations, e. g., we see that an image and a rotated copy are identical. Instead, they will report a large, false-positive difference. At the same time, if the transformations become too strong or too spatially incoherent, comparing two images indeed gets increasingly difficult. Between these two extrema, we propose a system to quantify the effect of transformations, not only on the perception of image differences, but also on saliency. To this end, we first fit local homographies to a given optical flow field and then convert this field into a field of elementary transformations such as translation, rotation, scaling, and perspective. We conduct a perceptual experiment quantifying the increase of difficulty when compensating for elementary transformations. Transformation entropy is proposed as a novel measure of complexity in a flow field. This representation is then used for applications, such as comparison of non-aligned images, where transformations cause threshold elevation, and detection of salient transformations.

Relativity of Scales: Application to AN Endo-Perspective of Temporal Structures

NASA Astrophysics Data System (ADS)

Nottale, Laurent; Timar, Pierre

The theory of scale relativity is an extension of the principle of relativity to scale transformations of the reference system, in a fractal geometry framework where coordinates become explicitly dependent on resolutions. Applied to an observer perspective, it means that the scales of length and of time, usually attributed to the observed object as being intrinsic to it, have actually no existence by themselves, since only the ratio between an external scale and an internal scale, which serves as unit, is meaningful. Oliver Sacks' observations on patients suffering from temporal and spatial distortions in Parkinson's and encephalitis lethargica disease offer a particularly relevant field of application for such a scale-relativistic view.

Model-based framework for multi-axial real-time hybrid simulation testing

NASA Astrophysics Data System (ADS)

Fermandois, Gaston A.; Spencer, Billie F.

2017-10-01

Real-time hybrid simulation is an efficient and cost-effective dynamic testing technique for performance evaluation of structural systems subjected to earthquake loading with rate-dependent behavior. A loading assembly with multiple actuators is required to impose realistic boundary conditions on physical specimens. However, such a testing system is expected to exhibit significant dynamic coupling of the actuators and suffer from time lags that are associated with the dynamics of the servo-hydraulic system, as well as control-structure interaction (CSI). One approach to reducing experimental errors considers a multi-input, multi-output (MIMO) controller design, yielding accurate reference tracking and noise rejection. In this paper, a framework for multi-axial real-time hybrid simulation (maRTHS) testing is presented. The methodology employs a real-time feedback-feedforward controller for multiple actuators commanded in Cartesian coordinates. Kinematic transformations between actuator space and Cartesian space are derived for all six-degrees-offreedom of the moving platform. Then, a frequency domain identification technique is used to develop an accurate MIMO transfer function of the system. Further, a Cartesian-domain model-based feedforward-feedback controller is implemented for time lag compensation and to increase the robustness of the reference tracking for given model uncertainty. The framework is implemented using the 1/5th-scale Load and Boundary Condition Box (LBCB) located at the University of Illinois at Urbana- Champaign. To demonstrate the efficacy of the proposed methodology, a single-story frame subjected to earthquake loading is tested. One of the columns in the frame is represented physically in the laboratory as a cantilevered steel column. For realtime execution, the numerical substructure, kinematic transformations, and controllers are implemented on a digital signal processor. Results show excellent performance of the maRTHS framework when six-degrees-of-freedom are controlled at the interface between substructures.

A fast and fully automatic registration approach based on point features for multi-source remote-sensing images

NASA Astrophysics Data System (ADS)

Yu, Le; Zhang, Dengrong; Holden, Eun-Jung

2008-07-01

Automatic registration of multi-source remote-sensing images is a difficult task as it must deal with the varying illuminations and resolutions of the images, different perspectives and the local deformations within the images. This paper proposes a fully automatic and fast non-rigid image registration technique that addresses those issues. The proposed technique performs a pre-registration process that coarsely aligns the input image to the reference image by automatically detecting their matching points by using the scale invariant feature transform (SIFT) method and an affine transformation model. Once the coarse registration is completed, it performs a fine-scale registration process based on a piecewise linear transformation technique using feature points that are detected by the Harris corner detector. The registration process firstly finds in succession, tie point pairs between the input and the reference image by detecting Harris corners and applying a cross-matching strategy based on a wavelet pyramid for a fast search speed. Tie point pairs with large errors are pruned by an error-checking step. The input image is then rectified by using triangulated irregular networks (TINs) to deal with irregular local deformations caused by the fluctuation of the terrain. For each triangular facet of the TIN, affine transformations are estimated and applied for rectification. Experiments with Quickbird, SPOT5, SPOT4, TM remote-sensing images of the Hangzhou area in China demonstrate the efficiency and the accuracy of the proposed technique for multi-source remote-sensing image registration.

Platform for Postprocessing Waveform-Based NDE

NASA Technical Reports Server (NTRS)

Roth, Don

2008-01-01

Taking advantage of the similarities that exist among all waveform-based non-destructive evaluation (NDE) methods, a common software platform has been developed containing multiple- signal and image-processing techniques for waveforms and images. The NASA NDE Signal and Image Processing software has been developed using the latest versions of LabVIEW, and its associated Advanced Signal Processing and Vision Toolkits. The software is useable on a PC with Windows XP and Windows Vista. The software has been designed with a commercial grade interface in which two main windows, Waveform Window and Image Window, are displayed if the user chooses a waveform file to display. Within these two main windows, most actions are chosen through logically conceived run-time menus. The Waveform Window has plots for both the raw time-domain waves and their frequency- domain transformations (fast Fourier transform and power spectral density). The Image Window shows the C-scan image formed from information of the time-domain waveform (such as peak amplitude) or its frequency-domain transformation at each scan location. The user also has the ability to open an image, or series of images, or a simple set of X-Y paired data set in text format. Each of the Waveform and Image Windows contains menus from which to perform many user actions. An option exists to use raw waves obtained directly from scan, or waves after deconvolution if system wave response is provided. Two types of deconvolution, time-based subtraction or inverse-filter, can be performed to arrive at a deconvolved wave set. Additionally, the menu on the Waveform Window allows preprocessing of waveforms prior to image formation, scaling and display of waveforms, formation of different types of images (including non-standard types such as velocity), gating of portions of waves prior to image formation, and several other miscellaneous and specialized operations. The menu available on the Image Window allows many further image processing and analysis operations, some of which are found in commercially-available image-processing software programs (such as Adobe Photoshop), and some that are not (removing outliers, Bscan information, region-of-interest analysis, line profiles, and precision feature measurements).

Seeing and believing: recent advances in imaging cell-cell interactions

PubMed Central

Yap, Alpha S.; Michael, Magdalene; Parton, Robert G.

2015-01-01

Advances in cell and developmental biology have often been closely linked to advances in our ability to visualize structure and function at many length and time scales. In this review, we discuss how new imaging technologies and new reagents have provided novel insights into the biology of cadherin-based cell-cell junctions. We focus on three developments: the application of super-resolution optical technologies to characterize the nanoscale organization of cadherins at cell-cell contacts, new approaches to interrogate the mechanical forces that act upon junctions, and advances in electron microscopy which have the potential to transform our understanding of cell-cell junctions. PMID:26543555

Seeing and believing: recent advances in imaging cell-cell interactions.

PubMed

Yap, Alpha S; Michael, Magdalene; Parton, Robert G

2015-01-01

Advances in cell and developmental biology have often been closely linked to advances in our ability to visualize structure and function at many length and time scales. In this review, we discuss how new imaging technologies and new reagents have provided novel insights into the biology of cadherin-based cell-cell junctions. We focus on three developments: the application of super-resolution optical technologies to characterize the nanoscale organization of cadherins at cell-cell contacts, new approaches to interrogate the mechanical forces that act upon junctions, and advances in electron microscopy which have the potential to transform our understanding of cell-cell junctions.

Uranium and strontium fate in waste-weathered sediments: Scaling of molecular processes to predict reactive transport

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

Chorover, Jon; Mueller, Karl; O'Day, Peggy

2016-04-02

Objectives of the project: 1. Determine the process coupling that occurs between mineral transformation and contaminant (U and Sr) speciation in acid-uranium waste weathered Hanford sediments. 2. Establish linkages between molecular-scale contaminant speciation and meso-scale contaminant lability, release and reactive transport. 3. Make conjunctive use of molecular- to bench-scale data to constrain the development of a mechanistic, reactive transport model that includes coupling of contaminant sorption-desorption and mineral transformation reactions. Hypotheses tested: - Uranium and strontium speciation in legacy sediments from the U-8 and U-12 Crib sites can be reproduced in bench-scale weathering experiments conducted on unimpacted Hanford sediments frommore » the same formations. - Reactive transport modeling of future uranium and strontium releases from the vadose zone of acid-waste weathered sediments can be effectively constrained by combining molecular-scale information on contaminant bonding environment with grain-scale information on contaminant phase partitioning, and meso-scale kinetic data on contaminant release from the waste-weathered porous media. - Although field contamination and laboratory experiments differ in their diagenetic time scales (decades for field vs. months to years for lab), sediment dissolution, neophase nucleation, and crystal growth reactions that occur during the initial disequilibrium induced by waste-sediment interaction leave a strong imprint that persists over subsequent longer-term equilibration time scales and, therefore, give rise to long-term memory effects. Enabling capabilities developed: Our team developed an iterative measure-model approach that is broadly applicable to elucidate the mechanistic underpinnings of reactive contaminant transport in geomedia subject to active weathering. Experimental design: Hypotheses were tested by comparing (with a similar set of techniques) the geochemical transformations and transport behaviors that occured in bench-scale studies of waste-sediment interaction with parallel model systems studies of homogeneous nucleation and neo-phase dissolution. Initial plans were to compare results with core sample extractions from the acid uranium waste impacted U-8 and U-12 Cribs at Hanford (see original proposal and letter of collaboration from J. Zachara). However, this part of the project was impossible because funding for core extractions were eliminated from the DoE budget. Three distinct crib waste aqueous simulants (whose composition is based on the most up-to-date information from field site investigations) were reacted with Hanford sediments in batch and column systems. Coupling of contaminant uptake to mineral weathering was monitored using a suite of methods both during waste-sediment interaction, and after, when waste-weathered sediments were subjected to infusion with circumneutral background pore water solutions. Our research was designed to adapt as needed to maintain a strong dialogue between laboratory and modeling investigations so that model development was increasingly constrained by emergent data and understanding. Potential impact of the project to DOE: Better prediction of contaminant uranium transport was achieved by employing multi-faceted lines of inquiry to build a strong bridge between molecular- and field-scale information. By focusing multiple lines and scales of observation on a common experimental design, our collaborative team revealed non-linear and emergent behavior in contaminated weathering systems. A goal of the current project was to expand our modeling capabilities, originally focused on hyperalkaline legacy waste streams, to include acidic weathering reactions that, as described above, were expected to result in profoundly different products. We were able to achieve this goal, and showed that these products nonetheless undergo analogous silicate and non-silicate transformation, ripening and aging processes. Our prediction that these weathering reactions would vary with waste stimulant chemistry resulted in data that was incorporated directly into a reactive transport model structure.« less

Dual linear structured support vector machine tracking method via scale correlation filter

NASA Astrophysics Data System (ADS)

Li, Weisheng; Chen, Yanquan; Xiao, Bin; Feng, Chen

2018-01-01

Adaptive tracking-by-detection methods based on structured support vector machine (SVM) performed well on recent visual tracking benchmarks. However, these methods did not adopt an effective strategy of object scale estimation, which limits the overall tracking performance. We present a tracking method based on a dual linear structured support vector machine (DLSSVM) with a discriminative scale correlation filter. The collaborative tracker comprised of a DLSSVM model and a scale correlation filter obtains good results in tracking target position and scale estimation. The fast Fourier transform is applied for detection. Extensive experiments show that our tracking approach outperforms many popular top-ranking trackers. On a benchmark including 100 challenging video sequences, the average precision of the proposed method is 82.8%.

Accuracy and uncertainty analysis of soil Bbf spatial distribution estimation at a coking plant-contaminated site based on normalization geostatistical technologies.

PubMed

Liu, Geng; Niu, Junjie; Zhang, Chao; Guo, Guanlin

2015-12-01

Data distribution is usually skewed severely by the presence of hot spots in contaminated sites. This causes difficulties for accurate geostatistical data transformation. Three types of typical normal distribution transformation methods termed the normal score, Johnson, and Box-Cox transformations were applied to compare the effects of spatial interpolation with normal distribution transformation data of benzo(b)fluoranthene in a large-scale coking plant-contaminated site in north China. Three normal transformation methods decreased the skewness and kurtosis of the benzo(b)fluoranthene, and all the transformed data passed the Kolmogorov-Smirnov test threshold. Cross validation showed that Johnson ordinary kriging has a minimum root-mean-square error of 1.17 and a mean error of 0.19, which was more accurate than the other two models. The area with fewer sampling points and that with high levels of contamination showed the largest prediction standard errors based on the Johnson ordinary kriging prediction map. We introduce an ideal normal transformation method prior to geostatistical estimation for severely skewed data, which enhances the reliability of risk estimation and improves the accuracy for determination of remediation boundaries.

Psychometric Validation of the Brief Adaptation to Disability Scale-Revised for Persons with Spinal Cord Injury in Taiwan

ERIC Educational Resources Information Center

Lin, Chen-Ping; Wang, Chia-Chiang; Fujikawa, Mayu; Brooks, Jessica; Eastvold-Walton, Lissa; Maxwell, Kristin; Chan, Fong

2013-01-01

Purpose: To examine the measurement structure of the Brief Adaptation to Disability Scale-Revised (B-ADS-R). Measure: A 12-item measure of disability acceptance based on the four value changes (enlarging the scope of values, containing the effects of the disability, subordinating the physique, and transforming comparative-status values to asset…

The Role of Bed Roughness in Wave Transformation Across Sloping Rock Shore Platforms

NASA Astrophysics Data System (ADS)

Poate, Tim; Masselink, Gerd; Austin, Martin J.; Dickson, Mark; McCall, Robert

2018-01-01

We present for the first time observations and model simulations of wave transformation across sloping (Type A) rock shore platforms. Pressure measurements of the water surface elevation using up to 15 sensors across five rock platforms with contrasting roughness, gradient, and wave climate represent the most extensive collected, both in terms of the range of environmental conditions, and the temporal and spatial resolution. Platforms are shown to dissipate both incident and infragravity wave energy as skewness and asymmetry develop and, in line with previous studies, surf zone wave heights are saturated and strongly tidally modulated. Overall, the observed properties of the waves and formulations derived from sandy beaches do not highlight any systematic interplatform variation, in spite of significant differences in platform roughness, suggesting that friction can be neglected when studying short wave transformation. Optimization of a numerical wave transformation model shows that the wave breaker criterion falls between the range of values reported for flat sandy beaches and those of steep coral fore reefs. However, the optimized drag coefficient shows significant scatter for the roughest sites and an alternative empirical drag model, based on the platform roughness, does not improve model performance. Thus, model results indicate that the parameterization of frictional drag using the bottom roughness length-scale may be inappropriate for the roughest platforms. Based on these results, we examine the balance of wave breaking to frictional dissipation for rock platforms and find that friction is only significant for very rough, flat platforms during small wave conditions outside the surf zone.

Compression of real time volumetric echocardiographic data using modified SPIHT based on the three-dimensional wavelet packet transform.

PubMed

Hang, X; Greenberg, N L; Shiota, T; Firstenberg, M S; Thomas, J D

2000-01-01

Real-time three-dimensional echocardiography has been introduced to provide improved quantification and description of cardiac function. Data compression is desired to allow efficient storage and improve data transmission. Previous work has suggested improved results utilizing wavelet transforms in the compression of medical data including 2D echocardiogram. Set partitioning in hierarchical trees (SPIHT) was extended to compress volumetric echocardiographic data by modifying the algorithm based on the three-dimensional wavelet packet transform. A compression ratio of at least 40:1 resulted in preserved image quality.

Relative velocity change measurement based on seismic noise analysis in exploration geophysics

NASA Astrophysics Data System (ADS)

Corciulo, M.; Roux, P.; Campillo, M.; Dubuq, D.

2011-12-01

Passive monitoring techniques based on noise cross-correlation analysis are still debated in exploration geophysics even if recent studies showed impressive performance in seismology at larger scale. Time evolution of complex geological structure using noise data includes localization of noise sources and measurement of relative velocity variations. Monitoring relative velocity variations only requires the measurement of phase shifts of seismic noise cross-correlation functions computed for successive time recordings. The existing algorithms, such as the Stretching and the Doublet, classically need great efforts in terms of computation time, making them not practical when continuous dataset on dense arrays are acquired. We present here an innovative technique for passive monitoring based on the measure of the instantaneous phase of noise-correlated signals. The Instantaneous Phase Variation (IPV) technique aims at cumulating the advantages of the Stretching and Doublet methods while proposing a faster measurement of the relative velocity change. The IPV takes advantage of the Hilbert transform to compute in the time domain the phase difference between two noise correlation functions. The relative velocity variation is measured through the slope of the linear regression of the phase difference curve as a function of correlation time. The large amount of noise correlation functions, classically available at exploration scale on dense arrays, allows for a statistical analysis that further improves the precision of the estimation of the velocity change. In this work, numerical tests first aim at comparing the IPV performance to the Stretching and Doublet techniques in terms of accuracy, robustness and computation time. Then experimental results are presented using a seismic noise dataset with five days of continuous recording on 397 geophones spread on a ~1 km-squared area.

User's Guide for MapIMG 2: Map Image Re-projection Software Package

USGS Publications Warehouse

Finn, Michael P.; Trent, Jason R.; Buehler, Robert A.

2006-01-01

BACKGROUND Scientists routinely accomplish small-scale geospatial modeling in the raster domain, using high-resolution datasets for large parts of continents and low-resolution to high-resolution datasets for the entire globe. Direct implementation of point-to-point transformation with appropriate functions yields the variety of projections available in commercial software packages, but implementation with data other than points requires specific adaptation of the transformation equations or prior preparation of the data to allow the transformation to succeed. It seems that some of these packages use the U.S. Geological Survey's (USGS) General Cartographic Transformation Package (GCTP) or similar point transformations without adaptation to the specific characteristics of raster data (Usery and others, 2003a). Usery and others (2003b) compiled and tabulated the accuracy of categorical areas in projected raster datasets of global extent. Based on the shortcomings identified in these studies, geographers and applications programmers at the USGS expanded and evolved a USGS software package, MapIMG, for raster map projection transformation (Finn and Trent, 2004). Daniel R. Steinwand of Science Applications International Corporation, National Center for Earth Resources Observation and Science, originally developed MapIMG for the USGS, basing it on GCTP. Through previous and continuing efforts at the USGS' National Geospatial Technical Operations Center, this program has been transformed from an application based on command line input into a software package based on a graphical user interface for Windows, Linux, and other UNIX machines.

Distributed Traffic Control for Reduced Fuel Consumption and Travel Time in Transportation Networks

DOT National Transportation Integrated Search

2018-04-01

Current technology in traffic control is limited to a centralized approach that has not paid appropriate attention to efficiency of fuel consumption and is subject to the scale of transportation networks. This project proposes a transformative approa...

A Novel Fault Diagnosis Method for Rotating Machinery Based on a Convolutional Neural Network

PubMed Central

Yang, Tao; Gao, Wei

2018-01-01

Fault diagnosis is critical to ensure the safety and reliable operation of rotating machinery. Most methods used in fault diagnosis of rotating machinery extract a few feature values from vibration signals for fault diagnosis, which is a dimensionality reduction from the original signal and may omit some important fault messages in the original signal. Thus, a novel diagnosis method is proposed involving the use of a convolutional neural network (CNN) to directly classify the continuous wavelet transform scalogram (CWTS), which is a time-frequency domain transform of the original signal and can contain most of the information of the vibration signals. In this method, CWTS is formed by discomposing vibration signals of rotating machinery in different scales using wavelet transform. Then the CNN is trained to diagnose faults, with CWTS as the input. A series of experiments is conducted on the rotor experiment platform using this method. The results indicate that the proposed method can diagnose the faults accurately. To verify the universality of this method, the trained CNN was also used to perform fault diagnosis for another piece of rotor equipment, and a good result was achieved. PMID:29734704

A Novel Fault Diagnosis Method for Rotating Machinery Based on a Convolutional Neural Network.

PubMed

Guo, Sheng; Yang, Tao; Gao, Wei; Zhang, Chen

2018-05-04

Fault diagnosis is critical to ensure the safety and reliable operation of rotating machinery. Most methods used in fault diagnosis of rotating machinery extract a few feature values from vibration signals for fault diagnosis, which is a dimensionality reduction from the original signal and may omit some important fault messages in the original signal. Thus, a novel diagnosis method is proposed involving the use of a convolutional neural network (CNN) to directly classify the continuous wavelet transform scalogram (CWTS), which is a time-frequency domain transform of the original signal and can contain most of the information of the vibration signals. In this method, CWTS is formed by discomposing vibration signals of rotating machinery in different scales using wavelet transform. Then the CNN is trained to diagnose faults, with CWTS as the input. A series of experiments is conducted on the rotor experiment platform using this method. The results indicate that the proposed method can diagnose the faults accurately. To verify the universality of this method, the trained CNN was also used to perform fault diagnosis for another piece of rotor equipment, and a good result was achieved.

Image Mosaic Method Based on SIFT Features of Line Segment

PubMed Central

Zhu, Jun; Ren, Mingwu

2014-01-01

This paper proposes a novel image mosaic method based on SIFT (Scale Invariant Feature Transform) feature of line segment, aiming to resolve incident scaling, rotation, changes in lighting condition, and so on between two images in the panoramic image mosaic process. This method firstly uses Harris corner detection operator to detect key points. Secondly, it constructs directed line segments, describes them with SIFT feature, and matches those directed segments to acquire rough point matching. Finally, Ransac method is used to eliminate wrong pairs in order to accomplish image mosaic. The results from experiment based on four pairs of images show that our method has strong robustness for resolution, lighting, rotation, and scaling. PMID:24511326

Scaling Relations and Self-Similarity of 3-Dimensional Reynolds-Averaged Navier-Stokes Equations.

PubMed

Ercan, Ali; Kavvas, M Levent

2017-07-25

Scaling conditions to achieve self-similar solutions of 3-Dimensional (3D) Reynolds-Averaged Navier-Stokes Equations, as an initial and boundary value problem, are obtained by utilizing Lie Group of Point Scaling Transformations. By means of an open-source Navier-Stokes solver and the derived self-similarity conditions, we demonstrated self-similarity within the time variation of flow dynamics for a rigid-lid cavity problem under both up-scaled and down-scaled domains. The strength of the proposed approach lies in its ability to consider the underlying flow dynamics through not only from the governing equations under consideration but also from the initial and boundary conditions, hence allowing to obtain perfect self-similarity in different time and space scales. The proposed methodology can be a valuable tool in obtaining self-similar flow dynamics under preferred level of detail, which can be represented by initial and boundary value problems under specific assumptions.

Degradation data analysis based on a generalized Wiener process subject to measurement error

NASA Astrophysics Data System (ADS)

Li, Junxing; Wang, Zhihua; Zhang, Yongbo; Fu, Huimin; Liu, Chengrui; Krishnaswamy, Sridhar

2017-09-01

Wiener processes have received considerable attention in degradation modeling over the last two decades. In this paper, we propose a generalized Wiener process degradation model that takes unit-to-unit variation, time-correlated structure and measurement error into considerations simultaneously. The constructed methodology subsumes a series of models studied in the literature as limiting cases. A simple method is given to determine the transformed time scale forms of the Wiener process degradation model. Then model parameters can be estimated based on a maximum likelihood estimation (MLE) method. The cumulative distribution function (CDF) and the probability distribution function (PDF) of the Wiener process with measurement errors are given based on the concept of the first hitting time (FHT). The percentiles of performance degradation (PD) and failure time distribution (FTD) are also obtained. Finally, a comprehensive simulation study is accomplished to demonstrate the necessity of incorporating measurement errors in the degradation model and the efficiency of the proposed model. Two illustrative real applications involving the degradation of carbon-film resistors and the wear of sliding metal are given. The comparative results show that the constructed approach can derive a reasonable result and an enhanced inference precision.

A New Dimensionless Number for Redox Conditions within the Hyporheic Zone: Morphological and Biogeochemical Controls

NASA Astrophysics Data System (ADS)

Marzadri, A.; Tonina, D.; Bellin, A.

2012-12-01

We introduce a new Damköhler number, Da, to quantify the biogeochemical status of the hyporheic zone and to upscale local hyporheic processes to reach scale. Da is defined as the ratio between the median hyporheic residence time, τup,50, which is a representative time scale of the hyporheic flow, and a representative time scale of biogeochemical reactions, which we define as the time τlim needed to consume dissolved oxygen to a prescribed threshold concentration below which reducing reactions are activated: Da = τup,50/τlim. This approach accounts for streambed topography and surface hydraulics via the hyporheic residence time and biogeochemical reaction via the time limit τlim. Da can readily evaluate the redox status of the hyporheic zone. Values of Da larger than 1 indicate prevailing anaerobic conditions, whereas values smaller than 1 prevailing aerobic conditions. This new Damköhler number can quantify the efficiency of hyporheic zone in transforming dissolved inorganic nitrogen species such as ammonium and nitrate, whose transformation depends on the redox condition of the hyporheic zone. We define a particular value of Da, Das, that indicates when the hyporheic zone is a source or a sink of nitrate. This index depends only on the relative abundance of ammonium and nitrate. The approach can be applied to any hyporheic zone of which the median hyporheic residence time is known. Application to streams with pool-riffle morphology shows that Da increases passing from small to large streams implying that the fraction of the hyporheic zone in anaerobic conditions increases with stream size.

Effect of Annealing Time on Microstructural Evolution and Deformation Characteristics in 10Mn1.5Al TRIP Steel

NASA Astrophysics Data System (ADS)

Han, Qihang; Zhang, Yulong; Wang, Li

2015-05-01

To investigate microstructural evolution and its effects on the deformation behaviors of cold-rolled 10Mn1.5Al TRIP steel, a series of intercritical annealing treatments with various holding times from 3 minutes to 48 hours were conducted. With the increase of the holding time from 3 minutes to 12 hours, the elongation was improved from 15 to 42 pct, while the tensile strength was only reduced from 1210 to 1095 MPa; the strength-ductility combination thus exceeded 45 GPa pct. Austenite was found to coexist with martensite within deformed grains, which reduced the strain concentration at the interface. The austenite transformation fraction, as measured from the {220} peaks, after 3 minutes annealing was half that after 12 hours annealing. This is an indication that the slip systems were more easily activated in the micro-scaled grains compared with nano-scaled grains. Therefore, although the stability of austenite would have increased during annealing, size-induced slip suppression was reduced. Thus, more strain was accommodated in the austenite, facilitating a greater strain-induced transformation and better ductility.

Mixing effects on nitrogen and oxygen concentrations and the relationship to mean residence time in a hyporheic zone of a riffle-pool sequence

USGS Publications Warehouse

Naranjo, Ramon C.; Niswonger, Richard G.; Clinton Davis,

2015-01-01

Flow paths and residence times in the hyporheic zone are known to influence biogeochemical processes such as nitrification and denitrification. The exchange across the sediment-water interface may involve mixing of surface water and groundwater through complex hyporheic flow paths that contribute to highly variable biogeochemically active zones. Despite the recognition of these patterns in the literature, conceptualization and analysis of flow paths and nitrogen transformations beneath riffle-pool sequences often neglect to consider bed form driven exchange along the entire reach. In this study, the spatial and temporal distribution of dissolved oxygen (DO), nitrate (NO3-) and ammonium (NH4+) were monitored in the hyporheic zone beneath a riffle-pool sequence on a losing section of the Truckee River, NV. Spatially-varying hyporheic exchange and the occurrence of multi-scale hyporheic mixing cells are shown to influence concentrations of DO and NO3- and the mean residence time (MRT) of riffle and pool areas. Distinct patterns observed in piezometers are shown to be influenced by the first large flow event following a steady 8 month period of low flow conditions. Increases in surface water discharge resulted in reversed hydraulic gradients and production of nitrate through nitrification at small vertical spatial scales (0.10 to 0.25 m) beneath the sediment-water interface. In areas with high downward flow rates and low MRT, denitrification may be limited. The use of a longitudinal two-dimensional flow model helped identify important mechanisms such as multi-scale hyporheic mixing cells and spatially varying MRT, an important driver for nitrogen transformation in the riverbed. Our observations of DO and NO3- concentrations and model simulations highlight the role of multi-scale hyporheic mixing cells on MRT and nitrogen transformations in the hyporheic zone of riffle-pool sequences. This article is protected by copyright. All rights reserved.

Focusing high-squint and large-baseline one-stationary bistatic SAR data using keystone transform and enhanced nonlinear chirp scaling based on an ellipse model

NASA Astrophysics Data System (ADS)

Zhong, Hua; Zhang, Song; Hu, Jian; Sun, Minhong

2017-12-01

This paper deals with the imaging problem for one-stationary bistatic synthetic aperture radar (BiSAR) with high-squint, large-baseline configuration. In this bistatic configuration, accurate focusing of BiSAR data is a difficult issue due to the relatively large range cell migration (RCM), severe range-azimuth coupling, and inherent azimuth-geometric variance. To circumvent these issues, an enhanced azimuth nonlinear chirp scaling (NLCS) algorithm based on an ellipse model is investigated in this paper. In the range processing, a method combining deramp operation and keystone transform (KT) is adopted to remove linear RCM completely and mitigate range-azimuth cross-coupling. In the azimuth focusing, an ellipse model is established to analyze and depict the characteristic of azimuth-variant Doppler phase. Based on the new model, an enhanced azimuth NLCS algorithm is derived to focus one-stationary BiSAR data. Simulating results exhibited at the end of this paper validate the effectiveness of the proposed algorithm.

Modeling Global Biogenic Emission of Isoprene: Exploration of Model Drivers

NASA Technical Reports Server (NTRS)

Alexander, Susan E.; Potter, Christopher S.; Coughlan, Joseph C.; Klooster, Steven A.; Lerdau, Manuel T.; Chatfield, Robert B.; Peterson, David L. (Technical Monitor)

1996-01-01

Vegetation provides the major source of isoprene emission to the atmosphere. We present a modeling approach to estimate global biogenic isoprene emission. The isoprene flux model is linked to a process-based computer simulation model of biogenic trace-gas fluxes that operates on scales that link regional and global data sets and ecosystem nutrient transformations Isoprene emission estimates are determined from estimates of ecosystem specific biomass, emission factors, and algorithms based on light and temperature. Our approach differs from an existing modeling framework by including the process-based global model for terrestrial ecosystem production, satellite derived ecosystem classification, and isoprene emission measurements from a tropical deciduous forest. We explore the sensitivity of model estimates to input parameters. The resulting emission products from the global 1 degree x 1 degree coverage provided by the satellite datasets and the process model allow flux estimations across large spatial scales and enable direct linkage to atmospheric models of trace-gas transport and transformation.

Practical Sub-Nyquist Sampling via Array-Based Compressed Sensing Receiver Architecture

DTIC Science & Technology

2016-07-10

different array ele- ments at different sub-Nyquist sampling rates. Signal processing inspired by the sparse fast Fourier transform allows for signal...reconstruction algorithms can be computationally demanding (REF). The related sparse Fourier transform algorithms aim to reduce the processing time nec- essary to...compute the DFT of frequency-sparse signals [7]. In particular, the sparse fast Fourier transform (sFFT) achieves processing time better than the

An overview of experimental results and dispersion modelling of nanoparticles in the wake of moving vehicles.

PubMed

Carpentieri, Matteo; Kumar, Prashant; Robins, Alan

2011-03-01

Understanding the transformation of nanoparticles emitted from vehicles is essential for developing appropriate methods for treating fine scale particle dynamics in dispersion models. This article provides an overview of significant research work relevant to modelling the dispersion of pollutants, especially nanoparticles, in the wake of vehicles. Literature on vehicle wakes and nanoparticle dispersion is reviewed, taking into account field measurements, wind tunnel experiments and mathematical approaches. Field measurements and modelling studies highlighted the very short time scales associated with nanoparticle transformations in the first stages after the emission. These transformations strongly interact with the flow and turbulence fields immediately behind the vehicle, hence the need of characterising in detail the mixing processes in the vehicle wake. Very few studies have analysed this interaction and more research is needed to build a basis for model development. A possible approach is proposed and areas of further investigation identified. Copyright © 2010 Elsevier Ltd. All rights reserved.

Single-trial log transformation is optimal in frequency analysis of resting EEG alpha.

PubMed

Smulders, Fren T Y; Ten Oever, Sanne; Donkers, Franc C L; Quaedflieg, Conny W E M; van de Ven, Vincent

2018-02-01

The appropriate definition and scaling of the magnitude of electroencephalogram (EEG) oscillations is an underdeveloped area. The aim of this study was to optimize the analysis of resting EEG alpha magnitude, focusing on alpha peak frequency and nonlinear transformation of alpha power. A family of nonlinear transforms, Box-Cox transforms, were applied to find the transform that (a) maximized a non-disputed effect: the increase in alpha magnitude when the eyes are closed (Berger effect), and (b) made the distribution of alpha magnitude closest to normal across epochs within each participant, or across participants. The transformations were performed either at the single epoch level or at the epoch-average level. Alpha peak frequency showed large individual differences, yet good correspondence between various ways to estimate it in 2 min of eyes-closed and 2 min of eyes-open resting EEG data. Both alpha magnitude and the Berger effect were larger for individual alpha than for a generic (8-12 Hz) alpha band. The log-transform on single epochs (a) maximized the t-value of the contrast between the eyes-open and eyes-closed conditions when tested within each participant, and (b) rendered near-normally distributed alpha power across epochs and participants, thereby making further transformation of epoch averages superfluous. The results suggest that the log-normal distribution is a fundamental property of variations in alpha power across time in the order of seconds. Moreover, effects on alpha power appear to be multiplicative rather than additive. These findings support the use of the log-transform on single epochs to achieve appropriate scaling of alpha magnitude. © 2018 The Authors. European Journal of Neuroscience published by Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Rolling Bearing Fault Diagnosis Based on an Improved HTT Transform

PubMed Central

Tang, Guiji; Tian, Tian; Zhou, Chong

2018-01-01

When rolling bearing failure occurs, vibration signals generally contain different signal components, such as impulsive fault feature signals, background noise and harmonic interference signals. One of the most challenging aspects of rolling bearing fault diagnosis is how to inhibit noise and harmonic interference signals, while enhancing impulsive fault feature signals. This paper presents a novel bearing fault diagnosis method, namely an improved Hilbert time–time (IHTT) transform, by combining a Hilbert time–time (HTT) transform with principal component analysis (PCA). Firstly, the HTT transform was performed on vibration signals to derive a HTT transform matrix. Then, PCA was employed to de-noise the HTT transform matrix in order to improve the robustness of the HTT transform. Finally, the diagonal time series of the de-noised HTT transform matrix was extracted as the enhanced impulsive fault feature signal and the contained fault characteristic information was identified through further analyses of amplitude and envelope spectrums. Both simulated and experimental analyses validated the superiority of the presented method for detecting bearing failures. PMID:29662013

Optimizing BAO measurements with non-linear transformations of the Lyman-α forest

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

Wang, Xinkang; Font-Ribera, Andreu; Seljak, Uroš, E-mail: xinkang.wang@berkeley.edu, E-mail: afont@lbl.gov, E-mail: useljak@berkeley.edu

2015-04-01

We explore the effect of applying a non-linear transformation to the Lyman-α forest transmitted flux F=e{sup −τ} and the ability of analytic models to predict the resulting clustering amplitude. Both the large-scale bias of the transformed field (signal) and the amplitude of small scale fluctuations (noise) can be arbitrarily modified, but we were unable to find a transformation that increases significantly the signal-to-noise ratio on large scales using Taylor expansion up to the third order. In particular, however, we achieve a 33% improvement in signal to noise for Gaussianized field in transverse direction. On the other hand, we explore anmore » analytic model for the large-scale biasing of the Lyα forest, and present an extension of this model to describe the biasing of the transformed fields. Using hydrodynamic simulations we show that the model works best to describe the biasing with respect to velocity gradients, but is less successful in predicting the biasing with respect to large-scale density fluctuations, especially for very nonlinear transformations.« less

The Neural Network In Coordinate Transformation

NASA Astrophysics Data System (ADS)

Urusan, Ahmet Yucel

2011-12-01

In international literature, Coordinate operations is divided into two categories. They are coordinate conversion and coordinate transformation. Coordinates converted from coordinate system A to coordinate system B in the same datum (mean origine, scale and axis directions are same) by coordinate conversion. There are two different datum in coordinate transformation. The basis of each datum to a different coordinate reference system. In Coordinate transformation, coordinates are transformed from coordinate reference system A to coordinate referance system B. Geodetic studies based on physical measurements. Coordinate transformation needs identical points which were measured in each coordinate reference system (A and B). However it is difficult (and need a big reserved budget) to measure in some places like as top of mountain, boundry of countries and seaside. In this study, this sample problem solution was researched. The method of learning which is one of the neural network methods, was used for solution of this problem.

Transforming Traditional Lectures into Problem-Based Blended Learning: Challenges and Experiences

ERIC Educational Resources Information Center

Dalsgaard, Christian; Godsk, Mikkel

2007-01-01

This paper presents our experiences and the challenges identified in transforming traditional lecture-based modules at a university into problem-based blended learning within a social constructivist approach. Our experiment was, among other factors, motivated by an urgent need to meet new curriculum requirements by reducing the lecturing time in a…

ExpandED Schools National Demonstration: Lessons for Scale and Sustainability

ERIC Educational Resources Information Center

Russell, Christina A.; Hildreth, Jeanine L.; Stevens, Pamela

2016-01-01

The ExpandED Schools model for expanded learning is designed to transform schools by changing the use of time, both as experienced by students in learning and by teachers in instruction. The model is grounded in the belief that strategically adding time to the school day can enhance skills and knowledge and broaden horizons by engaging students in…

Time-frequency dynamics of resting-state brain connectivity measured with fMRI.

PubMed

Chang, Catie; Glover, Gary H

2010-03-01

Most studies of resting-state functional connectivity using fMRI employ methods that assume temporal stationarity, such as correlation and data-driven decompositions computed across the duration of the scan. However, evidence from both task-based fMRI studies and animal electrophysiology suggests that functional connectivity may exhibit dynamic changes within time scales of seconds to minutes. In the present study, we investigated the dynamic behavior of resting-state connectivity across the course of a single scan, performing a time-frequency coherence analysis based on the wavelet transform. We focused on the connectivity of the posterior cingulate cortex (PCC), a primary node of the default-mode network, examining its relationship with both the "anticorrelated" ("task-positive") network as well as other nodes of the default-mode network. It was observed that coherence and phase between the PCC and the anticorrelated network was variable in time and frequency, and statistical testing based on Monte Carlo simulations revealed the presence of significant scale-dependent temporal variability. In addition, a sliding-window correlation procedure identified other regions across the brain that exhibited variable connectivity with the PCC across the scan, which included areas previously implicated in attention and salience processing. Although it is unclear whether the observed coherence and phase variability can be attributed to residual noise or modulation of cognitive state, the present results illustrate that resting-state functional connectivity is not static, and it may therefore prove valuable to consider measures of variability, in addition to average quantities, when characterizing resting-state networks. Copyright (c) 2009 Elsevier Inc. All rights reserved.

Interferometric sensitivity and entanglement by scanning through quantum phase transitions in spinor Bose-Einstein condensates

NASA Astrophysics Data System (ADS)

Feldmann, P.; Gessner, M.; Gabbrielli, M.; Klempt, C.; Santos, L.; Pezzè, L.; Smerzi, A.

2018-03-01

Recent experiments demonstrated the generation of entanglement by quasiadiabatically driving through quantum phase transitions of a ferromagnetic spin-1 Bose-Einstein condensate in the presence of a tunable quadratic Zeeman shift. We analyze, in terms of the Fisher information, the interferometric value of the entanglement accessible by this approach. In addition to the Twin-Fock phase studied experimentally, we unveil a second regime, in the broken axisymmetry phase, which provides Heisenberg scaling of the quantum Fisher information and can be reached on shorter time scales. We identify optimal unitary transformations and an experimentally feasible optimal measurement prescription that maximize the interferometric sensitivity. We further ascertain that the Fisher information is robust with respect to nonadiabaticity and measurement noise. Finally, we show that the quasiadiabatic entanglement preparation schemes admit higher sensitivities than dynamical methods based on fast quenches.

Sensor data as a measure of native freshwater mussel impact on nitrate formation and food digestion in continuous-flow mesocosms

USGS Publications Warehouse

Bril, Jeremy S.; Durst, Jonathan J.; Hurley, Brion M.; Just, Craig L.; Newton, Teresa J.

2014-01-01

Native freshwater mussels can influence the aquatic N cycle, but the mechanisms and magnitude of this effect are not fully understood. We assessed the effects of Amblema plicata and Lampsilis cardium on N transformations over 72 d in 4 continuous-flow mesocosms, with 2 replicates of 2 treatments (mesocosms with and without mussels), equipped with electronic water-chemistry sensors. We compared sensor data to discrete sample data to assess the effect of additional sensor measurements on the ability to detect mussel-related effects on NO3– formation. Analysis of 624 sensor-based data points detected a nearly 6% increase in NO3– concentration in overlying water of mesocosms with mussels relative to mesocosms without mussels (p 3– between treatments. Mussels also significantly increased NO2– concentrations in the overlying water, but no significant difference in total N was observed. We used the sensor data for phytoplankton-N and NH4+ to infer that digestion times in mussels were 13 ± 6 h. The results suggest that rapid increases in phytoplankton-N levels in the overlying water can lead to decreased lag times between phytoplankton-N and NH4+ maxima. This result indicates that mussels may adjust their digestion rates in response to increased levels of food. The adjustment in digestion time suggests that mussels have a strong response to food availability that can disrupt typical circadian rhythms. Use of sensor data to measure directly and to infer mussel effects on aquatic N transformations at the mesocosm scale could be useful at larger scales in the future.

Land surface phenological responses to land use and climate variation in a changing Central Asia

NASA Astrophysics Data System (ADS)

Kariyeva, Jahan

During the last few decades Central Asia has experienced widespread changes in land cover and land use following the socio-economic and institutional transformations of the region catalyzed by the USSR collapse in 1991. The decade-long drought events and steadily increasing temperature regimes in the region came on top of these institutional transformations, affecting the long term and landscape scale vegetation responses. This research is based on the need to better understand the potential ecological and policy implications of climate variation and land use practices in the contexts of landscape-scale changes dynamics and variability patterns of land surface phenology responses in Central Asia. The land surface phenology responses -- the spatio-temporal dynamics of terrestrial vegetation derived from the remotely sensed data -- provide measurements linked to the timing of vegetation growth cycles (e.g., start of growing season) and total vegetation productivity over the growing season, which are used as a proxy for the assessment of effects of variations in environmental settings. Local and regional scale assessment of the before and after the USSR collapse vegetation response patterns in the natural and agricultural systems of the Central Asian drylands was conducted to characterize newly emerging links (since 1991) between coupled human and natural systems, e.g., socio-economic and policy drivers of altered land and water use and distribution patterns. Spatio-temporal patterns of bioclimatic responses were examined to determine how phenology is associated with temperature and precipitation in different land use types, including rainfed and irrigated agricultural types. Phenological models were developed to examine relationship between environmental drivers and effect of their altitudinal and latitudinal gradients on the broad-scale vegetation response patterns in non-cropland ecosystems of the desert, steppe, and mountainous regional landscapes of Central Asia. The study results demonstrated that the satellite derived measurements of temporal cycles of vegetation greenness and productivity data was a valuable bioclimatic integrator of climatic and land use variation in Central Asia. The synthesis of broad-scale phenological changes in Central Asia showed that linkages of natural and human systems vary across space and time comprising complex and tightly integrated patterns and processes that are not evident when studied separately.

Biomolecular surface construction by PDE transform.

PubMed

Zheng, Qiong; Yang, Siyang; Wei, Guo-Wei

2012-03-01

This work proposes a new framework for the surface generation based on the partial differential equation (PDE) transform. The PDE transform has recently been introduced as a general approach for the mode decomposition of images, signals, and data. It relies on the use of arbitrarily high-order PDEs to achieve the time-frequency localization, control the spectral distribution, and regulate the spatial resolution. The present work provides a new variational derivation of high-order PDE transforms. The fast Fourier transform is utilized to accomplish the PDE transform so as to avoid stringent stability constraints in solving high-order PDEs. As a consequence, the time integration of high-order PDEs can be done efficiently with the fast Fourier transform. The present approach is validated with a variety of test examples in two-dimensional and three-dimensional settings. We explore the impact of the PDE transform parameters, such as the PDE order and propagation time, on the quality of resulting surfaces. Additionally, we utilize a set of 10 proteins to compare the computational efficiency of the present surface generation method and a standard approach in Cartesian meshes. Moreover, we analyze the present method by examining some benchmark indicators of biomolecular surface, that is, surface area, surface-enclosed volume, solvation free energy, and surface electrostatic potential. A test set of 13 protein molecules is used in the present investigation. The electrostatic analysis is carried out via the Poisson-Boltzmann equation model. To further demonstrate the utility of the present PDE transform-based surface method, we solve the Poisson-Nernst-Planck equations with a PDE transform surface of a protein. Second-order convergence is observed for the electrostatic potential and concentrations. Finally, to test the capability and efficiency of the present PDE transform-based surface generation method, we apply it to the construction of an excessively large biomolecule, a virus surface capsid. Virus surface morphologies of different resolutions are attained by adjusting the propagation time. Therefore, the present PDE transform provides a multiresolution analysis in the surface visualization. Extensive numerical experiment and comparison with an established surface model indicate that the present PDE transform is a robust, stable, and efficient approach for biomolecular surface generation in Cartesian meshes. Copyright © 2012 John Wiley & Sons, Ltd.

Pattern recognition and feature extraction with an optical Hough transform

NASA Astrophysics Data System (ADS)

Fernández, Ariel

2016-09-01

Pattern recognition and localization along with feature extraction are image processing applications of great interest in defect inspection and robot vision among others. In comparison to purely digital methods, the attractiveness of optical processors for pattern recognition lies in their highly parallel operation and real-time processing capability. This work presents an optical implementation of the generalized Hough transform (GHT), a well-established technique for the recognition of geometrical features in binary images. Detection of a geometric feature under the GHT is accomplished by mapping the original image to an accumulator space; the large computational requirements for this mapping make the optical implementation an attractive alternative to digital- only methods. Starting from the integral representation of the GHT, it is possible to device an optical setup where the transformation is obtained, and the size and orientation parameters can be controlled, allowing for dynamic scale and orientation-variant pattern recognition. A compact system for the above purposes results from the use of an electrically tunable lens for scale control and a rotating pupil mask for orientation variation, implemented on a high-contrast spatial light modulator (SLM). Real-time (as limited by the frame rate of the device used to capture the GHT) can also be achieved, allowing for the processing of video sequences. Besides, by thresholding of the GHT (with the aid of another SLM) and inverse transforming (which is optically achieved in the incoherent system under appropriate focusing setting), the previously detected features of interest can be extracted.

Sensors in the Stream: The High-Frequency Wave of the Present.

PubMed

Rode, Michael; Wade, Andrew J; Cohen, Matthew J; Hensley, Robert T; Bowes, Michael J; Kirchner, James W; Arhonditsis, George B; Jordan, Phil; Kronvang, Brian; Halliday, Sarah J; Skeffington, Richard A; Rozemeijer, Joachim C; Aubert, Alice H; Rinke, Karsten; Jomaa, Seifeddine

2016-10-04

New scientific understanding is catalyzed by novel technologies that enhance measurement precision, resolution or type, and that provide new tools to test and develop theory. Over the last 50 years, technology has transformed the hydrologic sciences by enabling direct measurements of watershed fluxes (evapotranspiration, streamflow) at time scales and spatial extents aligned with variation in physical drivers. High frequency water quality measurements, increasingly obtained by in situ water quality sensors, are extending that transformation. Widely available sensors for some physical (temperature) and chemical (conductivity, dissolved oxygen) attributes have become integral to aquatic science, and emerging sensors for nutrients, dissolved CO 2 , turbidity, algal pigments, and dissolved organic matter are now enabling observations of watersheds and streams at time scales commensurate with their fundamental hydrological, energetic, elemental, and biological drivers. Here we synthesize insights from emerging technologies across a suite of applications, and envision future advances, enabled by sensors, in our ability to understand, predict, and restore watershed and stream systems.

Doppler radar fall activity detection using the wavelet transform.

PubMed

Su, Bo Yu; Ho, K C; Rantz, Marilyn J; Skubic, Marjorie

2015-03-01

We propose in this paper the use of Wavelet transform (WT) to detect human falls using a ceiling mounted Doppler range control radar. The radar senses any motions from falls as well as nonfalls due to the Doppler effect. The WT is very effective in distinguishing the falls from other activities, making it a promising technique for radar fall detection in nonobtrusive inhome elder care applications. The proposed radar fall detector consists of two stages. The prescreen stage uses the coefficients of wavelet decomposition at a given scale to identify the time locations in which fall activities may have occurred. The classification stage extracts the time-frequency content from the wavelet coefficients at many scales to form a feature vector for fall versus nonfall classification. The selection of different wavelet functions is examined to achieve better performance. Experimental results using the data from the laboratory and real inhome environments validate the promising and robust performance of the proposed detector.

Multiple imputation in the presence of non-normal data.

PubMed

Lee, Katherine J; Carlin, John B

2017-02-20

Multiple imputation (MI) is becoming increasingly popular for handling missing data. Standard approaches for MI assume normality for continuous variables (conditionally on the other variables in the imputation model). However, it is unclear how to impute non-normally distributed continuous variables. Using simulation and a case study, we compared various transformations applied prior to imputation, including a novel non-parametric transformation, to imputation on the raw scale and using predictive mean matching (PMM) when imputing non-normal data. We generated data from a range of non-normal distributions, and set 50% to missing completely at random or missing at random. We then imputed missing values on the raw scale, following a zero-skewness log, Box-Cox or non-parametric transformation and using PMM with both type 1 and 2 matching. We compared inferences regarding the marginal mean of the incomplete variable and the association with a fully observed outcome. We also compared results from these approaches in the analysis of depression and anxiety symptoms in parents of very preterm compared with term-born infants. The results provide novel empirical evidence that the decision regarding how to impute a non-normal variable should be based on the nature of the relationship between the variables of interest. If the relationship is linear in the untransformed scale, transformation can introduce bias irrespective of the transformation used. However, if the relationship is non-linear, it may be important to transform the variable to accurately capture this relationship. A useful alternative is to impute the variable using PMM with type 1 matching. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Speckle-reducing scale-invariant feature transform match for synthetic aperture radar image registration

NASA Astrophysics Data System (ADS)

Wang, Xianmin; Li, Bo; Xu, Qizhi

2016-07-01

The anisotropic scale space (ASS) is often used to enhance the performance of a scale-invariant feature transform (SIFT) algorithm in the registration of synthetic aperture radar (SAR) images. The existing ASS-based methods usually suffer from unstable keypoints and false matches, since the anisotropic diffusion filtering has limitations in reducing the speckle noise from SAR images while building the ASS image representation. We proposed a speckle reducing SIFT match method to obtain stable keypoints and acquire precise matches for the SAR image registration. First, the keypoints are detected in a speckle reducing anisotropic scale space constructed by the speckle reducing anisotropic diffusion, so that speckle noise is greatly reduced and prominent structures of the images are preserved, consequently the stable keypoints can be derived. Next, the probabilistic relaxation labeling approach is employed to establish the matches of the keypoints then the correct match rate of the keypoints is significantly increased. Experiments conducted on simulated speckled images and real SAR images demonstrate the effectiveness of the proposed method.

Skeletonization of gray-scale images by gray weighted distance transform

NASA Astrophysics Data System (ADS)

Qian, Kai; Cao, Siqi; Bhattacharya, Prabir

1997-07-01

In pattern recognition, thinning algorithms are often a useful tool to represent a digital pattern by means of a skeletonized image, consisting of a set of one-pixel-width lines that highlight the significant features interest in applying thinning directly to gray-scale images, motivated by the desire of processing images characterized by meaningful information distributed over different levels of gray intensity. In this paper, a new algorithm is presented which can skeletonize both black-white and gray pictures. This algorithm is based on the gray distance transformation and can be used to process any non-well uniformly distributed gray-scale picture and can preserve the topology of original picture. This process includes a preliminary phase of investigation in the 'hollows' in the gray-scale image; these hollows are considered not as topological constrains for the skeleton structure depending on their statistically significant depth. This algorithm can also be executed on a parallel machine as all the operations are executed in local. Some examples are discussed to illustrate the algorithm.

Irreversible transformation of ferromagnetic ordered stripe domains in single-shot infrared-pump/resonant-x-ray-scattering-probe experiments

NASA Astrophysics Data System (ADS)

Bergeard, Nicolas; Schaffert, Stefan; López-Flores, Víctor; Jaouen, Nicolas; Geilhufe, Jan; Günther, Christian M.; Schneider, Michael; Graves, Catherine; Wang, Tianhan; Wu, Benny; Scherz, Andreas; Baumier, Cédric; Delaunay, Renaud; Fortuna, Franck; Tortarolo, Marina; Tudu, Bharati; Krupin, Oleg; Minitti, Michael P.; Robinson, Joe; Schlotter, William F.; Turner, Joshua J.; Lüning, Jan; Eisebitt, Stefan; Boeglin, Christine

2015-02-01

The evolution of a magnetic domain structure upon excitation by an intense, femtosecond infrared (IR) laser pulse has been investigated using single-shot based time-resolved resonant x-ray scattering at the x-ray free electron laser LCLS. A well-ordered stripe domain pattern as present in a thin CoPd alloy film has been used as a prototype magnetic domain structure for this study. The fluence of the IR laser pump pulse was sufficient to lead to an almost complete quenching of the magnetization within the ultrafast demagnetization process taking place within the first few hundreds of femtoseconds following the IR laser pump pulse excitation. On longer time scales this excitation gave rise to subsequent irreversible transformations of the magnetic domain structure. Under our specific experimental conditions, it took about 2 ns before the magnetization started to recover. After about 5 ns the previously ordered stripe domain structure had evolved into a disordered labyrinth domain structure. Surprisingly, we observe after about 7 ns the occurrence of a partially ordered stripe domain structure reoriented into a novel direction. It is this domain structure in which the sample's magnetization stabilizes as revealed by scattering patterns recorded long after the initial pump-probe cycle. Using micromagnetic simulations we can explain this observation based on changes of the magnetic anisotropy going along with heat dissipation in the film.

Harmonic analysis of traction power supply system based on wavelet decomposition

NASA Astrophysics Data System (ADS)

Dun, Xiaohong

2018-05-01

With the rapid development of high-speed railway and heavy-haul transport, AC drive electric locomotive and EMU large-scale operation in the country on the ground, the electrified railway has become the main harmonic source of China's power grid. In response to this phenomenon, the need for timely monitoring of power quality problems of electrified railway, assessment and governance. Wavelet transform is developed on the basis of Fourier analysis, the basic idea comes from the harmonic analysis, with a rigorous theoretical model, which has inherited and developed the local thought of Garbor transformation, and has overcome the disadvantages such as window fixation and lack of discrete orthogonally, so as to become a more recently studied spectral analysis tool. The wavelet analysis takes the gradual and precise time domain step in the high frequency part so as to focus on any details of the signal being analyzed, thereby comprehensively analyzing the harmonics of the traction power supply system meanwhile use the pyramid algorithm to increase the speed of wavelet decomposition. The matlab simulation shows that the use of wavelet decomposition of the traction power supply system for harmonic spectrum analysis is effective.

Solar Wind drivers affecting GIC magnitude in New Zealand.

NASA Astrophysics Data System (ADS)

Mac Manus, D. H.; Rodger, C. J.; Dalzell, M.; Petersen, T.; Clilverd, M. A.

2017-12-01

Interplanetary shocks arriving at the Earth drive magnetosphere and ionosphere current systems. Ground based magnetometers detect the time derivation of the horizontal magnetic field (dBH/dt) which can indicate the strength of these ionospheric currents. The strong dBH/dt spikes have been observed to cause large Geomagnetically Induced Currents (GIC) in New Zealand. Such could, potentially lead to large scale damage to technological infrastructure such as power network transformers; one transformer was written off in New Zealand after a sudden commencement on 6 November 2001. The strength of the incoming interplanetary shocks are monitored by satellite measurements undertaken at the L1 point. Such measurements could give power network operators a 20-60 minute warning before potentially damaging GIC occurs. In this presentation we examine solar wind measurements from the Advanced Composition Explorer (ACE), Wind, and the Solar and Heliospheric Observatory (SOHO). We contrast those solar wind observations with GIC measured in New Zealand's South Island from 2001 to 2016. We are searching for a consistent relationship between the incoming interplanetary shock and the GIC magnitude. Such a relationship would allow Transpower New Zealand Limited a small time window to implement mitigation plans in order to restrict any GIC-caused damage.

[Measurements of stable isotopes in atmospheric CO2 and H2O by open-path Fourier transform infrared spectrometry].

PubMed

Wang, Wei; Liu, Wen-Qing; Zhang, Tian-Shu

2013-08-01

The development of spectroscopic techniques has offered continuous measurement of stable isotopes in the ambient air. The method of measuring environmental stable isotopes based on Fourier transform infrared spectrometry (FTIR) is described. In order to verify the feasibility of the method for continuous measurement of the stable isotopes, an open-path FTIR system was used to measure stable isotopes of CO2 and H2O in ambient air directly in a seven-day field experiment, including 12CO2, 3CO2, H2 16O and HD16 O. Also, the time course of carbon isotopic ratio delta13 C and deuterium isotope composition deltaD was calculated. The measurement precision is about 1.08 per thousand for delta13 C and 1.32 per thousand for deltaD. The measured stable isotopes of CO2 and H2O were analyzed on different time scales by Keeling plot methods, and the deuterium isotopic ratios of evapotranspiration were determined. The results of the field experiment demonstrate the potential of the open-path FTIR system for continuous measurement of stable isotopes in the air.

Continuous fast Fourier transforms cyclic voltammetry as a new approach for investigation of skim milk k-casein proteolysis, a comparative study.

PubMed

Shayeh, Javad Shabani; Sefidbakht, Yahya; Siadat, Seyed Omid Ranaei; Niknam, Kaveh

2017-10-01

Cheese production is relied upon the action of Rennet on the casein micelles of milk. Chymosin assay methods are usually time consuming and offline. Herein, we report a new electrochemical technique for studying the proteolysis of K-casein. The interaction of rennet and its substrate was studied by fast Fourier transform continuous cyclic voltammetry (FFTCCV) based on a determination of k-casein in aqueous solution. FFTCCV technique is a very useful method for studying the enzymatic procedures. Fast response, no need of modified electrodes or complex equipment is some of FFTCCV advantages. Various concentrations of enzyme and substrate were selected and the increase in the appearance of charged species in solution as a result of the addition of rennet was studied. Data obtained using FFTCCV technique were also confirmed by turbidity analysis. The results show that rennet proteolysis activity occurs in much shorter time scales compare with its aggregation. Hence, following the appearance of charged segments as a result of proteolysis could be under consideration as a rapid and online method. Copyright © 2017 Elsevier B.V. All rights reserved.

Understanding Antipsychotic Drug Treatment Effects: A Novel Method to Reduce Pseudospecificity of the Positive and Negative Syndrome Scale (PANSS) Factors.

PubMed

Hopkins, Seth C; Ogirala, Ajay; Loebel, Antony; Koblan, Kenneth S

2017-12-01

The Positive and Negative Syndrome Scale (PANSS) is the most widely used efficacy measure in acute treatment studies of schizophrenia. However, interpretation of the efficacy of antipsychotics in improving specific symptom domains is confounded by moderate-to-high correlations among standard (Marder) PANSS factors. The authors review the results of an uncorrelated PANSS score matrix (UPSM) transform designed to reduce pseudospecificity in assessment of symptom change in patients with schizophrenia. Based on a factor analysis of five pooled, placebo-controlled lurasidone clinical trials (N=1,710 patients), a UPSM transform was identified that generated PANSS factors with high face validity (good correlation with standard Marder PANSS factors), and high specificity/orthogonality (low levels of between-factor correlation measuring change during treatment). Between-factor correlations were low at baseline for both standard (Marder) PANSS factors and transformed PANSS factors. However, when measured change in symptom severity was measured during treatment (in a pooled 5-study analysis), there was a notable difference for standard PANSS factors, where changes across factors were found to be highly correlated (factors exhibited pseudospecificity), compared to transformed PANSS factors, where factor change scores exhibited the same low levels of between-factor correlation observed at baseline. At Week 6-endpoint, correlations among PANSS factor severity scores were moderate-to-high for standard factors (0.34-0.68), but continued to be low for the transformed factors (-0.22-0.20). As an additional validity check, we analyzed data from one of the original five pooled clinical trials that included other well-validated assessment scales (MADRS, Negative Symptom Assessment scale [NSA]). In this baseline analysis, UPSM-transformed PANSS factor severity scores (negative and depression factors) were found to correlate well with the MADRS and NSA. The availability of transformed PANSS factors with a high degree of orthogonality/specificity, but which retain a high degree of concurrent and face validity, can reduce pseudospecificity as a measurement confound, and should facilitate the drug development process, permitting a more accurate characterization of the efficacy of putative new agents in targeting specific symptom domains in patients with psychotic illness.

Dissecting the mechanism of martensitic transformation via atomic-scale observations.

PubMed

Yang, Xu-Sheng; Sun, Sheng; Wu, Xiao-Lei; Ma, Evan; Zhang, Tong-Yi

2014-08-21

Martensitic transformation plays a pivotal role in the microstructural evolution and plasticity of many engineering materials. However, so far the underlying atomic processes that accomplish the displacive transformation have been obscured by the difficulty in directly observing key microstructural signatures on atomic scale. To resolve this long-standing problem, here we examine an AISI 304 austenitic stainless steel that has a strain/microstructure-gradient induced by surface mechanical attrition, which allowed us to capture in one sample all the key interphase regions generated during the γ(fcc) → ε(hcp) → α'(bcc) transition, a prototypical case of deformation induced martensitic transformation (DIMT). High-resolution transmission electron microscopy (HRTEM) observations confirm the crucial role of partial dislocations, and reveal tell-tale features including the lattice rotation of the α' martensite inclusion, the transition lattices at the ε/α' interfaces that cater the shears, and the excess reverse shear-shuffling induced γ necks in the ε martensite plates. These direct observations verify for the first time the 50-year-old Bogers-Burgers-Olson-Cohen (BBOC) model, and enrich our understanding of DIMT mechanisms. Our findings have implications for improved microstructural control in metals and alloys.

Dissecting the Mechanism of Martensitic Transformation via Atomic-Scale Observations

PubMed Central

Yang, Xu-Sheng; Sun, Sheng; Wu, Xiao-Lei; Ma, Evan; Zhang, Tong-Yi

2014-01-01

Martensitic transformation plays a pivotal role in the microstructural evolution and plasticity of many engineering materials. However, so far the underlying atomic processes that accomplish the displacive transformation have been obscured by the difficulty in directly observing key microstructural signatures on atomic scale. To resolve this long-standing problem, here we examine an AISI 304 austenitic stainless steel that has a strain/microstructure-gradient induced by surface mechanical attrition, which allowed us to capture in one sample all the key interphase regions generated during the γ(fcc) → ε(hcp) → α′(bcc) transition, a prototypical case of deformation induced martensitic transformation (DIMT). High-resolution transmission electron microscopy (HRTEM) observations confirm the crucial role of partial dislocations, and reveal tell-tale features including the lattice rotation of the α′ martensite inclusion, the transition lattices at the ε/α′ interfaces that cater the shears, and the excess reverse shear-shuffling induced γ necks in the ε martensite plates. These direct observations verify for the first time the 50-year-old Bogers-Burgers-Olson-Cohen (BBOC) model, and enrich our understanding of DIMT mechanisms. Our findings have implications for improved microstructural control in metals and alloys. PMID:25142283

Scale relativity theory and integrative systems biology: 2. Macroscopic quantum-type mechanics.

PubMed

Nottale, Laurent; Auffray, Charles

2008-05-01

In these two companion papers, we provide an overview and a brief history of the multiple roots, current developments and recent advances of integrative systems biology and identify multiscale integration as its grand challenge. Then we introduce the fundamental principles and the successive steps that have been followed in the construction of the scale relativity theory, which aims at describing the effects of a non-differentiable and fractal (i.e., explicitly scale dependent) geometry of space-time. The first paper of this series was devoted, in this new framework, to the construction from first principles of scale laws of increasing complexity, and to the discussion of some tentative applications of these laws to biological systems. In this second review and perspective paper, we describe the effects induced by the internal fractal structures of trajectories on motion in standard space. Their main consequence is the transformation of classical dynamics into a generalized, quantum-like self-organized dynamics. A Schrödinger-type equation is derived as an integral of the geodesic equation in a fractal space. We then indicate how gauge fields can be constructed from a geometric re-interpretation of gauge transformations as scale transformations in fractal space-time. Finally, we introduce a new tentative development of the theory, in which quantum laws would hold also in scale space, introducing complexergy as a measure of organizational complexity. Initial possible applications of this extended framework to the processes of morphogenesis and the emergence of prokaryotic and eukaryotic cellular structures are discussed. Having founded elements of the evolutionary, developmental, biochemical and cellular theories on the first principles of scale relativity theory, we introduce proposals for the construction of an integrative theory of life and for the design and implementation of novel macroscopic quantum-type experiments and devices, and discuss their potential applications for the analysis, engineering and management of physical and biological systems and properties, and the consequences for the organization of transdisciplinary research and the scientific curriculum in the context of the SYSTEMOSCOPE Consortium research and development agenda.

Energy Barriers and Hysteresis in Martensitic Phase Transformations

DTIC Science & Technology

2008-08-01

glacial acetic acid (CH3COOH) and 10-15% perchloric acid (HCLO4) by volume, the cathode was stainless steel , the anode was stainless steel or Ti, the...Submitted to Acta Materialia Energy barriers and hysteresis in martensitic phase transformations Zhiyong Zhang, Richard D. James and Stefan Müller...hysteresis based on the growth from a small scale of fully developed austenite martensite needles. In this theory the energy of the transition layer plays a

String duality transformations in f(R) gravity from Noether symmetry approach

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

Capozziello, Salvatore; Gionti, Gabriele S.J.; Vernieri, Daniele, E-mail: capozziello@na.inf.it, E-mail: ggionti@as.arizona.edu, E-mail: vernieri@iap.fr

2016-01-01

We select f(R) gravity models that undergo scale factor duality transformations. As a starting point, we consider the tree-level effective gravitational action of bosonic String Theory coupled with the dilaton field. This theory inherits the Busher's duality of its parent String Theory. Using conformal transformations of the metric tensor, it is possible to map the tree-level dilaton-graviton string effective action into f(R) gravity, relating the dilaton field to the Ricci scalar curvature. Furthermore, the duality can be framed under the standard of Noether symmetries and exact cosmological solutions are derived. Using suitable changes of variables, the string-based f(R) Lagrangians aremore » shown in cases where the duality transformation becomes a parity inversion.« less

Challenges in Modeling of the Global Atmosphere

NASA Astrophysics Data System (ADS)

Janjic, Zavisa; Djurdjevic, Vladimir; Vasic, Ratko; Black, Tom

2015-04-01

The massively parallel computer architectures require that some widely adopted modeling paradigms be reconsidered in order to utilize more productively the power of parallel processing. For high computational efficiency with distributed memory, each core should work on a small subdomain of the full integration domain, and exchange only few rows of halo data with the neighbouring cores. However, the described scenario implies that the discretization used in the model is horizontally local. The spherical geometry further complicates the problem. Various grid topologies will be discussed and examples will be shown. The latitude-longitude grid with local in space and explicit in time differencing has been an early choice and remained in use ever since. The problem with this method is that the grid size in the longitudinal direction tends to zero as the poles are approached. So, in addition to having unnecessarily high resolution near the poles, polar filtering has to be applied in order to use a time step of decent size. However, the polar filtering requires transpositions involving extra communications. The spectral transform method and the semi-implicit semi-Lagrangian schemes opened the way for a wide application of the spectral representation. With some variations, these techniques are used in most major centers. However, the horizontal non-locality is inherent to the spectral representation and implicit time differencing, which inhibits scaling on a large number of cores. In this respect the lat-lon grid with a fast Fourier transform represents a significant step in the right direction, particularly at high resolutions where the Legendre transforms become increasingly expensive. Other grids with reduced variability of grid distances such as various versions of the cubed sphere and the hexagonal/pentagonal ("soccer ball") grids were proposed almost fifty years ago. However, on these grids, large-scale (wavenumber 4 and 5) fictitious solutions ("grid imprinting") with significant amplitudes can develop. Due to their large scales, that are comparable to the scales of the dominant Rossby waves, such fictitious solutions are hard to identify and remove. Another new challenge on the global scale is that the limit of validity of the hydrostatic approximation is rapidly being approached. Having in mind the sensitivity of extended deterministic forecasts to small disturbances, we may need global non-hydrostatic models sooner than we think. The unified Non-hydrostatic Multi-scale Model (NMMB) that is being developed at the National Centers for Environmental Prediction (NCEP) as a part of the new NOAA Environmental Modeling System (NEMS) will be discussed as an example. The non-hydrostatic dynamics were designed in such a way as to avoid over-specification. The global version is run on the latitude-longitude grid, and the polar filter selectively slows down the waves that would otherwise be unstable. The model formulation has been successfully tested on various scales. A global forecasting system based on the NMMB has been run in order to test and tune the model. The skill of the medium range forecasts produced by the NMMB is comparable to that of other major medium range models. The computational efficiency of the global NMMB on parallel computers is good.

Performance comparison of ISAR imaging method based on time frequency transforms

NASA Astrophysics Data System (ADS)

Xie, Chunjian; Guo, Chenjiang; Xu, Jiadong

2013-03-01

Inverse synthetic aperture radar (ISAR) can image the moving target, especially the target in the air, so it is important in the air defence and missile defence system. Time-frequency Transform was applied to ISAR imaging process widely. Several time frequency transforms were introduced. Noise jamming methods were analysed, and when these noise jamming were added to the echo of the ISAR receiver, the image can become blur even can't to be identify. But the effect is different to the different time frequency analysis. The results of simulation experiment show the Performance Comparison of the method.

Where and why hyporheic exchange is important: Inferences from a parsimonious, physically-based river network model

NASA Astrophysics Data System (ADS)

Gomez-Velez, J. D.; Harvey, J. W.

2014-12-01

Hyporheic exchange has been hypothesized to have basin-scale consequences; however, predictions throughout river networks are limited by available geomorphic and hydrogeologic data as well as models that can analyze and aggregate hyporheic exchange flows across large spatial scales. We developed a parsimonious but physically-based model of hyporheic flow for application in large river basins: Networks with EXchange and Subsurface Storage (NEXSS). At the core of NEXSS is a characterization of the channel geometry, geomorphic features, and related hydraulic drivers based on scaling equations from the literature and readily accessible information such as river discharge, bankfull width, median grain size, sinuosity, channel slope, and regional groundwater gradients. Multi-scale hyporheic flow is computed based on combining simple but powerful analytical and numerical expressions that have been previously published. We applied NEXSS across a broad range of geomorphic diversity in river reaches and synthetic river networks. NEXSS demonstrates that vertical exchange beneath submerged bedforms dominates hyporheic fluxes and turnover rates along the river corridor. Moreover, the hyporheic zone's potential for biogeochemical transformations is comparable across stream orders, but the abundance of lower-order channels results in a considerably higher cumulative effect for low-order streams. Thus, vertical exchange beneath submerged bedforms has more potential for biogeochemical transformations than lateral exchange beneath banks, although lateral exchange through meanders may be important in large rivers. These results have implications for predicting outcomes of river and basin management practices.

[Recognition of landscape characteristic scale based on two-dimension wavelet analysis].

PubMed

Gao, Yan-Ni; Chen, Wei; He, Xing-Yuan; Li, Xiao-Yu

2010-06-01

Three wavelet bases, i. e., Haar, Daubechies, and Symlet, were chosen to analyze the validity of two-dimension wavelet analysis in recognizing the characteristic scales of the urban, peri-urban, and rural landscapes of Shenyang. Owing to the transform scale of two-dimension wavelet must be the integer power of 2, some characteristic scales cannot be accurately recognized. Therefore, the pixel resolution of images was resampled to 3, 3.5, 4, and 4.5 m to densify the scale in analysis. It was shown that two-dimension wavelet analysis worked effectively in checking characteristic scale. Haar, Daubechies, and Symle were the optimal wavelet bases to the peri-urban landscape, urban landscape, and rural landscape, respectively. Both Haar basis and Symlet basis played good roles in recognizing the fine characteristic scale of rural landscape and in detecting the boundary of peri-urban landscape. Daubechies basis and Symlet basis could be also used to detect the boundary of urban landscape and rural landscape, respectively.

Analysis of HD 73045 light curve data

NASA Astrophysics Data System (ADS)

Das, Mrinal Kanti; Bhatraju, Naveen Kumar; Joshi, Santosh

2018-04-01

In this work we analyzed the Kepler light curve data of HD 73045. The raw data has been smoothened using standard filters. The power spectrum has been obtained by using a fast Fourier transform routine. It shows the presence of more than one period. In order to take care of any non-stationary behavior, we carried out a wavelet analysis to obtain the wavelet power spectrum. In addition, to identify the scale invariant structure, the data has been analyzed using a multifractal detrended fluctuation analysis. Further to characterize the diversity of embedded patterns in the HD 73045 flux time series, we computed various entropy-based complexity measures e.g. sample entropy, spectral entropy and permutation entropy. The presence of periodic structure in the time series was further analyzed using the visibility network and horizontal visibility network model of the time series. The degree distributions in the two network models confirm such structures.

Adaptive synchrosqueezing based on a quilted short-time Fourier transform

NASA Astrophysics Data System (ADS)

Berrian, Alexander; Saito, Naoki

2017-08-01

In recent years, the synchrosqueezing transform (SST) has gained popularity as a method for the analysis of signals that can be broken down into multiple components determined by instantaneous amplitudes and phases. One such version of SST, based on the short-time Fourier transform (STFT), enables the sharpening of instantaneous frequency (IF) information derived from the STFT, as well as the separation of amplitude-phase components corresponding to distinct IF curves. However, this SST is limited by the time-frequency resolution of the underlying window function, and may not resolve signals exhibiting diverse time-frequency behaviors with sufficient accuracy. In this work, we develop a framework for an SST based on a "quilted" short-time Fourier transform (SST-QSTFT), which allows adaptation to signal behavior in separate time-frequency regions through the use of multiple windows. This motivates us to introduce a discrete reassignment frequency formula based on a finite difference of the phase spectrum, ensuring computational accuracy for a wider variety of windows. We develop a theoretical framework for the SST-QSTFT in both the continuous and the discrete settings, and describe an algorithm for the automatic selection of optimal windows depending on the region of interest. Using synthetic data, we demonstrate the superior numerical performance of SST-QSTFT relative to other SST methods in a noisy context. Finally, we apply SST-QSTFT to audio recordings of animal calls to demonstrate the potential of our method for the analysis of real bioacoustic signals.

Ultrafast and versatile spectroscopy by temporal Fourier transform

NASA Astrophysics Data System (ADS)

Zhang, Chi; Wei, Xiaoming; Marhic, Michel E.; Wong, Kenneth K. Y.

2014-06-01

One of the most remarkable and useful properties of a spatially converging lens system is its inherent ability to perform the Fourier transform; the same applies for the time-lens system. At the back focal plane of the time-lens, the spectral information can be instantaneously obtained in the time axis. By implementing temporal Fourier transform for spectroscopy applications, this time-lens-based architecture can provide orders of magnitude improvement over the state-of-art spatial-dispersion-based spectroscopy in terms of the frame rate. On the other hand, in addition to the single-lens structure, the multi-lens structures (e.g. telescope or wide-angle scope) will provide very versatile operating conditions. Leveraging the merit of instantaneous response, as well as the flexible lens structure, here we present a 100-MHz frame rate spectroscopy system - the parametric spectro-temporal analyzer (PASTA), which achieves 17 times zoom in/out ratio for different observation ranges.

Upscaling of Hydraulic Conductivity using the Double Constraint Method

NASA Astrophysics Data System (ADS)

El-Rawy, Mustafa; Zijl, Wouter; Batelaan, Okke

2013-04-01

The mathematics and modeling of flow through porous media is playing an increasingly important role for the groundwater supply, subsurface contaminant remediation and petroleum reservoir engineering. In hydrogeology hydraulic conductivity data are often collected at a scale that is smaller than the grid block dimensions of a groundwater model (e.g. MODFLOW). For instance, hydraulic conductivities determined from the field using slug and packer tests are measured in the order of centimeters to meters, whereas numerical groundwater models require conductivities representative of tens to hundreds of meters of grid cell length. Therefore, there is a need for upscaling to decrease the number of grid blocks in a groundwater flow model. Moreover, models with relatively few grid blocks are simpler to apply, especially when the model has to run many times, as is the case when it is used to assimilate time-dependent data. Since the 1960s different methods have been used to transform a detailed description of the spatial variability of hydraulic conductivity to a coarser description. In this work we will investigate a relatively simple, but instructive approach: the Double Constraint Method (DCM) to identify the coarse-scale conductivities to decrease the number of grid blocks. Its main advantages are robustness and easy implementation, enabling to base computations on any standard flow code with some post processing added. The inversion step of the double constraint method is based on a first forward run with all known fluxes on the boundary and in the wells, followed by a second forward run based on the heads measured on the phreatic surface (i.e. measured in shallow observation wells) and in deeper observation wells. Upscaling, in turn is inverse modeling (DCM) to determine conductivities in coarse-scale grid blocks from conductivities in fine-scale grid blocks. In such a way that the head and flux boundary conditions applied to the fine-scale model are also honored at the coarse-scale. Exemplification will be presented for the Kleine Nete catchment, Belgium. As a result we identified coarse-scale conductivities while decreasing the number of grid blocks with the advantage that a model run costs less computation time and requires less memory space. In addition, ranking of models was investigated.

SU-E-J-237: Image Feature Based DRR and Portal Image Registration

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

Wang, X; Chang, J

Purpose: Two-dimensional (2D) matching of the kV X-ray and digitally reconstructed radiography (DRR) images is an important setup technique for image-guided radiotherapy (IGRT). In our clinics, mutual information based methods are used for this purpose on commercial linear accelerators, but with often needs for manual corrections. This work proved the feasibility that feature based image transform can be used to register kV and DRR images. Methods: The scale invariant feature transform (SIFT) method was implemented to detect the matching image details (or key points) between the kV and DRR images. These key points represent high image intensity gradients, and thusmore » the scale invariant features. Due to the poor image contrast from our kV image, direct application of the SIFT method yielded many detection errors. To assist the finding of key points, the center coordinates of the kV and DRR images were read from the DICOM header, and the two groups of key points with similar relative positions to their corresponding centers were paired up. Using these points, a rigid transform (with scaling, horizontal and vertical shifts) was estimated. We also artificially introduced vertical and horizontal shifts to test the accuracy of our registration method on anterior-posterior (AP) and lateral pelvic images. Results: The results provided a satisfactory overlay of the transformed kV onto the DRR image. The introduced vs. detected shifts were fit into a linear regression. In the AP image experiments, linear regression analysis showed a slope of 1.15 and 0.98 with an R2 of 0.89 and 0.99 for the horizontal and vertical shifts, respectively. The results are 1.2 and 1.3 with R2 of 0.72 and 0.82 for the lateral image shifts. Conclusion: This work provided an alternative technique for kV to DRR alignment. Further improvements in the estimation accuracy and image contrast tolerance are underway.« less

Quantification of pathogen inactivation efficacy by free chlorine disinfection of drinking water for QMRA.

PubMed

Petterson, S R; Stenström, T A

2015-09-01

To support the implementation of quantitative microbial risk assessment (QMRA) for managing infectious risks associated with drinking water systems, a simple modeling approach for quantifying Log10 reduction across a free chlorine disinfection contactor was developed. The study was undertaken in three stages: firstly, review of the laboratory studies published in the literature; secondly, development of a conceptual approach to apply the laboratory studies to full-scale conditions; and finally implementation of the calculations for a hypothetical case study system. The developed model explicitly accounted for variability in residence time and pathogen specific chlorine sensitivity. Survival functions were constructed for a range of pathogens relying on the upper bound of the reported data transformed to a common metric. The application of the model within a hypothetical case study demonstrated the importance of accounting for variable residence time in QMRA. While the overall Log10 reduction may appear high, small parcels of water with short residence time can compromise the overall performance of the barrier. While theoretically simple, the approach presented is of great value for undertaking an initial assessment of a full-scale disinfection contactor based on limited site-specific information.

Competitive Self-Assembly Manifests Supramolecular Darwinism in Soft-Oxometalates

NASA Astrophysics Data System (ADS)

Das, Santu; Kumar, Saurabh; Mallick, Apabrita; Roy, Soumyajit

2015-09-01

Topological transformation manifested in inorganic materials shows manifold possibilities. In our present work, we show a clear topological transformation in a soft-oxometalate (SOM) system which was formed from its polyoxometalate (POM) precursor [PMo12@Mo72Fe30]. This topological transformation was observed due to time dependent competitive self-assembly of two different length scale soft-oxometalate moieties formed from this two-component host-guest reaction. We characterized different morphologies by scanning electron microscopy, electron dispersive scattering spectroscopy, dynamic light scattering, horizontal attenuated total reflection-infrared spectroscopy and Raman spectroscopy. The predominant structure is selected by its size in a sort of supramolecular Darwinian competition in this process and is described here.

a Signal-Tuned Gabor Transform with Application to Eeg Analysis

NASA Astrophysics Data System (ADS)

Torreão, José R. A.; Victer, Silvia M. C.; Fernandes, João L.

2013-04-01

We introduce a time-frequency transform based on Gabor functions whose parameters are given by the Fourier transform of the analyzed signal. At any given frequency, the width and the phase of the Gabor function are obtained, respectively, from the magnitude and the phase of the signal's corresponding Fourier component, yielding an analyzing kernel which is a representation of the signal's content at that particular frequency. The resulting Gabor transform tunes itself to the input signal, allowing the accurate detection of time and frequency events, even in situations where the traditional Gabor and S-transform approaches tend to fail. This is the case, for instance, when considering the time-frequency representation of electroencephalogram traces (EEG) of epileptic subjects, as illustrated by the experimental study presented here.

Analysis of photonic Doppler velocimetry data based on the continuous wavelet transform

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

Liu Shouxian; Wang Detian; Li Tao

2011-02-15

The short time Fourier transform (STFT) cannot resolve rapid velocity changes in most photonic Doppler velocimetry (PDV) data. A practical analysis method based on the continuous wavelet transform (CWT) was presented to overcome this difficulty. The adaptability of the wavelet family predicates that the continuous wavelet transform uses an adaptive time window to estimate the instantaneous frequency of signals. The local frequencies of signal are accurately determined by finding the ridge in the spectrogram of the CWT and then are converted to target velocity according to the Doppler effects. A performance comparison between the CWT and STFT is demonstrated bymore » a plate-impact experiment data. The results illustrate that the new method is automatic and adequate for analysis of PDV data.« less

Improving the accuracy and efficiency of time-resolved electronic spectra calculations: cellular dephasing representation with a prefactor.

PubMed

Zambrano, Eduardo; Šulc, Miroslav; Vaníček, Jiří

2013-08-07

Time-resolved electronic spectra can be obtained as the Fourier transform of a special type of time correlation function known as fidelity amplitude, which, in turn, can be evaluated approximately and efficiently with the dephasing representation. Here we improve both the accuracy of this approximation-with an amplitude correction derived from the phase-space propagator-and its efficiency-with an improved cellular scheme employing inverse Weierstrass transform and optimal scaling of the cell size. We demonstrate the advantages of the new methodology by computing dispersed time-resolved stimulated emission spectra in the harmonic potential, pyrazine, and the NCO molecule. In contrast, we show that in strongly chaotic systems such as the quartic oscillator the original dephasing representation is more appropriate than either the cellular or prefactor-corrected methods.

Formation of soluble mercury oxide coatings: Transformation of elemental mercury in soils

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

Miller, Carrie L.; Watson, David B.; Lester, Brian P.

2015-09-21

In this study, the impact of mercury (Hg) on human and ecological health has been known for decades. Although a treaty signed in 2013 by 147 nations regulates future large-scale mercury emissions, legacy Hg contamination exists worldwide and small-scale releases will continue. The fate of elemental mercury, Hg(0), lost to the subsurface and its potential chemical transformation that can lead to changes in speciation and mobility are poorly understood. Here, we show that Hg(0) beads interact with soil or manganese oxide solids and X-ray spectroscopic analysis indicates that the soluble mercury coatings are HgO. Dissolution studies show that, after reactingmore » with a composite soil, >20 times more Hg is released into water from the coated beads than from a pure liquid mercury bead. An even larger, >700 times, release occurs from coated Hg(0) beads that have been reacted with manganese oxide, suggesting that manganese oxides are involved in the transformation of the Hg(0) beads and creation of the soluble mercury coatings. Although the coatings may inhibit Hg(0) evaporation, the high solubility of the coatings can enhance Hg(II) migration away from the Hg(0)-spill site and result in potential changes in mercury speciation in the soil and increased mercury mobility.« less

Terahertz bandwidth all-optical Hilbert transformers based on long-period gratings.

PubMed

Ashrafi, Reza; Azaña, José

2012-07-01

A novel, all-optical design for implementing terahertz (THz) bandwidth real-time Hilbert transformers is proposed and numerically demonstrated. An all-optical Hilbert transformer can be implemented using a uniform-period long-period grating (LPG) with a properly designed amplitude-only grating apodization profile, incorporating a single π-phase shift in the middle of the grating length. The designed LPG-based Hilbert transformers can be practically implemented using either fiber-optic or integrated-waveguide technologies. As a generalization, photonic fractional Hilbert transformers are also designed based on the same optical platform. In this general case, the resulting LPGs have multiple π-phase shifts along the grating length. Our numerical simulations confirm that all-optical Hilbert transformers capable of processing arbitrary optical signals with bandwidths well in the THz range can be implemented using feasible fiber/waveguide LPG designs.

International Linear Collider Reference Design Report

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

Brau, James,; Okada, Yasuhiro,; Walker, Nicholas J.,

2007-08-13

{lg_bullet} What is the universe? How did it begin? {lg_bullet} What are matter and energy? What are space and time? These basic questions have been the subject of scientific theories and experiments throughout human history. The answers have revolutionized the enlightened view of the world, transforming society and advancing civilization. Universal laws and principles govern everyday phenomena, some of them manifesting themselves only at scales of time and distance far beyond everyday experience. Particle physics experiments using particle accelerators transform matter and energy, to reveal the basic workings of the universe. Other experiments exploit naturally occurring particles, such as solarmore » neutrinos or cosmic rays, and astrophysical observations, to provide additional insights.« less

Scaling Analysis of Ocean Surface Turbulent Heterogeneities from Satellite Remote Sensing: Use of 2D Structure Functions.

PubMed

Renosh, P R; Schmitt, Francois G; Loisel, Hubert

2015-01-01

Satellite remote sensing observations allow the ocean surface to be sampled synoptically over large spatio-temporal scales. The images provided from visible and thermal infrared satellite observations are widely used in physical, biological, and ecological oceanography. The present work proposes a method to understand the multi-scaling properties of satellite products such as the Chlorophyll-a (Chl-a), and the Sea Surface Temperature (SST), rarely studied. The specific objectives of this study are to show how the small scale heterogeneities of satellite images can be characterised using tools borrowed from the fields of turbulence. For that purpose, we show how the structure function, which is classically used in the frame of scaling time series analysis, can be used also in 2D. The main advantage of this method is that it can be applied to process images which have missing data. Based on both simulated and real images, we demonstrate that coarse-graining (CG) of a gradient modulus transform of the original image does not provide correct scaling exponents. We show, using a fractional Brownian simulation in 2D, that the structure function (SF) can be used with randomly sampled couple of points, and verify that 1 million of couple of points provides enough statistics.

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

Seljak, Uroš, E-mail: useljak@berkeley.edu

On large scales a nonlinear transformation of matter density field can be viewed as a biased tracer of the density field itself. A nonlinear transformation also modifies the redshift space distortions in the same limit, giving rise to a velocity bias. In models with primordial nongaussianity a nonlinear transformation generates a scale dependent bias on large scales. We derive analytic expressions for the large scale bias, the velocity bias and the redshift space distortion (RSD) parameter β, as well as the scale dependent bias from primordial nongaussianity for a general nonlinear transformation. These biases can be expressed entirely in termsmore » of the one point distribution function (PDF) of the final field and the parameters of the transformation. The analysis shows that one can view the large scale bias different from unity and primordial nongaussianity bias as a consequence of converting higher order correlations in density into 2-point correlations of its nonlinear transform. Our analysis allows one to devise nonlinear transformations with nearly arbitrary bias properties, which can be used to increase the signal in the large scale clustering limit. We apply the results to the ionizing equilibrium model of Lyman-α forest, in which Lyman-α flux F is related to the density perturbation δ via a nonlinear transformation. Velocity bias can be expressed as an average over the Lyman-α flux PDF. At z = 2.4 we predict the velocity bias of -0.1, compared to the observed value of −0.13±0.03. Bias and primordial nongaussianity bias depend on the parameters of the transformation. Measurements of bias can thus be used to constrain these parameters, and for reasonable values of the ionizing background intensity we can match the predictions to observations. Matching to the observed values we predict the ratio of primordial nongaussianity bias to bias to have the opposite sign and lower magnitude than the corresponding values for the highly biased galaxies, but this depends on the model parameters and can also vanish or change the sign.« less

Log-polar mapping-based scale space tracking with adaptive target response

NASA Astrophysics Data System (ADS)

Li, Dongdong; Wen, Gongjian; Kuai, Yangliu; Zhang, Ximing

2017-05-01

Correlation filter-based tracking has exhibited impressive robustness and accuracy in recent years. Standard correlation filter-based trackers are restricted to translation estimation and equipped with fixed target response. These trackers produce an inferior performance when encountered with a significant scale variation or appearance change. We propose a log-polar mapping-based scale space tracker with an adaptive target response. This tracker transforms the scale variation of the target in the Cartesian space into a shift along the logarithmic axis in the log-polar space. A one-dimensional scale correlation filter is learned online to estimate the shift along the logarithmic axis. With the log-polar representation, scale estimation is achieved accurately without a multiresolution pyramid. To achieve an adaptive target response, a variance of the Gaussian function is computed from the response map and updated online with a learning rate parameter. Our log-polar mapping-based scale correlation filter and adaptive target response can be combined with any correlation filter-based trackers. In addition, the scale correlation filter can be extended to a two-dimensional correlation filter to achieve joint estimation of the scale variation and in-plane rotation. Experiments performed on an OTB50 benchmark demonstrate that our tracker achieves superior performance against state-of-the-art trackers.

Stochastic Kinetics on Networks: When Slow Is Fast

PubMed Central

2015-01-01

Most chemical and biological processes can be viewed as reaction networks in which different pathways often compete kinetically for transformation of substrates into products. An enzymatic process is an example of such phenomena when biological catalysts create new routes for chemical reactions to proceed. It is typically assumed that the general process of product formation is governed by the pathway with the fastest kinetics at all time scales. In contrast to the expectation, here we show theoretically that at time scales sufficiently short, reactions are predominantly determined by the shortest pathway (in the number of intermediate states), regardless of the average turnover time associated with each pathway. This universal phenomenon is demonstrated by an explicit calculation for a system with two competing reversible (or irreversible) pathways. The time scales that characterize this regime and its relevance for single-molecule experimental studies are also discussed. PMID:25140607

Anharmonic Infrared Spectroscopy through the Fourier Transform of Time Correlation Function Formalism in ONETEP.

PubMed

Vitale, Valerio; Dziedzic, Jacek; Dubois, Simon M-M; Fangohr, Hans; Skylaris, Chris-Kriton

2015-07-14

Density functional theory molecular dynamics (DFT-MD) provides an efficient framework for accurately computing several types of spectra. The major benefit of DFT-MD approaches lies in the ability to naturally take into account the effects of temperature and anharmonicity, without having to introduce any ad hoc or a posteriori corrections. Consequently, computational spectroscopy based on DFT-MD approaches plays a pivotal role in the understanding and assignment of experimental peaks and bands at finite temperature, particularly in the case of floppy molecules. Linear-scaling DFT methods can be used to study large and complex systems, such as peptides, DNA strands, amorphous solids, and molecules in solution. Here, we present the implementation of DFT-MD IR spectroscopy in the ONETEP linear-scaling code. In addition, two methods for partitioning the dipole moment within the ONETEP framework are presented. Dipole moment partitioning allows us to compute spectra of molecules in solution, which fully include the effects of the solvent, while at the same time removing the solvent contribution from the spectra.

A Study of Mexican Free-Tailed Bat Chirp Syllables: Bayesian Functional Mixed Models for Nonstationary Acoustic Time Series.

PubMed

Martinez, Josue G; Bohn, Kirsten M; Carroll, Raymond J; Morris, Jeffrey S

2013-06-01

We describe a new approach to analyze chirp syllables of free-tailed bats from two regions of Texas in which they are predominant: Austin and College Station. Our goal is to characterize any systematic regional differences in the mating chirps and assess whether individual bats have signature chirps. The data are analyzed by modeling spectrograms of the chirps as responses in a Bayesian functional mixed model. Given the variable chirp lengths, we compute the spectrograms on a relative time scale interpretable as the relative chirp position, using a variable window overlap based on chirp length. We use 2D wavelet transforms to capture correlation within the spectrogram in our modeling and obtain adaptive regularization of the estimates and inference for the regions-specific spectrograms. Our model includes random effect spectrograms at the bat level to account for correlation among chirps from the same bat, and to assess relative variability in chirp spectrograms within and between bats. The modeling of spectrograms using functional mixed models is a general approach for the analysis of replicated nonstationary time series, such as our acoustical signals, to relate aspects of the signals to various predictors, while accounting for between-signal structure. This can be done on raw spectrograms when all signals are of the same length, and can be done using spectrograms defined on a relative time scale for signals of variable length in settings where the idea of defining correspondence across signals based on relative position is sensible.

Instantaneous frequency time analysis of physiology signals: The application of pregnant women’s radial artery pulse signals

NASA Astrophysics Data System (ADS)

Su, Zhi-Yuan; Wang, Chuan-Chen; Wu, Tzuyin; Wang, Yeng-Tseng; Tang, Feng-Cheng

2008-01-01

This study used the Hilbert-Huang transform, a recently developed, instantaneous frequency-time analysis, to analyze radial artery pulse signals taken from women in their 36th week of pregnancy and after pregnancy. The acquired instantaneous frequency-time spectrum (Hilbert spectrum) is further compared with the Morlet wavelet spectrum. Results indicate that the Hilbert spectrum is especially suitable for analyzing the time series of non-stationary radial artery pulse signals since, in the Hilbert-Huang transform, signals are decomposed into different mode functions in accordance with signal’s local time scale. Therefore, the Hilbert spectrum contains more detailed information than the Morlet wavelet spectrum. From the Hilbert spectrum, we can see that radial artery pulse signals taken from women in their 36th week of pregnancy and after pregnancy have different patterns. This approach could be applied to facilitate non-invasive diagnosis of fetus’ physiological signals in the future.

Security Analysis of Image Encryption Based on Gyrator Transform by Searching the Rotation Angle with Improved PSO Algorithm.

PubMed

Sang, Jun; Zhao, Jun; Xiang, Zhili; Cai, Bin; Xiang, Hong

2015-08-05

Gyrator transform has been widely used for image encryption recently. For gyrator transform-based image encryption, the rotation angle used in the gyrator transform is one of the secret keys. In this paper, by analyzing the properties of the gyrator transform, an improved particle swarm optimization (PSO) algorithm was proposed to search the rotation angle in a single gyrator transform. Since the gyrator transform is continuous, it is time-consuming to exhaustedly search the rotation angle, even considering the data precision in a computer. Therefore, a computational intelligence-based search may be an alternative choice. Considering the properties of severe local convergence and obvious global fluctuations of the gyrator transform, an improved PSO algorithm was proposed to be suitable for such situations. The experimental results demonstrated that the proposed improved PSO algorithm can significantly improve the efficiency of searching the rotation angle in a single gyrator transform. Since gyrator transform is the foundation of image encryption in gyrator transform domains, the research on the method of searching the rotation angle in a single gyrator transform is useful for further study on the security of such image encryption algorithms.

Multiadaptive Bionic Wavelet Transform: Application to ECG Denoising and Baseline Wandering Reduction

NASA Astrophysics Data System (ADS)

Sayadi, Omid; Shamsollahi, Mohammad B.

2007-12-01

We present a new modified wavelet transform, called the multiadaptive bionic wavelet transform (MABWT), that can be applied to ECG signals in order to remove noise from them under a wide range of variations for noise. By using the definition of bionic wavelet transform and adaptively determining both the center frequency of each scale together with the[InlineEquation not available: see fulltext.]-function, the problem of desired signal decomposition is solved. Applying a new proposed thresholding rule works successfully in denoising the ECG. Moreover by using the multiadaptation scheme, lowpass noisy interference effects on the baseline of ECG will be removed as a direct task. The method was extensively clinically tested with real and simulated ECG signals which showed high performance of noise reduction, comparable to those of wavelet transform (WT). Quantitative evaluation of the proposed algorithm shows that the average SNR improvement of MABWT is 1.82 dB more than the WT-based results, for the best case. Also the procedure has largely proved advantageous over wavelet-based methods for baseline wandering cancellation, including both DC components and baseline drifts.

Orthogonal fast spherical Bessel transform on uniform grid

NASA Astrophysics Data System (ADS)

Serov, Vladislav V.

2017-07-01

We propose an algorithm for the orthogonal fast discrete spherical Bessel transform on a uniform grid. Our approach is based upon the spherical Bessel transform factorization into the two subsequent orthogonal transforms, namely the fast Fourier transform and the orthogonal transform founded on the derivatives of the discrete Legendre orthogonal polynomials. The method utility is illustrated by its implementation for the problem of a two-atomic molecule in a time-dependent external field simulating the one utilized in the attosecond streaking technique.

A decision support system for map projections of small scale data

USGS Publications Warehouse

Finn, Michael P.; Usery, E. Lynn; Posch, Stephan T.; Seong, Jeong Chang

2004-01-01

The use of commercial geographic information system software to process large raster datasets of terrain elevation, population, land cover, vegetation, soils, temperature, and rainfall requires both projection from spherical coordinates to plane coordinate systems and transformation from one plane system to another. Decision support systems deliver information resulting in knowledge that assists in policies, priorities, or processes. This paper presents an approach to handling the problems of raster dataset projection and transformation through the development of a Web-enabled decision support system to aid users of transformation processes with the selection of appropriate map projections based on data type, areal extent, location, and preservation properties.

A Transformation-Induced Shear Instability Model for Deep Earthquakes Based on Laboratory Nanoseismological and Microstructural Observations

NASA Astrophysics Data System (ADS)

Wang, Y.; Zhu, L.; Shi, F.; Schubnel, A.; Hilairet, N.; Yu, T.; Rivers, M. L.; Gasc, J.; Li, Z.; Brunet, F.

2016-12-01

Global earthquake hypocenters depth displays a bimodal distribution: a first peak at < 50 km and a second peak around 550 - 600 km, before ceasing abruptly near 700 km. How fractures initiate, nucleate, and propagate at depths >70 km remains one of the greatest puzzles in earth science, since increasing pressure inhibits fracture propagation. Here we report high-resolution acoustic emission (AE) analysis of fractures triggered by partial transformation from olivine to spinel in Mg2GeO4, an analog to (Mg,Fe)2SiO4, the dominant mineral in the upper mantle. State-of-the-art synchrotron techniques and seismological methodologies were used for fault imaging and for event location and waveform analysis. Our results reveal unprecedented details of rupture nucleation and propagation, in both space and time: AE event magnitudes follow the Gutenberg-Richter law, with b values generally consistent with seismological observations, while the empirical relation between magnitude and rupture area is extended to millimeter-sized samples. A new rupture model for deep-focus earthquakes is proposed based on the well-known strain localization theory for pressure sensitive (dilatant) materials. The results show that shear failure processes, even at great depths, are scale-invariant.

MITHRA 1.0: A full-wave simulation tool for free electron lasers

NASA Astrophysics Data System (ADS)

Fallahi, Arya; Yahaghi, Alireza; Kärtner, Franz X.

2018-07-01

Free Electron Lasers (FELs) are a solution for providing intense, coherent and bright radiation in the hard X-ray regime. Due to the low wall-plug efficiency of FEL facilities, it is crucial and additionally very useful to develop complete and accurate simulation tools for better optimizing a FEL interaction. The highly sophisticated dynamics involved in a FEL process was the main obstacle hindering the development of general simulation tools for this problem. We present a numerical algorithm based on finite difference time domain/Particle in cell (FDTD/PIC) in a Lorentz boosted coordinate system which is able to fulfill a full-wave simulation of a FEL process. The developed software offers a suitable tool for the analysis of FEL interactions without considering any of the usual approximations. A coordinate transformation to bunch rest frame makes the very different length scales of bunch size, optical wavelengths and the undulator period transform to values with the same order. Consequently, FDTD/PIC simulations in conjunction with efficient parallelization techniques make the full-wave simulation feasible using the available computational resources. Several examples of free electron lasers are analyzed using the developed software, the results are benchmarked based on standard FEL codes and discussed in detail.

Based on a multi-agent system for multi-scale simulation and application of household's LUCC: a case study for Mengcha village, Mizhi county, Shaanxi province.

PubMed

Chen, Hai; Liang, Xiaoying; Li, Rui

2013-01-01

Multi-Agent Systems (MAS) offer a conceptual approach to include multi-actor decision making into models of land use change. Through the simulation based on the MAS, this paper tries to show the application of MAS in the micro scale LUCC, and reveal the transformation mechanism of difference scale. This paper starts with a description of the context of MAS research. Then, it adopts the Nested Spatial Choice (NSC) method to construct the multi-scale LUCC decision-making model. And a case study for Mengcha village, Mizhi County, Shaanxi Province is reported. Finally, the potentials and drawbacks of the following approach is discussed and concluded. From our design and implementation of the MAS in multi-scale model, a number of observations and conclusions can be drawn on the implementation and future research directions. (1) The use of the LUCC decision-making and multi-scale transformation framework provides, according to us, a more realistic modeling of multi-scale decision making process. (2) By using continuous function, rather than discrete function, to construct the decision-making of the households is more realistic to reflect the effect. (3) In this paper, attempts have been made to give a quantitative analysis to research the household interaction. And it provides the premise and foundation for researching the communication and learning among the households. (4) The scale transformation architecture constructed in this paper helps to accumulate theory and experience for the interaction research between the micro land use decision-making and the macro land use landscape pattern. Our future research work will focus on: (1) how to rational use risk aversion principle, and put the rule on rotation between household parcels into model. (2) Exploring the methods aiming at researching the household decision-making over a long period, it allows us to find the bridge between the long-term LUCC data and the short-term household decision-making. (3) Researching the quantitative method and model, especially the scenario analysis model which may reflect the interaction among different household types.

Fingerprint Identification Using SIFT-Based Minutia Descriptors and Improved All Descriptor-Pair Matching

PubMed Central

Zhou, Ru; Zhong, Dexing; Han, Jiuqiang

2013-01-01

The performance of conventional minutiae-based fingerprint authentication algorithms degrades significantly when dealing with low quality fingerprints with lots of cuts or scratches. A similar degradation of the minutiae-based algorithms is observed when small overlapping areas appear because of the quite narrow width of the sensors. Based on the detection of minutiae, Scale Invariant Feature Transformation (SIFT) descriptors are employed to fulfill verification tasks in the above difficult scenarios. However, the original SIFT algorithm is not suitable for fingerprint because of: (1) the similar patterns of parallel ridges; and (2) high computational resource consumption. To enhance the efficiency and effectiveness of the algorithm for fingerprint verification, we propose a SIFT-based Minutia Descriptor (SMD) to improve the SIFT algorithm through image processing, descriptor extraction and matcher. A two-step fast matcher, named improved All Descriptor-Pair Matching (iADM), is also proposed to implement the 1:N verifications in real-time. Fingerprint Identification using SMD and iADM (FISiA) achieved a significant improvement with respect to accuracy in representative databases compared with the conventional minutiae-based method. The speed of FISiA also can meet real-time requirements. PMID:23467056

Characterization of coarse bainite transformation in low carbon steel during simulated welding thermal cycles

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

Lan, Liangyun, E-mail: lanly@me.neu.edu.cn; State Key Laboratory of Rolling Technology and Automation, Northeastern University, Shenyang 110819; Kong, Xiangwei

2015-07-15

Coarse austenite to bainite transformation in low carbon steel under simulated welding thermal cycles was morphologically and crystallographically characterized by means of optical microscope, transmission electron microscope and electron backscattered diffraction technology. The results showed that the main microstructure changes from a mixture of lath martensite and bainitic ferrite to granular bainite with the increase in cooling time. The width of bainitic laths also increases gradually with the cooling time. For a welding thermal cycle with relatively short cooling time (e.g. t{sub 8/5} is 30 s), the main mode of variant grouping at the scale of individual prior austenite grainsmore » changes from Bain grouping to close-packed plane grouping with the progress of phase transformation, which results in inhomogeneous distribution of high angle boundaries. As the cooling time is increased, the Bain grouping of variants becomes predominant mode, which enlarges the effective grain size of product phase. - Highlights: • Main microstructure changes and the width of lath structure increases with cooling time. • Variant grouping changes from Bain zone to close-packed plane grouping with the transformation. • The change of variant grouping results in uneven distribution of high angle grain boundary. • Bain grouping is main mode for large heat input, which lowers the density of high angle boundary.« less

GPU-accelerated non-uniform fast Fourier transform-based compressive sensing spectral domain optical coherence tomography.

PubMed

Xu, Daguang; Huang, Yong; Kang, Jin U

2014-06-16

We implemented the graphics processing unit (GPU) accelerated compressive sensing (CS) non-uniform in k-space spectral domain optical coherence tomography (SD OCT). Kaiser-Bessel (KB) function and Gaussian function are used independently as the convolution kernel in the gridding-based non-uniform fast Fourier transform (NUFFT) algorithm with different oversampling ratios and kernel widths. Our implementation is compared with the GPU-accelerated modified non-uniform discrete Fourier transform (MNUDFT) matrix-based CS SD OCT and the GPU-accelerated fast Fourier transform (FFT)-based CS SD OCT. It was found that our implementation has comparable performance to the GPU-accelerated MNUDFT-based CS SD OCT in terms of image quality while providing more than 5 times speed enhancement. When compared to the GPU-accelerated FFT based-CS SD OCT, it shows smaller background noise and less side lobes while eliminating the need for the cumbersome k-space grid filling and the k-linear calibration procedure. Finally, we demonstrated that by using a conventional desktop computer architecture having three GPUs, real-time B-mode imaging can be obtained in excess of 30 fps for the GPU-accelerated NUFFT based CS SD OCT with frame size 2048(axial) × 1,000(lateral).

Digital Transformation and Disruption of the Health Care Sector: Internet-Based Observational Study.

PubMed

Herrmann, Maximilian; Boehme, Philip; Mondritzki, Thomas; Ehlers, Jan P; Kavadias, Stylianos; Truebel, Hubert

2018-03-27

Digital innovation, introduced across many industries, is a strong force of transformation. Some industries have seen faster transformation, whereas the health care sector only recently came into focus. A context where digital corporations move into health care, payers strive to keep rising costs at bay, and longer-living patients desire continuously improved quality of care points to a digital and value-based transformation with drastic implications for the health care sector. We tried to operationalize the discussion within the health care sector around digital and disruptive innovation to identify what type of technological enablers, business models, and value networks seem to be emerging from different groups of innovators with respect to their digital transformational efforts. From the Forbes 2000 and CBinsights databases, we identified 100 leading technology, life science, and start-up companies active in the health care sector. Further analysis identified projects from these companies within a digital context that were subsequently evaluated using the following criteria: delivery of patient value, presence of a comprehensive and distinctive underlying business model, solutions provided, and customer needs addressed. Our methodological approach recorded more than 400 projects and collaborations. We identified patterns that show established corporations rely more on incremental innovation that supports their current business models, while start-ups engage their flexibility to explore new market segments with notable transformations of established business models. Thereby, start-ups offer higher promises of disruptive innovation. Additionally, start-ups offer more diversified value propositions addressing broader areas of the health care sector. Digital transformation is an opportunity to accelerate health care performance by lowering cost and improving quality of care. At an economic scale, business models can be strengthened and disruptive innovation models enabled. Corporations should look for collaborations with start-up companies to keep investment costs at bay and off the balance sheet. At the same time, the regulatory knowledge of established corporations might help start-ups to kick off digital disruption in the health care sector. ©Maximilian Herrmann, Philip Boehme, Thomas Mondritzki, Jan P Ehlers, Stylianos Kavadias, Hubert Truebel. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 27.03.2018.

Digital Transformation and Disruption of the Health Care Sector: Internet-Based Observational Study

PubMed Central

Mondritzki, Thomas; Ehlers, Jan P; Kavadias, Stylianos

2018-01-01

Background Digital innovation, introduced across many industries, is a strong force of transformation. Some industries have seen faster transformation, whereas the health care sector only recently came into focus. A context where digital corporations move into health care, payers strive to keep rising costs at bay, and longer-living patients desire continuously improved quality of care points to a digital and value-based transformation with drastic implications for the health care sector. Objective We tried to operationalize the discussion within the health care sector around digital and disruptive innovation to identify what type of technological enablers, business models, and value networks seem to be emerging from different groups of innovators with respect to their digital transformational efforts. Methods From the Forbes 2000 and CBinsights databases, we identified 100 leading technology, life science, and start-up companies active in the health care sector. Further analysis identified projects from these companies within a digital context that were subsequently evaluated using the following criteria: delivery of patient value, presence of a comprehensive and distinctive underlying business model, solutions provided, and customer needs addressed. Results Our methodological approach recorded more than 400 projects and collaborations. We identified patterns that show established corporations rely more on incremental innovation that supports their current business models, while start-ups engage their flexibility to explore new market segments with notable transformations of established business models. Thereby, start-ups offer higher promises of disruptive innovation. Additionally, start-ups offer more diversified value propositions addressing broader areas of the health care sector. Conclusions Digital transformation is an opportunity to accelerate health care performance by lowering cost and improving quality of care. At an economic scale, business models can be strengthened and disruptive innovation models enabled. Corporations should look for collaborations with start-up companies to keep investment costs at bay and off the balance sheet. At the same time, the regulatory knowledge of established corporations might help start-ups to kick off digital disruption in the health care sector. PMID:29588274

Multi-time scale dynamics in power electronics-dominated power systems

NASA Astrophysics Data System (ADS)

Yuan, Xiaoming; Hu, Jiabing; Cheng, Shijie

2017-09-01

Electric power infrastructure has recently undergone a comprehensive transformation from electromagnetics to semiconductors. Such a development is attributed to the rapid growth of power electronic converter applications in the load side to realize energy conservation and on the supply side for renewable generations and power transmissions using high voltage direct current transmission. This transformation has altered the fundamental mechanism of power system dynamics, which demands the establishment of a new theory for power system control and protection. This paper presents thoughts on a theoretical framework for the coming semiconducting power systems.