International Nuclear Information System (INIS)
Walter, H.
2004-01-01
Powerful tools to describe atmospheric transport processes for radiation protection can be provided by meteorology; these are atmospheric flow and dispersion models. Concerning dispersion models, Gaussian plume models have been used since a long time to describe atmospheric dispersion processes. Advantages of the Gaussian plume models are short computation time, good validation and broad acceptance worldwide. However, some limitations and their implications on model result interpretation have to be taken into account, as the mathematical derivation of an analytic solution of the equations of motion leads to severe constraints. In order to minimise these constraints, various dispersion models for scientific and regulatory purposes have been developed and applied. Among these the Lagrangian particle models are of special interest, because these models are able to simulate atmospheric transport processes close to reality, e.g. the influence of orography, topography, wind shear and other meteorological phenomena. Within this study, the characteristics and computational results of Gaussian dispersion models as well as of Lagrangian models have been compared and evaluated on the base of numerous papers and reports published in literature. Special emphasis has been laid on the intention that dispersion models should comply with EU requests (Richtlinie 96/29/Euratom, 1996) on a more realistic assessment of the radiation exposure to the population. (orig.)
Energy Technology Data Exchange (ETDEWEB)
Ma, Denglong [Fuli School of Food Equipment Engineering and Science, Xi’an Jiaotong University, No.28 Xianning West Road, Xi’an 710049 (China); Zhang, Zaoxiao, E-mail: zhangzx@mail.xjtu.edu.cn [State Key Laboratory of Multiphase Flow in Power Engineering, Xi’an Jiaotong University, No.28 Xianning West Road, Xi’an 710049 (China); School of Chemical Engineering and Technology, Xi’an Jiaotong University, No.28 Xianning West Road, Xi’an 710049 (China)
2016-07-05
Highlights: • The intelligent network models were built to predict contaminant gas concentrations. • The improved network models coupled with Gaussian dispersion model were presented. • New model has high efficiency and accuracy for concentration prediction. • New model were applied to indentify the leakage source with satisfied results. - Abstract: Gas dispersion model is important for predicting the gas concentrations when contaminant gas leakage occurs. Intelligent network models such as radial basis function (RBF), back propagation (BP) neural network and support vector machine (SVM) model can be used for gas dispersion prediction. However, the prediction results from these network models with too many inputs based on original monitoring parameters are not in good agreement with the experimental data. Then, a new series of machine learning algorithms (MLA) models combined classic Gaussian model with MLA algorithm has been presented. The prediction results from new models are improved greatly. Among these models, Gaussian-SVM model performs best and its computation time is close to that of classic Gaussian dispersion model. Finally, Gaussian-MLA models were applied to identifying the emission source parameters with the particle swarm optimization (PSO) method. The estimation performance of PSO with Gaussian-MLA is better than that with Gaussian, Lagrangian stochastic (LS) dispersion model and network models based on original monitoring parameters. Hence, the new prediction model based on Gaussian-MLA is potentially a good method to predict contaminant gas dispersion as well as a good forward model in emission source parameters identification problem.
International Nuclear Information System (INIS)
Ma, Denglong; Zhang, Zaoxiao
2016-01-01
Highlights: • The intelligent network models were built to predict contaminant gas concentrations. • The improved network models coupled with Gaussian dispersion model were presented. • New model has high efficiency and accuracy for concentration prediction. • New model were applied to indentify the leakage source with satisfied results. - Abstract: Gas dispersion model is important for predicting the gas concentrations when contaminant gas leakage occurs. Intelligent network models such as radial basis function (RBF), back propagation (BP) neural network and support vector machine (SVM) model can be used for gas dispersion prediction. However, the prediction results from these network models with too many inputs based on original monitoring parameters are not in good agreement with the experimental data. Then, a new series of machine learning algorithms (MLA) models combined classic Gaussian model with MLA algorithm has been presented. The prediction results from new models are improved greatly. Among these models, Gaussian-SVM model performs best and its computation time is close to that of classic Gaussian dispersion model. Finally, Gaussian-MLA models were applied to identifying the emission source parameters with the particle swarm optimization (PSO) method. The estimation performance of PSO with Gaussian-MLA is better than that with Gaussian, Lagrangian stochastic (LS) dispersion model and network models based on original monitoring parameters. Hence, the new prediction model based on Gaussian-MLA is potentially a good method to predict contaminant gas dispersion as well as a good forward model in emission source parameters identification problem.
Higher-Order Hybrid Gaussian Kernel in Meshsize Boosting Algorithm
African Journals Online (AJOL)
In this paper, we shall use higher-order hybrid Gaussian kernel in a meshsize boosting algorithm in kernel density estimation. Bias reduction is guaranteed in this scheme like other existing schemes but uses the higher-order hybrid Gaussian kernel instead of the regular fixed kernels. A numerical verification of this scheme ...
Dispersion under low wind speed conditions using Gaussian Plume approach
International Nuclear Information System (INIS)
Rakesh, P.T.; Srinivas, C.V.; Baskaran, R.; Venkatesan, R.; Venkatraman, B.
2018-01-01
For radioactive dose computation due to atmospheric releases, dispersion models are essential requirement. For this purpose, Gaussian plume model (GPM) is used in the short range and advanced particle dispersion models are used in all ranges. In dispersion models, other than wind speed the most influential parameter which determines the fate of the pollutant is the turbulence diffusivity. In GPM the diffusivity is represented using empirical approach. Studies show that under low wind speed conditions, the existing diffusivity relationships are not adequate in estimating the diffusion. An important phenomenon that occurs during the low wind speed is the meandering motions. It is found that under meandering motions the extent of plume dispersion is more than the estimated value using conventional GPM and particle transport models. In this work a set of new turbulence parameters for the horizontal diffusion of the plume is suggested and using them in GPM, the plume is simulated and is compared against observation available from Hanford tracer release experiment
EDPUFF- a Gaussian dispersion code for consequence analysis
International Nuclear Information System (INIS)
Oza, R.B.; Bapat, V.N.; Nair, R.N.; Hukkoo, R.K.; Krishnamoorthy, T.M.
1995-01-01
EDPUFF- Equi Distance Puff is a Gaussian dispersion code in FORTRAN language to model atmospheric dispersion of instantaneous or continuous point source releases. It is designed to incorporate the effect of changing meteorological conditions and source release rates on the spatial distribution profiles and its consequences. Effects of variation of parameters like puff spacing, puff packing, averaging schemes are discussed and the choice of the best values for minimum errors and minimum computer CPU time are identified. The code calculates the doses to individual receptors as well as average doses for population zones from internal and external routes over the area of interest. Internal dose computations are made for inhalation and ingestion pathways while the doses from external route consists of cloud doses and doses from surface deposited activity. It computes inhalation and ingestion dose (milk route only) for critical group (1 yr old child). In case of population zones it finds out maximum possible doses in a given area along with the average doses discussed above. Report gives the doses from various pathways for unit release of fixed duration. (author). 7 refs., figs., 7 appendixes
International Nuclear Information System (INIS)
Deng Dongmei; Guo Hong; Han Dingan; Liu Mingwei; Li Changfu
2005-01-01
Taking into account the dispersion and the longitudinal chromatic aberration (LCA) of the material of the lens, focusing of isodiffracting pulsed Gaussian light beam through single lens is analyzed. The smaller the cycle number of the isodiffracting pulsed Gaussian light beam is, the higher the order of the material dispersion should be considered
Iterative Mixture Component Pruning Algorithm for Gaussian Mixture PHD Filter
Directory of Open Access Journals (Sweden)
Xiaoxi Yan
2014-01-01
Full Text Available As far as the increasing number of mixture components in the Gaussian mixture PHD filter is concerned, an iterative mixture component pruning algorithm is proposed. The pruning algorithm is based on maximizing the posterior probability density of the mixture weights. The entropy distribution of the mixture weights is adopted as the prior distribution of mixture component parameters. The iterative update formulations of the mixture weights are derived by Lagrange multiplier and Lambert W function. Mixture components, whose weights become negative during iterative procedure, are pruned by setting corresponding mixture weights to zeros. In addition, multiple mixture components with similar parameters describing the same PHD peak can be merged into one mixture component in the algorithm. Simulation results show that the proposed iterative mixture component pruning algorithm is superior to the typical pruning algorithm based on thresholds.
Mixed Platoon Flow Dispersion Model Based on Speed-Truncated Gaussian Mixture Distribution
Directory of Open Access Journals (Sweden)
Weitiao Wu
2013-01-01
Full Text Available A mixed traffic flow feature is presented on urban arterials in China due to a large amount of buses. Based on field data, a macroscopic mixed platoon flow dispersion model (MPFDM was proposed to simulate the platoon dispersion process along the road section between two adjacent intersections from the flow view. More close to field observation, truncated Gaussian mixture distribution was adopted as the speed density distribution for mixed platoon. Expectation maximum (EM algorithm was used for parameters estimation. The relationship between the arriving flow distribution at downstream intersection and the departing flow distribution at upstream intersection was investigated using the proposed model. Comparison analysis using virtual flow data was performed between the Robertson model and the MPFDM. The results confirmed the validity of the proposed model.
Stochastic cluster algorithms for discrete Gaussian (SOS) models
International Nuclear Information System (INIS)
Evertz, H.G.; Hamburg Univ.; Hasenbusch, M.; Marcu, M.; Tel Aviv Univ.; Pinn, K.; Muenster Univ.; Solomon, S.
1990-10-01
We present new Monte Carlo cluster algorithms which eliminate critical slowing down in the simulation of solid-on-solid models. In this letter we focus on the two-dimensional discrete Gaussian model. The algorithms are based on reflecting the integer valued spin variables with respect to appropriately chosen reflection planes. The proper choice of the reflection plane turns out to be crucial in order to obtain a small dynamical exponent z. Actually, the successful versions of our algorithm are a mixture of two different procedures for choosing the reflection plane, one of them ergodic but slow, the other one non-ergodic and also slow when combined with a Metropolis algorithm. (orig.)
Blind signal processing algorithms under DC biased Gaussian noise
Kim, Namyong; Byun, Hyung-Gi; Lim, Jeong-Ok
2013-05-01
Distortions caused by the DC-biased laser input can be modeled as DC biased Gaussian noise and removing DC bias is important in the demodulation process of the electrical signal in most optical communications. In this paper, a new performance criterion and a related algorithm for unsupervised equalization are proposed for communication systems in the environment of channel distortions and DC biased Gaussian noise. The proposed criterion utilizes the Euclidean distance between the Dirac-delta function located at zero on the error axis and a probability density function of biased constant modulus errors, where constant modulus error is defined by the difference between the system out and a constant modulus calculated from the transmitted symbol points. From the results obtained from the simulation under channel models with fading and DC bias noise abruptly added to background Gaussian noise, the proposed algorithm converges rapidly even after the interruption of DC bias proving that the proposed criterion can be effectively applied to optical communication systems corrupted by channel distortions and DC bias noise.
Lawton, Pat
2004-01-01
The objective of this work was to support the design of improved IUE NEWSIPS high dispersion extraction algorithms. The purpose of this work was to evaluate use of the Linearized Image (LIHI) file versus the Re-Sampled Image (SIHI) file, evaluate various extraction, and design algorithms for evaluation of IUE High Dispersion spectra. It was concluded the use of the Re-Sampled Image (SIHI) file was acceptable. Since the Gaussian profile worked well for the core and the Lorentzian profile worked well for the wings, the Voigt profile was chosen for use in the extraction algorithm. It was found that the gamma and sigma parameters varied significantly across the detector, so gamma and sigma masks for the SWP detector were developed. Extraction code was written.
A wavelet-based Gaussian method for energy dispersive X-ray fluorescence spectrum
Directory of Open Access Journals (Sweden)
Pan Liu
2017-05-01
Full Text Available This paper presents a wavelet-based Gaussian method (WGM for the peak intensity estimation of energy dispersive X-ray fluorescence (EDXRF. The relationship between the parameters of Gaussian curve and the wavelet coefficients of Gaussian peak point is firstly established based on the Mexican hat wavelet. It is found that the Gaussian parameters can be accurately calculated by any two wavelet coefficients at the peak point which has to be known. This fact leads to a local Gaussian estimation method for spectral peaks, which estimates the Gaussian parameters based on the detail wavelet coefficients of Gaussian peak point. The proposed method is tested via simulated and measured spectra from an energy X-ray spectrometer, and compared with some existing methods. The results prove that the proposed method can directly estimate the peak intensity of EDXRF free from the background information, and also effectively distinguish overlap peaks in EDXRF spectrum.
A Scalable Gaussian Process Analysis Algorithm for Biomass Monitoring
Energy Technology Data Exchange (ETDEWEB)
Chandola, Varun [ORNL; Vatsavai, Raju [ORNL
2011-01-01
Biomass monitoring is vital for studying the carbon cycle of earth's ecosystem and has several significant implications, especially in the context of understanding climate change and its impacts. Recently, several change detection methods have been proposed to identify land cover changes in temporal profiles (time series) of vegetation collected using remote sensing instruments, but do not satisfy one or both of the two requirements of the biomass monitoring problem, i.e., {\\em operating in online mode} and {\\em handling periodic time series}. In this paper, we adapt Gaussian process regression to detect changes in such time series in an online fashion. While Gaussian process (GP) have been widely used as a kernel based learning method for regression and classification, their applicability to massive spatio-temporal data sets, such as remote sensing data, has been limited owing to the high computational costs involved. We focus on addressing the scalability issues associated with the proposed GP based change detection algorithm. This paper makes several significant contributions. First, we propose a GP based online time series change detection algorithm and demonstrate its effectiveness in detecting different types of changes in {\\em Normalized Difference Vegetation Index} (NDVI) data obtained from a study area in Iowa, USA. Second, we propose an efficient Toeplitz matrix based solution which significantly improves the computational complexity and memory requirements of the proposed GP based method. Specifically, the proposed solution can analyze a time series of length $t$ in $O(t^2)$ time while maintaining a $O(t)$ memory footprint, compared to the $O(t^3)$ time and $O(t^2)$ memory requirement of standard matrix manipulation based methods. Third, we describe a parallel version of the proposed solution which can be used to simultaneously analyze a large number of time series. We study three different parallel implementations: using threads, MPI, and a
MCEM algorithm for the log-Gaussian Cox process
Delmas, Celine; Dubois-Peyrard, Nathalie; Sabbadin, Regis
2014-01-01
Log-Gaussian Cox processes are an important class of models for aggregated point patterns. They have been largely used in spatial epidemiology (Diggle et al., 2005), in agronomy (Bourgeois et al., 2012), in forestry (Moller et al.), in ecology (sightings of wild animals) or in environmental sciences (radioactivity counts). A log-Gaussian Cox process is a Poisson process with a stochastic intensity depending on a Gaussian random eld. We consider the case where this Gaussian random eld is ...
State of the art atmospheric dispersion modelling. Should the Gaussian plume model still be used?
Energy Technology Data Exchange (ETDEWEB)
Richter, Cornelia [Gesellschaft fuer Anlagen- und Reaktorsicherheit (GRS) gGmbH, Koeln (Germany)
2016-11-15
For regulatory purposes with respect to licensing and supervision of airborne releases of nuclear installations, the Gaussian plume model is still in use in Germany. However, for complex situations the Gaussian plume model is to be replaced by a Lagrangian particle model. Now the new EU basic safety standards for protection against the dangers arising from exposure to ionising radiation (EU BSS) [1] asks for a realistic assessment of doses to the members of the public from authorised practices. This call for a realistic assessment raises the question whether dispersion modelling with the Gaussian plume model is an adequate approach anymore or whether the use of more complex models is mandatory.
A fast Gaussian filtering algorithm for three-dimensional surface roughness measurements
International Nuclear Information System (INIS)
Yuan, Y B; Piao, W Y; Xu, J B
2007-01-01
The two-dimensional (2-D) Gaussian filter can be separated into two one-dimensional (1-D) Gaussian filters. The 1-D Gaussian filter can be implemented approximately by the cascaded Butterworth filters. The approximation accuracy will be improved with the increase of the number of the cascaded filters. A recursive algorithm for Gaussian filtering requires a relatively small number of simple mathematical operations such as addition, subtraction, multiplication, or division, so that it has considerable computational efficiency and it is very useful for three-dimensional (3-D) surface roughness measurements. The zero-phase-filtering technique is used in this algorithm, so there is no phase distortion in the Gaussian filtered mean surface. High-order approximation Gaussian filters are proposed for practical use to assure high accuracy of Gaussian filtering of 3-D surface roughness measurements
A fast Gaussian filtering algorithm for three-dimensional surface roughness measurements
Yuan, Y. B.; Piao, W. Y.; Xu, J. B.
2007-07-01
The two-dimensional (2-D) Gaussian filter can be separated into two one-dimensional (1-D) Gaussian filters. The 1-D Gaussian filter can be implemented approximately by the cascaded Butterworth filters. The approximation accuracy will be improved with the increase of the number of the cascaded filters. A recursive algorithm for Gaussian filtering requires a relatively small number of simple mathematical operations such as addition, subtraction, multiplication, or division, so that it has considerable computational efficiency and it is very useful for three-dimensional (3-D) surface roughness measurements. The zero-phase-filtering technique is used in this algorithm, so there is no phase distortion in the Gaussian filtered mean surface. High-order approximation Gaussian filters are proposed for practical use to assure high accuracy of Gaussian filtering of 3-D surface roughness measurements.
Improved Expectation Maximization Algorithm for Gaussian Mixed Model Using the Kernel Method
Directory of Open Access Journals (Sweden)
Mohd Izhan Mohd Yusoff
2013-01-01
Full Text Available Fraud activities have contributed to heavy losses suffered by telecommunication companies. In this paper, we attempt to use Gaussian mixed model, which is a probabilistic model normally used in speech recognition to identify fraud calls in the telecommunication industry. We look at several issues encountered when calculating the maximum likelihood estimates of the Gaussian mixed model using an Expectation Maximization algorithm. Firstly, we look at a mechanism for the determination of the initial number of Gaussian components and the choice of the initial values of the algorithm using the kernel method. We show via simulation that the technique improves the performance of the algorithm. Secondly, we developed a procedure for determining the order of the Gaussian mixed model using the log-likelihood function and the Akaike information criteria. Finally, for illustration, we apply the improved algorithm to real telecommunication data. The modified method will pave the way to introduce a comprehensive method for detecting fraud calls in future work.
International Nuclear Information System (INIS)
Fields, D.E.; Miller, C.W.
1980-05-01
The most commonly used approach for estimating the atmospheric concentration and deposition of material downwind from its point of release is the Gaussian plume atmospheric dispersion model. Two of the critical parameters in this model are sigma/sub y/ and sigma/sub z/, the horizontal and vertical dispersion parameters, respectively. A number of different sets of values for sigma/sub y/ and sigma/sub z/ have been determined empirically for different release heights and meteorological and terrain conditions. The computer code DWNWND, described in this report, is an interactive implementation of the Gaussian plume model. This code allows the user to specify any one of eight different sets of the empirically determined dispersion paramters. Using the selected dispersion paramters, ground-level normalized exposure estimates are made at any specified downwind distance. Computed values may be corrected for plume depletion due to deposition and for plume settling due to gravitational fall. With this interactive code, the user chooses values for ten parameters which define the source, the dispersion and deposition process, and the sampling point. DWNWND is written in FORTRAN for execution on a PDP-10 computer, requiring less than one second of central processor unit time for each simulation
Covariance-Based Measurement Selection Criterion for Gaussian-Based Algorithms
Directory of Open Access Journals (Sweden)
Fernando A. Auat Cheein
2013-01-01
Full Text Available Process modeling by means of Gaussian-based algorithms often suffers from redundant information which usually increases the estimation computational complexity without significantly improving the estimation performance. In this article, a non-arbitrary measurement selection criterion for Gaussian-based algorithms is proposed. The measurement selection criterion is based on the determination of the most significant measurement from both an estimation convergence perspective and the covariance matrix associated with the measurement. The selection criterion is independent from the nature of the measured variable. This criterion is used in conjunction with three Gaussian-based algorithms: the EIF (Extended Information Filter, the EKF (Extended Kalman Filter and the UKF (Unscented Kalman Filter. Nevertheless, the measurement selection criterion shown herein can also be applied to other Gaussian-based algorithms. Although this work is focused on environment modeling, the results shown herein can be applied to other Gaussian-based algorithm implementations. Mathematical descriptions and implementation results that validate the proposal are also included in this work.
Directory of Open Access Journals (Sweden)
Shin'ya Nakano
2014-05-01
Full Text Available A hybrid algorithm that combines the ensemble transform Kalman filter (ETKF and the importance sampling approach is proposed. Since the ETKF assumes a linear Gaussian observation model, the estimate obtained by the ETKF can be biased in cases with nonlinear or non-Gaussian observations. The particle filter (PF is based on the importance sampling technique, and is applicable to problems with nonlinear or non-Gaussian observations. However, the PF usually requires an unrealistically large sample size in order to achieve a good estimation, and thus it is computationally prohibitive. In the proposed hybrid algorithm, we obtain a proposal distribution similar to the posterior distribution by using the ETKF. A large number of samples are then drawn from the proposal distribution, and these samples are weighted to approximate the posterior distribution according to the importance sampling principle. Since the importance sampling provides an estimate of the probability density function (PDF without assuming linearity or Gaussianity, we can resolve the bias due to the nonlinear or non-Gaussian observations. Finally, in the next forecast step, we reduce the sample size to achieve computational efficiency based on the Gaussian assumption, while we use a relatively large number of samples in the importance sampling in order to consider the non-Gaussian features of the posterior PDF. The use of the ETKF is also beneficial in terms of the computational simplicity of generating a number of random samples from the proposal distribution and in weighting each of the samples. The proposed algorithm is not necessarily effective in case that the ensemble is located distant from the true state. However, monitoring the effective sample size and tuning the factor for covariance inflation could resolve this problem. In this paper, the proposed hybrid algorithm is introduced and its performance is evaluated through experiments with non-Gaussian observations.
International Nuclear Information System (INIS)
Emad, A.A.; El Shazly, S.M.; Kassem, Kh.O.
2010-01-01
A line source model, developed in laboratory of environmental physics, faculty of science at Qena, Egypt is proposed to describe the downwind dispersion of pollutants near roadways, at different cities in Egypt. The model is based on the Gaussian plume methodology and is used to predict air pollutants' concentrations near roadways. In this direction, simple software has been presented in this paper, developed by authors, adopted completely Graphical User Interface (GUI) technique for operating in various windows-based microcomputers. The software interface and code have been designed by Microsoft Visual basic 6.0 based on the Gaussian diffusion equation. This software is developed to predict concentrations of gaseous pollutants (eg. CO, SO 2 , NO 2 and particulates) at a user specified receptor grid
On the relationship between Gaussian stochastic blockmodels and label propagation algorithms
International Nuclear Information System (INIS)
Zhang, Junhao; Hu, Junfeng; Chen, Tongfei
2015-01-01
The problem of community detection has received great attention in recent years. Many methods have been proposed to discover communities in networks. In this paper, we propose a Gaussian stochastic blockmodel that uses Gaussian distributions to fit weight of edges in networks for non-overlapping community detection. The maximum likelihood estimation of this model has the same objective function as general label propagation with node preference. The node preference of a specific vertex turns out to be a value proportional to the intra-community eigenvector centrality (the corresponding entry in principal eigenvector of the adjacency matrix of the subgraph inside that vertex's community) under maximum likelihood estimation. Additionally, the maximum likelihood estimation of a constrained version of our model is highly related to another extension of the label propagation algorithm, namely, the label propagation algorithm under constraint. Experiments show that the proposed Gaussian stochastic blockmodel performs well on various benchmark networks. (paper)
A Fast Elitism Gaussian Estimation of Distribution Algorithm and Application for PID Optimization
Directory of Open Access Journals (Sweden)
Qingyang Xu
2014-01-01
Full Text Available Estimation of distribution algorithm (EDA is an intelligent optimization algorithm based on the probability statistics theory. A fast elitism Gaussian estimation of distribution algorithm (FEGEDA is proposed in this paper. The Gaussian probability model is used to model the solution distribution. The parameters of Gaussian come from the statistical information of the best individuals by fast learning rule. A fast learning rule is used to enhance the efficiency of the algorithm, and an elitism strategy is used to maintain the convergent performance. The performances of the algorithm are examined based upon several benchmarks. In the simulations, a one-dimensional benchmark is used to visualize the optimization process and probability model learning process during the evolution, and several two-dimensional and higher dimensional benchmarks are used to testify the performance of FEGEDA. The experimental results indicate the capability of FEGEDA, especially in the higher dimensional problems, and the FEGEDA exhibits a better performance than some other algorithms and EDAs. Finally, FEGEDA is used in PID controller optimization of PMSM and compared with the classical-PID and GA.
A fast elitism Gaussian estimation of distribution algorithm and application for PID optimization.
Xu, Qingyang; Zhang, Chengjin; Zhang, Li
2014-01-01
Estimation of distribution algorithm (EDA) is an intelligent optimization algorithm based on the probability statistics theory. A fast elitism Gaussian estimation of distribution algorithm (FEGEDA) is proposed in this paper. The Gaussian probability model is used to model the solution distribution. The parameters of Gaussian come from the statistical information of the best individuals by fast learning rule. A fast learning rule is used to enhance the efficiency of the algorithm, and an elitism strategy is used to maintain the convergent performance. The performances of the algorithm are examined based upon several benchmarks. In the simulations, a one-dimensional benchmark is used to visualize the optimization process and probability model learning process during the evolution, and several two-dimensional and higher dimensional benchmarks are used to testify the performance of FEGEDA. The experimental results indicate the capability of FEGEDA, especially in the higher dimensional problems, and the FEGEDA exhibits a better performance than some other algorithms and EDAs. Finally, FEGEDA is used in PID controller optimization of PMSM and compared with the classical-PID and GA.
A Gaussian Process Based Online Change Detection Algorithm for Monitoring Periodic Time Series
Energy Technology Data Exchange (ETDEWEB)
Chandola, Varun [ORNL; Vatsavai, Raju [ORNL
2011-01-01
Online time series change detection is a critical component of many monitoring systems, such as space and air-borne remote sensing instruments, cardiac monitors, and network traffic profilers, which continuously analyze observations recorded by sensors. Data collected by such sensors typically has a periodic (seasonal) component. Most existing time series change detection methods are not directly applicable to handle such data, either because they are not designed to handle periodic time series or because they cannot operate in an online mode. We propose an online change detection algorithm which can handle periodic time series. The algorithm uses a Gaussian process based non-parametric time series prediction model and monitors the difference between the predictions and actual observations within a statistically principled control chart framework to identify changes. A key challenge in using Gaussian process in an online mode is the need to solve a large system of equations involving the associated covariance matrix which grows with every time step. The proposed algorithm exploits the special structure of the covariance matrix and can analyze a time series of length T in O(T^2) time while maintaining a O(T) memory footprint, compared to O(T^4) time and O(T^2) memory requirement of standard matrix manipulation methods. We experimentally demonstrate the superiority of the proposed algorithm over several existing time series change detection algorithms on a set of synthetic and real time series. Finally, we illustrate the effectiveness of the proposed algorithm for identifying land use land cover changes using Normalized Difference Vegetation Index (NDVI) data collected for an agricultural region in Iowa state, USA. Our algorithm is able to detect different types of changes in a NDVI validation data set (with ~80% accuracy) which occur due to crop type changes as well as disruptive changes (e.g., natural disasters).
Gaussian mixture models-based ship target recognition algorithm in remote sensing infrared images
Yao, Shoukui; Qin, Xiaojuan
2018-02-01
Since the resolution of remote sensing infrared images is low, the features of ship targets become unstable. The issue of how to recognize ships with fuzzy features is an open problem. In this paper, we propose a novel ship target recognition algorithm based on Gaussian mixture models (GMMs). In the proposed algorithm, there are mainly two steps. At the first step, the Hu moments of these ship target images are calculated, and the GMMs are trained on the moment features of ships. At the second step, the moment feature of each ship image is assigned to the trained GMMs for recognition. Because of the scale, rotation, translation invariance property of Hu moments and the power feature-space description ability of GMMs, the GMMs-based ship target recognition algorithm can recognize ship reliably. Experimental results of a large simulating image set show that our approach is effective in distinguishing different ship types, and obtains a satisfactory ship recognition performance.
A Digital Image Denoising Algorithm Based on Gaussian Filtering and Bilateral Filtering
Directory of Open Access Journals (Sweden)
Piao Weiying
2018-01-01
Full Text Available Bilateral filtering has been applied in the area of digital image processing widely, but in the high gradient region of the image, bilateral filtering may generate staircase effect. Bilateral filtering can be regarded as one particular form of local mode filtering, according to above analysis, an mixed image de-noising algorithm is proposed based on Gaussian filter and bilateral filtering. First of all, it uses Gaussian filter to filtrate the noise image and get the reference image, then to take both the reference image and noise image as the input for range kernel function of bilateral filter. The reference image can provide the image’s low frequency information, and noise image can provide image’s high frequency information. Through the competitive experiment on both the method in this paper and traditional bilateral filtering, the experimental result showed that the mixed de-noising algorithm can effectively overcome staircase effect, and the filtrated image was more smooth, its textural features was also more close to the original image, and it can achieve higher PSNR value, but the amount of calculation of above two algorithms are basically the same.
Pecha, Petr; Pechova, Emilie
2014-06-01
This article focuses on derivation of an effective algorithm for the fast estimation of cloudshine doses/dose rates induced by a large mixture of radionuclides discharged into the atmosphere. A certain special modification of the classical Gaussian plume approach is proposed for approximation of the near-field dispersion problem. Specifically, the accidental radioactivity release is subdivided into consecutive one-hour Gaussian segments, each driven by a short-term meteorological forecast for the respective hours. Determination of the physical quantity of photon fluence rate from an ambient cloud irradiation is coupled to a special decomposition of the Gaussian plume shape into the equivalent virtual elliptic disks. It facilitates solution of the formerly used time-consuming 3-D integration and provides advantages with regard to acceleration of the computational process on a local scale. An optimal choice of integration limit is adopted on the basis of the mean free path of γ-photons in the air. An efficient approach is introduced for treatment of a wide range of energetic spectrum of the emitted photons when the usual multi-nuclide approach is replaced by a new multi-group scheme. The algorithm is capable of generating the radiological responses in a large net of spatial nodes. It predetermines the proposed procedure such as a proper tool for online data assimilation analysis in the near-field areas. A specific technique for numerical integration is verified on the basis of comparison with a partial analytical solution. Convergence of the finite cloud approximation to the tabulated semi-infinite cloud values for dose conversion factors was validated.
A Gaussian process and derivative spectral-based algorithm for red blood cell segmentation
Xue, Yingying; Wang, Jianbiao; Zhou, Mei; Hou, Xiyue; Li, Qingli; Liu, Hongying; Wang, Yiting
2017-07-01
As an imaging technology used in remote sensing, hyperspectral imaging can provide more information than traditional optical imaging of blood cells. In this paper, an AOTF based microscopic hyperspectral imaging system is used to capture hyperspectral images of blood cells. In order to achieve the segmentation of red blood cells, Gaussian process using squared exponential kernel function is applied first after the data preprocessing to make the preliminary segmentation. The derivative spectrum with spectral angle mapping algorithm is then applied to the original image to segment the boundary of cells, and using the boundary to cut out cells obtained from the Gaussian process to separated adjacent cells. Then the morphological processing method including closing, erosion and dilation is applied so as to keep adjacent cells apart, and by applying median filtering to remove noise points and filling holes inside the cell, the final segmentation result can be obtained. The experimental results show that this method appears better segmentation effect on human red blood cells.
Energy Technology Data Exchange (ETDEWEB)
Liu, Yunlong; Wang, Aiping; Guo, Lei; Wang, Hong
2017-07-09
This paper presents an error-entropy minimization tracking control algorithm for a class of dynamic stochastic system. The system is represented by a set of time-varying discrete nonlinear equations with non-Gaussian stochastic input, where the statistical properties of stochastic input are unknown. By using Parzen windowing with Gaussian kernel to estimate the probability densities of errors, recursive algorithms are then proposed to design the controller such that the tracking error can be minimized. The performance of the error-entropy minimization criterion is compared with the mean-square-error minimization in the simulation results.
Optimal multigrid algorithms for the massive Gaussian model and path integrals
International Nuclear Information System (INIS)
Brandt, A.; Galun, M.
1996-01-01
Multigrid algorithms are presented which, in addition to eliminating the critical slowing down, can also eliminate the open-quotes volume factorclose quotes. The elimination of the volume factor removes the need to produce many independent fine-grid configurations for averaging out their statistical deviations, by averaging over the many samples produced on coarse grids during the multigrid cycle. Thermodynamic limits of observables can be calculated to relative accuracy var-epsilon r in just O(var-epsilon r -2 ) computer operations, where var-epsilon r is the error relative to the standard deviation of the observable. In this paper, we describe in detail the calculation of the susceptibility in the one-dimensional massive Gaussian model, which is also a simple example of path integrals. Numerical experiments show that the susceptibility can be calculated to relative accuracy var-epsilon r in about 8 var-epsilon r -2 random number generations, independent of the mass size
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables and small number of samples as well as its non-linearity. It is difficult to get satisfying results by using conventional linear statistical methods. Recursive feature elimination based on support vector machine (SVM RFE) is an effective algorithm for gene selection and cancer classification, which are integrated into a consistent framework. In this paper, we propose a new method to select parameters of the aforementioned algorithm implemented with Gaussian kernel SVMs as better alternatives to the common practice of selecting the apparently best parameters by using a genetic algorithm to search for a couple of optimal parameter. Fast implementation issues for this method are also discussed for pragmatic reasons. The proposed method was tested on two representative hereditary breast cancer and acute leukaemia datasets. The experimental results indicate that the proposed method performs well in selecting genes and achieves high classification accuracies with these genes.
Barrett, Steven R. H.; Britter, Rex E.
Predicting long-term mean pollutant concentrations in the vicinity of airports, roads and other industrial sources are frequently of concern in regulatory and public health contexts. Many emissions are represented geometrically as ground-level line or area sources. Well developed modelling tools such as AERMOD and ADMS are able to model dispersion from finite (i.e. non-point) sources with considerable accuracy, drawing upon an up-to-date understanding of boundary layer behaviour. Due to mathematical difficulties associated with line and area sources, computationally expensive numerical integration schemes have been developed. For example, some models decompose area sources into a large number of line sources orthogonal to the mean wind direction, for which an analytical (Gaussian) solution exists. Models also employ a time-series approach, which involves computing mean pollutant concentrations for every hour over one or more years of meteorological data. This can give rise to computer runtimes of several days for assessment of a site. While this may be acceptable for assessment of a single industrial complex, airport, etc., this level of computational cost precludes national or international policy assessments at the level of detail available with dispersion modelling. In this paper, we extend previous work [S.R.H. Barrett, R.E. Britter, 2008. Development of algorithms and approximations for rapid operational air quality modelling. Atmospheric Environment 42 (2008) 8105-8111] to line and area sources. We introduce approximations which allow for the development of new analytical solutions for long-term mean dispersion from line and area sources, based on hypergeometric functions. We describe how these solutions can be parameterized from a single point source run from an existing advanced dispersion model, thereby accounting for all processes modelled in the more costly algorithms. The parameterization method combined with the analytical solutions for long-term mean
Directory of Open Access Journals (Sweden)
Wu Chi-Yeh
2010-01-01
Full Text Available Abstract Background MicroRNAs (miRNAs are short non-coding RNA molecules, which play an important role in post-transcriptional regulation of gene expression. There have been many efforts to discover miRNA precursors (pre-miRNAs over the years. Recently, ab initio approaches have attracted more attention because they do not depend on homology information and provide broader applications than comparative approaches. Kernel based classifiers such as support vector machine (SVM are extensively adopted in these ab initio approaches due to the prediction performance they achieved. On the other hand, logic based classifiers such as decision tree, of which the constructed model is interpretable, have attracted less attention. Results This article reports the design of a predictor of pre-miRNAs with a novel kernel based classifier named the generalized Gaussian density estimator (G2DE based classifier. The G2DE is a kernel based algorithm designed to provide interpretability by utilizing a few but representative kernels for constructing the classification model. The performance of the proposed predictor has been evaluated with 692 human pre-miRNAs and has been compared with two kernel based and two logic based classifiers. The experimental results show that the proposed predictor is capable of achieving prediction performance comparable to those delivered by the prevailing kernel based classification algorithms, while providing the user with an overall picture of the distribution of the data set. Conclusion Software predictors that identify pre-miRNAs in genomic sequences have been exploited by biologists to facilitate molecular biology research in recent years. The G2DE employed in this study can deliver prediction accuracy comparable with the state-of-the-art kernel based machine learning algorithms. Furthermore, biologists can obtain valuable insights about the different characteristics of the sequences of pre-miRNAs with the models generated by the G
Algorithms for Rapidly Dispersing Robot Swarms in Unknown Environments
Hsiang, Tien-Ruey; Arkin, Esther M.; Bender, Michael; Fekete, Sandor P.; Mitchell, Joseph S. B.
2002-01-01
We develop and analyze algorithms for dispersing a swarm of primitive robots in an unknown environment, R. The primary objective is to minimize the makespan, that is, the time to fill the entire region. An environment is composed of pixels that form a connected subset of the integer grid. There is at most one robot per pixel and robots move horizontally or vertically at unit speed. Robots enter R by means of k>=1 door pixels Robots are primitive finite automata, only having local communicatio...
International Nuclear Information System (INIS)
Brown, M.J.; Arya, S.P.; Snyder, W.H.
1993-01-01
The vertical diffusion of a passive tracer released from surface and elevated sources in a neutrally stratified boundary layer has been studied by comparing field and laboratory experiments with a non-Gaussian K-theory model that assumes power-law profiles for the mean velocity and vertical eddy diffusivity. Several important differences between model predictions and experimental data were discovered: (1) the model overestimated ground-level concentrations from surface and elevated releases at distances beyond the peak concentration; (2) the model overpredicted vertical mixing near elevated sources, especially in the upward direction; (3) the model-predicted exponent α in the exponential vertical concentration profile for a surface release [bar C(z)∝ exp(-z α )] was smaller than the experimentally measured exponent. Model closure assumptions and experimental short-comings are discussed in relation to their probable effect on model predictions and experimental measurements. 42 refs., 13 figs., 3 tabs
Assessment of Safety Parameters for Radiological Explosion Based on Gaussian Dispersion Model
Energy Technology Data Exchange (ETDEWEB)
Pandey, Alok [Korea Advanced Institute of Science and Technology, Daejeon (Korea, Republic of); Yu, Hyungjoon; Kim, Hong Suk [Korea Institute of Nuclear Safety, Daejeon (Korea, Republic of)
2014-10-15
These sources if used with explosive (called RDD - radiological dispersion device), can cause dispersion of radioactive material resulting in public exposure and contamination of the environment. Radiological explosion devices are not weapons for the mass destruction like atom bombs, but can cause the death of few persons and contamination of large areas. The reduction of the threat of radiological weapon attack by terrorist groups causing dispersion of radioactive material is one of the priority tasks of the IAEA Nuclear Safety and Security Program.Emergency preparedness is an essential part for reducing and mitigating radiological weapon threat. Preliminary assessment of dispersion study followed by radiological explosion and its quantitative effect will be helpful for the emergency preparedness team for an early response. The effect of the radiological dispersion depends on various factors like radioisotope, its activity, physical form, amount of explosive used and meteorological factors at the time of an explosion. This study aim to determine the area affected by the radiological explosion as pre assessment to provide feedback to emergency management teams for handling and mitigation the situation after an explosion. Most practical scenarios of radiological explosion are considered with conservative approach for the assessment of the area under a threat for emergency handling and management purpose. Radioisotopes under weak security controls can be used for a radiological explosion to create terror and socioeconomic threat for the public. Prior assessment of radiological threats is helpful for emergency management teams to take prompt decision about evacuation of the affected area and other emergency handling actions. Comparable activities of Co-60 source used in radiotherapy and Sr-90 source of disused and orphaned RTGs with two different quantities of TNT were used for the scenario development of radiological explosion. In the Basic Safety Standard (BSS
Assessment of Safety Parameters for Radiological Explosion Based on Gaussian Dispersion Model
International Nuclear Information System (INIS)
Pandey, Alok; Yu, Hyungjoon; Kim, Hong Suk
2014-01-01
These sources if used with explosive (called RDD - radiological dispersion device), can cause dispersion of radioactive material resulting in public exposure and contamination of the environment. Radiological explosion devices are not weapons for the mass destruction like atom bombs, but can cause the death of few persons and contamination of large areas. The reduction of the threat of radiological weapon attack by terrorist groups causing dispersion of radioactive material is one of the priority tasks of the IAEA Nuclear Safety and Security Program.Emergency preparedness is an essential part for reducing and mitigating radiological weapon threat. Preliminary assessment of dispersion study followed by radiological explosion and its quantitative effect will be helpful for the emergency preparedness team for an early response. The effect of the radiological dispersion depends on various factors like radioisotope, its activity, physical form, amount of explosive used and meteorological factors at the time of an explosion. This study aim to determine the area affected by the radiological explosion as pre assessment to provide feedback to emergency management teams for handling and mitigation the situation after an explosion. Most practical scenarios of radiological explosion are considered with conservative approach for the assessment of the area under a threat for emergency handling and management purpose. Radioisotopes under weak security controls can be used for a radiological explosion to create terror and socioeconomic threat for the public. Prior assessment of radiological threats is helpful for emergency management teams to take prompt decision about evacuation of the affected area and other emergency handling actions. Comparable activities of Co-60 source used in radiotherapy and Sr-90 source of disused and orphaned RTGs with two different quantities of TNT were used for the scenario development of radiological explosion. In the Basic Safety Standard (BSS
Hamdi, Mazda; Kenari, Masoumeh Nasiri
2013-06-01
We consider a time-hopping based multiple access scheme introduced in [1] for communication over dispersive infrared links, and evaluate its performance for correlator and matched filter receivers. In the investigated time-hopping code division multiple access (TH-CDMA) method, the transmitter benefits a low rate convolutional encoder. In this method, the bit interval is divided into Nc chips and the output of the encoder along with a PN sequence assigned to the user determines the position of the chip in which the optical pulse is transmitted. We evaluate the multiple access performance of the system for correlation receiver considering background noise which is modeled as White Gaussian noise due to its large intensity. For the correlation receiver, the results show that for a fixed processing gain, at high transmit power, where the multiple access interference has the dominant effect, the performance improves by the coding gain. But at low transmit power, in which the increase of coding gain leads to the decrease of the chip time, and consequently, to more corruption due to the channel dispersion, there exists an optimum value for the coding gain. However, for the matched filter, the performance always improves by the coding gain. The results show that the matched filter receiver outperforms the correlation receiver in the considered cases. Our results show that, for the same bandwidth and bit rate, the proposed system excels other multiple access techniques, like conventional CDMA and time hopping scheme.
Li, Tiejun; Min, Bin; Wang, Zhiming
2013-03-14
The stochastic integral ensuring the Newton-Leibnitz chain rule is essential in stochastic energetics. Marcus canonical integral has this property and can be understood as the Wong-Zakai type smoothing limit when the driving process is non-Gaussian. However, this important concept seems not well-known for physicists. In this paper, we discuss Marcus integral for non-Gaussian processes and its computation in the context of stochastic energetics. We give a comprehensive introduction to Marcus integral and compare three equivalent definitions in the literature. We introduce the exact pathwise simulation algorithm and give the error analysis. We show how to compute the thermodynamic quantities based on the pathwise simulation algorithm. We highlight the information hidden in the Marcus mapping, which plays the key role in determining thermodynamic quantities. We further propose the tau-leaping algorithm, which advance the process with deterministic time steps when tau-leaping condition is satisfied. The numerical experiments and its efficiency analysis show that it is very promising.
Directory of Open Access Journals (Sweden)
Chia-Feng Lu
Full Text Available Automatic identification of various perfusion compartments from dynamic susceptibility contrast magnetic resonance brain images can assist in clinical diagnosis and treatment of cerebrovascular diseases. The principle of segmentation methods was based on the clustering of bolus transit-time profiles to discern areas of different tissues. However, the cerebrovascular diseases may result in a delayed and dispersed local perfusion and therefore alter the hemodynamic signal profiles. Assessing the accuracy of the segmentation technique under delayed/dispersed circumstance is critical to accurately evaluate the severity of the vascular disease. In this study, we improved the segmentation method of expectation-maximization algorithm by using the results of hierarchical clustering on whitened perfusion data as initial parameters for a mixture of multivariate Gaussians model. In addition, Monte Carlo simulations were conducted to evaluate the performance of proposed method under different levels of delay, dispersion, and noise of signal profiles in tissue segmentation. The proposed method was used to classify brain tissue types using perfusion data from five normal participants, a patient with unilateral stenosis of the internal carotid artery, and a patient with moyamoya disease. Our results showed that the normal, delayed or dispersed hemodynamics can be well differentiated for patients, and therefore the local arterial input function for impaired tissues can be recognized to minimize the error when estimating the cerebral blood flow. Furthermore, the tissue in the risk of infarct and the tissue with or without the complementary blood supply from the communicating arteries can be identified.
Schellenberg, Graham; Stortz, Greg; Goertzen, Andrew L
2016-02-07
A typical positron emission tomography detector is comprised of a scintillator crystal array coupled to a photodetector array or other position sensitive detector. Such detectors using light sharing to read out crystal elements require the creation of a crystal lookup table (CLUT) that maps the detector response to the crystal of interaction based on the x-y position of the event calculated through Anger-type logic. It is vital for system performance that these CLUTs be accurate so that the location of events can be accurately identified and so that crystal-specific corrections, such as energy windowing or time alignment, can be applied. While using manual segmentation of the flood image to create the CLUT is a simple and reliable approach, it is both tedious and time consuming for systems with large numbers of crystal elements. In this work we describe the development of an automated algorithm for CLUT generation that uses a Gaussian mixture model paired with thin plate splines (TPS) to iteratively fit a crystal layout template that includes the crystal numbering pattern. Starting from a region of stability, Gaussians are individually fit to data corresponding to crystal locations while simultaneously updating a TPS for predicting future Gaussian locations at the edge of a region of interest that grows as individual Gaussians converge to crystal locations. The algorithm was tested with flood image data collected from 16 detector modules, each consisting of a 409 crystal dual-layer offset LYSO crystal array readout by a 32 pixel SiPM array. For these detector flood images, depending on user defined input parameters, the algorithm runtime ranged between 17.5-82.5 s per detector on a single core of an Intel i7 processor. The method maintained an accuracy above 99.8% across all tests, with the majority of errors being localized to error prone corner regions. This method can be easily extended for use with other detector types through adjustment of the initial
International Nuclear Information System (INIS)
Schellenberg, Graham; Goertzen, Andrew L; Stortz, Greg
2016-01-01
A typical positron emission tomography detector is comprised of a scintillator crystal array coupled to a photodetector array or other position sensitive detector. Such detectors using light sharing to read out crystal elements require the creation of a crystal lookup table (CLUT) that maps the detector response to the crystal of interaction based on the x–y position of the event calculated through Anger-type logic. It is vital for system performance that these CLUTs be accurate so that the location of events can be accurately identified and so that crystal-specific corrections, such as energy windowing or time alignment, can be applied. While using manual segmentation of the flood image to create the CLUT is a simple and reliable approach, it is both tedious and time consuming for systems with large numbers of crystal elements. In this work we describe the development of an automated algorithm for CLUT generation that uses a Gaussian mixture model paired with thin plate splines (TPS) to iteratively fit a crystal layout template that includes the crystal numbering pattern. Starting from a region of stability, Gaussians are individually fit to data corresponding to crystal locations while simultaneously updating a TPS for predicting future Gaussian locations at the edge of a region of interest that grows as individual Gaussians converge to crystal locations. The algorithm was tested with flood image data collected from 16 detector modules, each consisting of a 409 crystal dual-layer offset LYSO crystal array readout by a 32 pixel SiPM array. For these detector flood images, depending on user defined input parameters, the algorithm runtime ranged between 17.5–82.5 s per detector on a single core of an Intel i7 processor. The method maintained an accuracy above 99.8% across all tests, with the majority of errors being localized to error prone corner regions. This method can be easily extended for use with other detector types through adjustment of the initial
Schellenberg, Graham; Stortz, Greg; Goertzen, Andrew L.
2016-02-01
A typical positron emission tomography detector is comprised of a scintillator crystal array coupled to a photodetector array or other position sensitive detector. Such detectors using light sharing to read out crystal elements require the creation of a crystal lookup table (CLUT) that maps the detector response to the crystal of interaction based on the x-y position of the event calculated through Anger-type logic. It is vital for system performance that these CLUTs be accurate so that the location of events can be accurately identified and so that crystal-specific corrections, such as energy windowing or time alignment, can be applied. While using manual segmentation of the flood image to create the CLUT is a simple and reliable approach, it is both tedious and time consuming for systems with large numbers of crystal elements. In this work we describe the development of an automated algorithm for CLUT generation that uses a Gaussian mixture model paired with thin plate splines (TPS) to iteratively fit a crystal layout template that includes the crystal numbering pattern. Starting from a region of stability, Gaussians are individually fit to data corresponding to crystal locations while simultaneously updating a TPS for predicting future Gaussian locations at the edge of a region of interest that grows as individual Gaussians converge to crystal locations. The algorithm was tested with flood image data collected from 16 detector modules, each consisting of a 409 crystal dual-layer offset LYSO crystal array readout by a 32 pixel SiPM array. For these detector flood images, depending on user defined input parameters, the algorithm runtime ranged between 17.5-82.5 s per detector on a single core of an Intel i7 processor. The method maintained an accuracy above 99.8% across all tests, with the majority of errors being localized to error prone corner regions. This method can be easily extended for use with other detector types through adjustment of the initial
International Nuclear Information System (INIS)
Anjos, Gullit Diego C. dos
2017-01-01
The Santa Quitéria Project (PSQ) is an enterprise that aims at the production of phosphate compounds as main products, and of uranium concentrates as by-products, from the minerals of the Itataia deposit. The intended area for implementation of the project is located in the municipality of Santa Quitéria, north central region of the State of Ceará. The U.S. Nuclear Regulatory Commission, lists the basic design accidents for a Uranium Processing Plant, such as the PSQ. Among all these scenarios,fire in uranium extraction cells was the one with the highest doses released. For simulation of the fire event, the atmospheric dispersion model used was the standard Gaussian plume model. The doses were calculated for two sets of meteorological conditions: stability class F, with wind velocity of 1 m/s and class D stability with wind velocity of 4.5 m/s. For PSQ, it was considered that there will be no public individual until after 2000 meters from the release point. The doses corresponding to the Occupationally exposed individuals are: 2.0E-3 mSv (class D) and 5 mSv (Class F), 300 meters away from the event. Analyzing the results, it can be concluded that there are no significant radiological consequences for the occupationally exposed individual, both in the stability class D and the F. For PSQ, it was considered that there will be no public individual until after 2000 meters from the release point. The doses, in mSv, corresponding to the individuals located near the project are: Morrinhos (1.27E-03 mSv - Class D and 3.92E-02 - Class F); Burned (1.41E-03 mSv - Class D and 4.28E-02 - Class F); Lagoa do Mato (<7.94E-04 mSv - Class D and <2.68E-02 mv - Class F). (author)
Energy Technology Data Exchange (ETDEWEB)
Anjos, Gullit Diego C. dos, E-mail: gullitcardoso@inb.gov.br [Indústrias Nucleares do Brasil (INB), Rio de Janeiro, RJ (Brazil)
2017-07-01
The Santa Quitéria Project (PSQ) is an enterprise that aims at the production of phosphate compounds as main products, and of uranium concentrates as by-products, from the minerals of the Itataia deposit. The intended area for implementation of the project is located in the municipality of Santa Quitéria, north central region of the State of Ceará. The U.S. Nuclear Regulatory Commission, lists the basic design accidents for a Uranium Processing Plant, such as the PSQ. Among all these scenarios,fire in uranium extraction cells was the one with the highest doses released. For simulation of the fire event, the atmospheric dispersion model used was the standard Gaussian plume model. The doses were calculated for two sets of meteorological conditions: stability class F, with wind velocity of 1 m/s and class D stability with wind velocity of 4.5 m/s. For PSQ, it was considered that there will be no public individual until after 2000 meters from the release point. The doses corresponding to the Occupationally exposed individuals are: 2.0E-3 mSv (class D) and 5 mSv (Class F), 300 meters away from the event. Analyzing the results, it can be concluded that there are no significant radiological consequences for the occupationally exposed individual, both in the stability class D and the F. For PSQ, it was considered that there will be no public individual until after 2000 meters from the release point. The doses, in mSv, corresponding to the individuals located near the project are: Morrinhos (1.27E-03 mSv - Class D and 3.92E-02 - Class F); Burned (1.41E-03 mSv - Class D and 4.28E-02 - Class F); Lagoa do Mato (<7.94E-04 mSv - Class D and <2.68E-02 mv - Class F). (author)
Directory of Open Access Journals (Sweden)
Douglas Scott C
2007-01-01
Full Text Available We derive new fixed-point algorithms for the blind separation of complex-valued mixtures of independent, noncircularly symmetric, and non-Gaussian source signals. Leveraging recently developed results on the separability of complex-valued signal mixtures, we systematically construct iterative procedures on a kurtosis-based contrast whose evolutionary characteristics are identical to those of the FastICA algorithm of Hyvarinen and Oja in the real-valued mixture case. Thus, our methods inherit the fast convergence properties, computational simplicity, and ease of use of the FastICA algorithm while at the same time extending this class of techniques to complex signal mixtures. For extracting multiple sources, symmetric and asymmetric signal deflation procedures can be employed. Simulations for both noiseless and noisy mixtures indicate that the proposed algorithms have superior finite-sample performance in data-starved scenarios as compared to existing complex ICA methods while performing about as well as the best of these techniques for larger data-record lengths.
Genovese, Mariangela; Napoli, Ettore
2013-05-01
The identification of moving objects is a fundamental step in computer vision processing chains. The development of low cost and lightweight smart cameras steadily increases the request of efficient and high performance circuits able to process high definition video in real time. The paper proposes two processor cores aimed to perform the real time background identification on High Definition (HD, 1920 1080 pixel) video streams. The implemented algorithm is the OpenCV version of the Gaussian Mixture Model (GMM), an high performance probabilistic algorithm for the segmentation of the background that is however computationally intensive and impossible to implement on general purpose CPU with the constraint of real time processing. In the proposed paper, the equations of the OpenCV GMM algorithm are optimized in such a way that a lightweight and low power implementation of the algorithm is obtained. The reported performances are also the result of the use of state of the art truncated binary multipliers and ROM compression techniques for the implementation of the non-linear functions. The first circuit has commercial FPGA devices as a target and provides speed and logic resource occupation that overcome previously proposed implementations. The second circuit is oriented to an ASIC (UMC-90nm) standard cell implementation. Both implementations are able to process more than 60 frames per second in 1080p format, a frame rate compatible with HD television.
Caratelli, Diego; Mescia, Luciano; Bia, Pietro; Stukach, Oleg V.
2016-01-01
A novel finite-difference time-domain algorithm for modeling ultrawideband electromagnetic pulse propagation in arbitrary multirelaxed dispersive media is presented. The proposed scheme is based on a general, yet computationally efficient, series representation of the fractional derivative operators
Al-Jabr, Ahmad Ali; Alsunaidi, Mohammad A.; Ng, Tien Khee; Ooi, Boon S.
2013-01-01
In this paper, an finite-difference time-domain (FDTD) algorithm for simulating propagation of EM waves in anisotropic material is presented. The algorithm is based on the auxiliary differential equation and the general polarization formulation. In anisotropic materials, electric fields are coupled and elements in the permittivity tensor are, in general, multiterm dispersive. The presented algorithm resolves the field coupling using a formulation based on electric polarizations. It also offers a simple procedure for the treatment of multiterm dispersion in the FDTD scheme. The algorithm is tested by simulating wave propagation in 1-D magnetized plasma showing excellent agreement with analytical solutions. Extension of the algorithm to multidimensional structures is straightforward. The presented algorithm is efficient and simple compared to other algorithms found in the literature. © 2012 IEEE.
Al-Jabr, Ahmad Ali
2013-03-01
In this paper, an finite-difference time-domain (FDTD) algorithm for simulating propagation of EM waves in anisotropic material is presented. The algorithm is based on the auxiliary differential equation and the general polarization formulation. In anisotropic materials, electric fields are coupled and elements in the permittivity tensor are, in general, multiterm dispersive. The presented algorithm resolves the field coupling using a formulation based on electric polarizations. It also offers a simple procedure for the treatment of multiterm dispersion in the FDTD scheme. The algorithm is tested by simulating wave propagation in 1-D magnetized plasma showing excellent agreement with analytical solutions. Extension of the algorithm to multidimensional structures is straightforward. The presented algorithm is efficient and simple compared to other algorithms found in the literature. © 2012 IEEE.
International Nuclear Information System (INIS)
Phuoc, Le Minh; Lee, Suk Han; Kim, Hun Mo; Martinet, Philippe
2008-01-01
Robot inverse kinematics based on Jacobian inversion encounters critical issues of kinematic singularities. In this paper, several techniques based on damped least squares are proposed to lead robot pass through kinematic singularities without excessive joint velocities. Unlike other work in which the same damping factor is used for all singular vectors, this paper proposes a different damping coefficient for each singular vector based on corresponding singular value of the Jacobian. Moreover, a continuous distribution of damping factor following Gaussian function guarantees the continuous in joint velocities. A genetic algorithm is utilized to search for the best maximum damping factor and singular region, which used to require ad hoc searching in other works. As a result, end effector tracking error, which is inherited from damped least squares by introducing damping factors, is minimized. The effectiveness of our approach is compared with other methods in both non-redundant robot and redundant robot
Energy Technology Data Exchange (ETDEWEB)
Phuoc, Le Minh; Lee, Suk Han; Kim, Hun Mo [Sungkyunkwan University, Suwon (Korea, Republic of); Martinet, Philippe [Blaise Pascal University, Clermont-Ferrand Cedex (France)
2008-07-15
Robot inverse kinematics based on Jacobian inversion encounters critical issues of kinematic singularities. In this paper, several techniques based on damped least squares are proposed to lead robot pass through kinematic singularities without excessive joint velocities. Unlike other work in which the same damping factor is used for all singular vectors, this paper proposes a different damping coefficient for each singular vector based on corresponding singular value of the Jacobian. Moreover, a continuous distribution of damping factor following Gaussian function guarantees the continuous in joint velocities. A genetic algorithm is utilized to search for the best maximum damping factor and singular region, which used to require ad hoc searching in other works. As a result, end effector tracking error, which is inherited from damped least squares by introducing damping factors, is minimized. The effectiveness of our approach is compared with other methods in both non-redundant robot and redundant robot
FPGA Implementation of Gaussian Mixture Model Algorithm for 47 fps Segmentation of 1080p Video
Directory of Open Access Journals (Sweden)
Mariangela Genovese
2013-01-01
Full Text Available Circuits and systems able to process high quality video in real time are fundamental in nowadays imaging systems. The circuit proposed in the paper, aimed at the robust identification of the background in video streams, implements the improved formulation of the Gaussian Mixture Model (GMM algorithm that is included in the OpenCV library. An innovative, hardware oriented, formulation of the GMM equations, the use of truncated binary multipliers, and ROM compression techniques allow reduced hardware complexity and increased processing capability. The proposed circuit has been designed having commercial FPGA devices as target and provides speed and logic resources occupation that overcome previously proposed implementations. The circuit, when implemented on Virtex6 or StratixIV, processes more than 45 frame per second in 1080p format and uses few percent of FPGA logic resources.
A Novel Evolutionary Algorithm Inspired by Beans Dispersal
Directory of Open Access Journals (Sweden)
Xiaoming Zhang
2013-02-01
Full Text Available Inspired by the transmission of beans in nature, a novel evolutionary algorithm-Bean Optimization Algorithm (BOA is proposed in this paper. BOA is mainly based on the normal distribution which is an important continuous probability distribution of quantitative phenomena. Through simulating the self-adaptive phenomena of plant, BOA is designed for solving continuous optimization problems. We also analyze the global convergence of BOA by using the Solis and Wetsarsquo; research results. The conclusion is that BOA can converge to the global optimization solution with probability one. In order to validate its effectiveness, BOA is tested against benchmark functions. And its performance is also compared with that of particle swarm optimization (PSO algorithm. The experimental results show that BOA has competitive performance to PSO in terms of accuracy and convergence speed on the explored tests and stands out as a promising alternative to existing optimization methods for engineering designs or applications.
Mehdinejadiani, Behrouz
2017-08-01
This study represents the first attempt to estimate the solute transport parameters of the spatial fractional advection-dispersion equation using Bees Algorithm. The numerical studies as well as the experimental studies were performed to certify the integrity of Bees Algorithm. The experimental ones were conducted in a sandbox for homogeneous and heterogeneous soils. A detailed comparative study was carried out between the results obtained from Bees Algorithm and those from Genetic Algorithm and LSQNONLIN routines in FracFit toolbox. The results indicated that, in general, the Bees Algorithm much more accurately appraised the sFADE parameters in comparison with Genetic Algorithm and LSQNONLIN, especially in the heterogeneous soil and for α values near to 1 in the numerical study. Also, the results obtained from Bees Algorithm were more reliable than those from Genetic Algorithm. The Bees Algorithm showed the relative similar performances for all cases, while the Genetic Algorithm and the LSQNONLIN yielded different performances for various cases. The performance of LSQNONLIN strongly depends on the initial guess values so that, compared to the Genetic Algorithm, it can more accurately estimate the sFADE parameters by taking into consideration the suitable initial guess values. To sum up, the Bees Algorithm was found to be very simple, robust and accurate approach to estimate the transport parameters of the spatial fractional advection-dispersion equation. Copyright © 2017 Elsevier B.V. All rights reserved.
Mehdinejadiani, Behrouz
2017-08-01
This study represents the first attempt to estimate the solute transport parameters of the spatial fractional advection-dispersion equation using Bees Algorithm. The numerical studies as well as the experimental studies were performed to certify the integrity of Bees Algorithm. The experimental ones were conducted in a sandbox for homogeneous and heterogeneous soils. A detailed comparative study was carried out between the results obtained from Bees Algorithm and those from Genetic Algorithm and LSQNONLIN routines in FracFit toolbox. The results indicated that, in general, the Bees Algorithm much more accurately appraised the sFADE parameters in comparison with Genetic Algorithm and LSQNONLIN, especially in the heterogeneous soil and for α values near to 1 in the numerical study. Also, the results obtained from Bees Algorithm were more reliable than those from Genetic Algorithm. The Bees Algorithm showed the relative similar performances for all cases, while the Genetic Algorithm and the LSQNONLIN yielded different performances for various cases. The performance of LSQNONLIN strongly depends on the initial guess values so that, compared to the Genetic Algorithm, it can more accurately estimate the sFADE parameters by taking into consideration the suitable initial guess values. To sum up, the Bees Algorithm was found to be very simple, robust and accurate approach to estimate the transport parameters of the spatial fractional advection-dispersion equation.
Directory of Open Access Journals (Sweden)
Benjamin M. Cowan
2013-04-01
Full Text Available We describe a modification to the finite-difference time-domain algorithm for electromagnetics on a Cartesian grid which eliminates numerical dispersion error in vacuum for waves propagating along a grid axis. We provide details of the algorithm, which generalizes previous work by allowing 3D operation with a wide choice of aspect ratio, and give conditions to eliminate dispersive errors along one or more of the coordinate axes. We discuss the algorithm in the context of laser-plasma acceleration simulation, showing significant reduction—up to a factor of 280, at a plasma density of 10^{23} m^{-3}—of the dispersion error of a linear laser pulse in a plasma channel. We then compare the new algorithm with the standard electromagnetic update for laser-plasma accelerator stage simulations, demonstrating that by controlling numerical dispersion, the new algorithm allows more accurate simulation than is otherwise obtained. We also show that the algorithm can be used to overcome the critical but difficult challenge of consistent initialization of a relativistic particle beam and its fields in an accelerator simulation.
Asymmetry in some common assignment algorithms: the dispersion factor solution
T de la Barra; B Pérez
1986-01-01
Many common assignment algorithms are based on Dial's original design to determine the paths that trip makers will follow from a given origin to destination centroids. The purpose of this paper is to show that the rules that have to be applied result in two unwanted properties. The first is that trips assigned from an origin centroid i to a destination j can be dramatically different to those resulting from centroid j to centroid i , even if the number of trips is the same and the network is ...
International Nuclear Information System (INIS)
Svensson, Urban
2001-04-01
A particle tracking algorithm, PARTRACK, that simulates transport and dispersion in a sparsely fractured rock is described. The main novel feature of the algorithm is the introduction of multiple particle states. It is demonstrated that the introduction of this feature allows for the simultaneous simulation of Taylor dispersion, sorption and matrix diffusion. A number of test cases are used to verify and demonstrate the features of PARTRACK. It is shown that PARTRACK can simulate the following processes, believed to be important for the problem addressed: the split up of a tracer cloud at a fracture intersection, channeling in a fracture plane, Taylor dispersion and matrix diffusion and sorption. From the results of the test cases, it is concluded that PARTRACK is an adequate framework for simulation of transport and dispersion of a solute in a sparsely fractured rock
A simplified algorithm for measuring erythrocyte deformability dispersion by laser ektacytometry
Energy Technology Data Exchange (ETDEWEB)
Nikitin, S Yu; Yurchuk, Yu S [Department of Physics, M.V. Lomonosov Moscow State University (Russian Federation)
2015-08-31
The possibility of measuring the dispersion of red blood cell deformability by laser diffractometry in shear flow (ektacytometry) is analysed theoretically. A diffraction pattern parameter is found, which is sensitive to the dispersion of erythrocyte deformability and to a lesser extent – to such parameters as the level of the scattered light intensity, the shape of red blood cells, the concentration of red blood cells in the suspension, the geometric dimensions of the experimental setup, etc. A new algorithm is proposed for measuring erythrocyte deformability dispersion by using data of laser ektacytometry. (laser applications in medicine)
Fallah-Shorshani, Masoud; Shekarrizfard, Maryam; Hatzopoulou, Marianne
2017-03-01
The development and use of dispersion models that simulate traffic-related air pollution in urban areas has risen significantly in support of air pollution exposure research. In order to accurately estimate population exposure, it is important to generate concentration surfaces that take into account near-road concentrations as well as the transport of pollutants throughout an urban region. In this paper, an integrated modelling chain was developed to simulate ambient Nitrogen Dioxide (NO2) in a dense urban neighbourhood while taking into account traffic emissions, the regional background, and the transport of pollutants within the urban canopy. For this purpose, we developed a hybrid configuration including 1) a street canyon model, which simulates pollutant transfer along streets and intersections, taking into account the geometry of buildings and other obstacles, and 2) a Gaussian puff model, which resolves the transport of contaminants at the top of the urban canopy and accounts for regional meteorology. Each dispersion model was validated against measured concentrations and compared against the hybrid configuration. Our results demonstrate that the hybrid approach significantly improves the output of each model on its own. An underestimation appears clearly for the Gaussian model and street-canyon model compared to observed data. This is due to ignoring the building effect by the Gaussian model and undermining the contribution of other roads by the canyon model. The hybrid approach reduced the RMSE (of observed vs. predicted concentrations) by 16%-25% compared to each model on its own, and increased FAC2 (fraction of predictions within a factor of two of the observations) by 10%-34%.
CVFEM for Multiphase Flow with Disperse and Interface Tracking, and Algorithms Performances
Directory of Open Access Journals (Sweden)
M. Milanez
2015-12-01
Full Text Available A Control-Volume Finite-Element Method (CVFEM is newly formulated within Eulerian and spatial averaging frameworks for effective simulation of disperse transport, deposit distribution and interface tracking. Their algorithms are implemented alongside an existing continuous phase algorithm. Flow terms are newly implemented for a control volume (CV fixed in a space, and the CVs' equations are assembled based on a finite element method (FEM. Upon impacting stationary and moving boundaries, the disperse phase changes its phase and the solver triggers identification of CVs with excess deposit and their neighboring CVs for its accommodation in front of an interface. The solver then updates boundary conditions on the moving interface as well as domain conditions on the accumulating deposit. Corroboration of the algorithms' performances is conducted on illustrative simulations with novel and existing Eulerian and Lagrangian solutions, such as (- other, i. e. external methods with analytical and physical experimental formulations, and (- characteristics internal to CVFEM.
Directory of Open Access Journals (Sweden)
Rongxiao Wang
2017-09-01
Full Text Available The accurate prediction of air contaminant dispersion is essential to air quality monitoring and the emergency management of contaminant gas leakage incidents in chemical industry parks. Conventional atmospheric dispersion models can seldom give accurate predictions due to inaccurate input parameters. In order to improve the prediction accuracy of dispersion models, two data assimilation methods (i.e., the typical particle filter & the combination of a particle filter and expectation-maximization algorithm are proposed to assimilate the virtual Unmanned Aerial Vehicle (UAV observations with measurement error into the atmospheric dispersion model. Two emission cases with different dimensions of state parameters are considered. To test the performances of the proposed methods, two numerical experiments corresponding to the two emission cases are designed and implemented. The results show that the particle filter can effectively estimate the model parameters and improve the accuracy of model predictions when the dimension of state parameters is relatively low. In contrast, when the dimension of state parameters becomes higher, the method of particle filter combining the expectation-maximization algorithm performs better in terms of the parameter estimation accuracy. Therefore, the proposed data assimilation methods are able to effectively support air quality monitoring and emergency management in chemical industry parks.
Akkaş, Efe; Çubukçu, H. Evren; Artuner, Harun
2014-05-01
C5.0 Decision Tree algorithm. The predictions of the decision tree classifier, namely the matching of the test data with the appropriate mineral group, yield an overall accuracy of >90%. Besides, the algorithm successfully discriminated some mineral (groups) despite their similar elemental composition such as orthopyroxene ((Mg,Fe)2[SiO6]) and olivine ((Mg,Fe)2[SiO4]). Furthermore, the effects of various operating conditions have been insignificant for the classifier. These results demonstrate that decision tree algorithm stands as an accurate, rapid and automated method for mineral classification/identification. Hence, decision tree algorithm would be a promising component of an expert system focused on real-time, automated mineral identification using energy dispersive spectrometers without being affected from the operating conditions. Keywords: mineral identification, energy dispersive spectrometry, decision tree algorithm.
Advanced Dispersed Fringe Sensing Algorithm for Coarse Phasing Segmented Mirror Telescopes
Spechler, Joshua A.; Hoppe, Daniel J.; Sigrist, Norbert; Shi, Fang; Seo, Byoung-Joon; Bikkannavar, Siddarayappa A.
2013-01-01
Segment mirror phasing, a critical step of segment mirror alignment, requires the ability to sense and correct the relative pistons between segments from up to a few hundred microns to a fraction of wavelength in order to bring the mirror system to its full diffraction capability. When sampling the aperture of a telescope, using auto-collimating flats (ACFs) is more economical. The performance of a telescope with a segmented primary mirror strongly depends on how well those primary mirror segments can be phased. One such process to phase primary mirror segments in the axial piston direction is dispersed fringe sensing (DFS). DFS technology can be used to co-phase the ACFs. DFS is essentially a signal fitting and processing operation. It is an elegant method of coarse phasing segmented mirrors. DFS performance accuracy is dependent upon careful calibration of the system as well as other factors such as internal optical alignment, system wavefront errors, and detector quality. Novel improvements to the algorithm have led to substantial enhancements in DFS performance. The Advanced Dispersed Fringe Sensing (ADFS) Algorithm is designed to reduce the sensitivity to calibration errors by determining the optimal fringe extraction line. Applying an angular extraction line dithering procedure and combining this dithering process with an error function while minimizing the phase term of the fitted signal, defines in essence the ADFS algorithm.
International Nuclear Information System (INIS)
Luo, Yusheng; Du, Z W; Yang, Y J; Chen, P; Wang, X H; Cheng, Y; Peng, J; Shen, A G; Hu, J M; Tian, Q; Shang, X L; Liu, Z C; Yao, X Q; Wang, J Z
2013-01-01
Early and differential diagnosis of Alzheimer’s disease (AD) has puzzled many clinicians. In this work, laser Raman spectroscopy (LRS) was developed to diagnose AD from platelet samples from AD transgenic mice and non-transgenic controls of different ages. An adaptive Gaussian process (GP) classification algorithm was used to re-establish the classification models of early AD, advanced AD and the control group with just two features and the capacity for noise reduction. Compared with the previous multilayer perceptron network method, the GP showed much better classification performance with the same feature set. Besides, spectra of platelets isolated from AD and Parkinson’s disease (PD) mice were also discriminated. Spectral data from 4 month AD (n = 39) and 12 month AD (n = 104) platelets, as well as control data (n = 135), were collected. Prospective application of the algorithm to the data set resulted in a sensitivity of 80%, a specificity of about 100% and a Matthews correlation coefficient of 0.81. Samples from PD (n = 120) platelets were also collected for differentiation from 12 month AD. The results suggest that platelet LRS detection analysis with the GP appears to be an easier and more accurate method than current ones for early and differential diagnosis of AD. (paper)
Directory of Open Access Journals (Sweden)
Shizhen Zhao
2018-06-01
Full Text Available Mobile activity recognition is significant to the development of human-centric pervasive applications including elderly care, personalized recommendations, etc. Nevertheless, the distribution of inertial sensor data can be influenced to a great extent by varying users. This means that the performance of an activity recognition classifier trained by one user’s dataset will degenerate when transferred to others. In this study, we focus on building a personalized classifier to detect four categories of human activities: light intensity activity, moderate intensity activity, vigorous intensity activity, and fall. In order to solve the problem caused by different distributions of inertial sensor signals, a user-adaptive algorithm based on K-Means clustering, local outlier factor (LOF, and multivariate Gaussian distribution (MGD is proposed. To automatically cluster and annotate a specific user’s activity data, an improved K-Means algorithm with a novel initialization method is designed. By quantifying the samples’ informative degree in a labeled individual dataset, the most profitable samples can be selected for activity recognition model adaption. Through experiments, we conclude that our proposed models can adapt to new users with good recognition performance.
Wang, Xu; Shi, Fang; Sigrist, Norbert; Seo, Byoung-Joon; Tang, Hong; Bikkannavar, Siddarayappa; Basinger, Scott; Lay, Oliver
2012-01-01
Large aperture telescope commonly features segment mirrors and a coarse phasing step is needed to bring these individual segments into the fine phasing capture range. Dispersed Fringe Sensing (DFS) is a powerful coarse phasing technique and its alteration is currently being used for JWST.An Advanced Dispersed Fringe Sensing (ADFS) algorithm is recently developed to improve the performance and robustness of previous DFS algorithms with better accuracy and unique solution. The first part of the paper introduces the basic ideas and the essential features of the ADFS algorithm and presents the some algorithm sensitivity study results. The second part of the paper describes the full details of algorithm validation process through the advanced wavefront sensing and correction testbed (AWCT): first, the optimization of the DFS hardware of AWCT to ensure the data accuracy and reliability is illustrated. Then, a few carefully designed algorithm validation experiments are implemented, and the corresponding data analysis results are shown. Finally the fiducial calibration using Range-Gate-Metrology technique is carried out and a <10nm or <1% algorithm accuracy is demonstrated.
Chen, Guangye; Luis, Chacon; Bird, Robert; Stark, David; Yin, Lin; Albright, Brian
2017-10-01
Leap-frog based explicit algorithms, either ``energy-conserving'' or ``momentum-conserving'', do not conserve energy discretely. Time-centered fully implicit algorithms can conserve discrete energy exactly, but introduce large dispersion errors in the light-wave modes, regardless of timestep sizes. This can lead to intolerable simulation errors where highly accurate light propagation is needed (e.g. laser-plasma interactions, LPI). In this study, we selectively combine the leap-frog and Crank-Nicolson methods to produce a low-dispersion, exactly energy-and-charge-conserving PIC algorithm. Specifically, we employ the leap-frog method for Maxwell equations, and the Crank-Nicolson method for particle equations. Such an algorithm admits exact global energy conservation, exact local charge conservation, and preserves the dispersion properties of the leap-frog method for the light wave. The algorithm has been implemented in a code named iVPIC, based on the VPIC code developed at LANL. We will present numerical results that demonstrate the properties of the scheme with sample test problems (e.g. Weibel instability run for 107 timesteps, and LPI applications.
Methods and Algorithms for Solving Inverse Problems for Fractional Advection-Dispersion Equations
Aldoghaither, Abeer
2015-11-12
Fractional calculus has been introduced as an e cient tool for modeling physical phenomena, thanks to its memory and hereditary properties. For example, fractional models have been successfully used to describe anomalous di↵usion processes such as contaminant transport in soil, oil flow in porous media, and groundwater flow. These models capture important features of particle transport such as particles with velocity variations and long-rest periods. Mathematical modeling of physical phenomena requires the identification of pa- rameters and variables from available measurements. This is referred to as an inverse problem. In this work, we are interested in studying theoretically and numerically inverse problems for space Fractional Advection-Dispersion Equation (FADE), which is used to model solute transport in porous media. Identifying parameters for such an equa- tion is important to understand how chemical or biological contaminants are trans- ported throughout surface aquifer systems. For instance, an estimate of the di↵eren- tiation order in groundwater contaminant transport model can provide information about soil properties, such as the heterogeneity of the medium. Our main contribution is to propose a novel e cient algorithm based on modulat-ing functions to estimate the coe cients and the di↵erentiation order for space FADE, which can be extended to general fractional Partial Di↵erential Equation (PDE). We also show how the method can be applied to the source inverse problem. This work is divided into two parts: In part I, the proposed method is described and studied through an extensive numerical analysis. The local convergence of the proposed two-stage algorithm is proven for 1D space FADE. The properties of this method are studied along with its limitations. Then, the algorithm is generalized to the 2D FADE. In part II, we analyze direct and inverse source problems for a space FADE. The problem consists of recovering the source term using final
Leroy, C; Maro, D; Hébert, D; Solier, L; Rozet, M; Le Cavelier, S; Connan, O
2010-11-01
Atmospheric releases of krypton-85, from the nuclear fuel reprocessing plant at the AREVA NC facility at La Hague (France), were used to test Gaussian models of dispersion. In 2001-2002, the French Institute for Radiological Protection and Nuclear Safety (IRSN) studied the atmospheric dispersion of 15 releases, using krypton-85 as a tracer for plumes emitted from two 100-m-high stacks. Krypton-85 is a chemically inert radionuclide. Krypton-85 air concentration measurements were performed on the ground in the downwind direction, at distances between 0.36 and 3.3 km from the release, by neutral or slightly unstable atmospheric conditions. The standard deviation for the horizontal dispersion of the plume and the Atmospheric Transfer Coefficient (ATC) were determined from these measurements. The experimental results were compared with calculations using first generation (Doury, Briggs) and second generation (ADMS 4.0) Gaussian models. The ADMS 4.0 model was used in two configurations; one takes account of the effect of the built-up area, and the other the effect of the roughness of the surface on the plume dispersion. Only the Briggs model correctly reproduced the measured values for the width of the plume, whereas the ADMS 4.0 model overestimated it and the Doury model underestimated it. The agreement of the models with measured values of the ATC varied according to distance from the release point. For distances less than 2 km from the release point, the ADMS 4.0 model achieved the best agreement between model and measurement; beyond this distance, the best agreement was achieved by the Briggs and Doury models.
AUTONOMOUS GAUSSIAN DECOMPOSITION
Energy Technology Data Exchange (ETDEWEB)
Lindner, Robert R.; Vera-Ciro, Carlos; Murray, Claire E.; Stanimirović, Snežana; Babler, Brian [Department of Astronomy, University of Wisconsin, 475 North Charter Street, Madison, WI 53706 (United States); Heiles, Carl [Radio Astronomy Lab, UC Berkeley, 601 Campbell Hall, Berkeley, CA 94720 (United States); Hennebelle, Patrick [Laboratoire AIM, Paris-Saclay, CEA/IRFU/SAp-CNRS-Université Paris Diderot, F-91191 Gif-sur Yvette Cedex (France); Goss, W. M. [National Radio Astronomy Observatory, P.O. Box O, 1003 Lopezville, Socorro, NM 87801 (United States); Dickey, John, E-mail: rlindner@astro.wisc.edu [University of Tasmania, School of Maths and Physics, Private Bag 37, Hobart, TAS 7001 (Australia)
2015-04-15
We present a new algorithm, named Autonomous Gaussian Decomposition (AGD), for automatically decomposing spectra into Gaussian components. AGD uses derivative spectroscopy and machine learning to provide optimized guesses for the number of Gaussian components in the data, and also their locations, widths, and amplitudes. We test AGD and find that it produces results comparable to human-derived solutions on 21 cm absorption spectra from the 21 cm SPectral line Observations of Neutral Gas with the EVLA (21-SPONGE) survey. We use AGD with Monte Carlo methods to derive the H i line completeness as a function of peak optical depth and velocity width for the 21-SPONGE data, and also show that the results of AGD are stable against varying observational noise intensity. The autonomy and computational efficiency of the method over traditional manual Gaussian fits allow for truly unbiased comparisons between observations and simulations, and for the ability to scale up and interpret the very large data volumes from the upcoming Square Kilometer Array and pathfinder telescopes.
AUTONOMOUS GAUSSIAN DECOMPOSITION
International Nuclear Information System (INIS)
Lindner, Robert R.; Vera-Ciro, Carlos; Murray, Claire E.; Stanimirović, Snežana; Babler, Brian; Heiles, Carl; Hennebelle, Patrick; Goss, W. M.; Dickey, John
2015-01-01
We present a new algorithm, named Autonomous Gaussian Decomposition (AGD), for automatically decomposing spectra into Gaussian components. AGD uses derivative spectroscopy and machine learning to provide optimized guesses for the number of Gaussian components in the data, and also their locations, widths, and amplitudes. We test AGD and find that it produces results comparable to human-derived solutions on 21 cm absorption spectra from the 21 cm SPectral line Observations of Neutral Gas with the EVLA (21-SPONGE) survey. We use AGD with Monte Carlo methods to derive the H i line completeness as a function of peak optical depth and velocity width for the 21-SPONGE data, and also show that the results of AGD are stable against varying observational noise intensity. The autonomy and computational efficiency of the method over traditional manual Gaussian fits allow for truly unbiased comparisons between observations and simulations, and for the ability to scale up and interpret the very large data volumes from the upcoming Square Kilometer Array and pathfinder telescopes
Directory of Open Access Journals (Sweden)
Y. W. Sun
2013-08-01
Full Text Available In this paper, we present an optimized analysis algorithm for non-dispersive infrared (NDIR to in situ monitor stack emissions. The proposed algorithm simultaneously compensates for nonlinear absorption and cross interference among different gases. We present a mathematical derivation for the measurement error caused by variations in interference coefficients when nonlinear absorption occurs. The proposed algorithm is derived from a classical one and uses interference functions to quantify cross interference. The interference functions vary proportionally with the nonlinear absorption. Thus, interference coefficients among different gases can be modeled by the interference functions whether gases are characterized by linear or nonlinear absorption. In this study, the simultaneous analysis of two components (CO2 and CO serves as an example for the validation of the proposed algorithm. The interference functions in this case can be obtained by least-squares fitting with third-order polynomials. Experiments show that the results of cross interference correction are improved significantly by utilizing the fitted interference functions when nonlinear absorptions occur. The dynamic measurement ranges of CO2 and CO are improved by about a factor of 1.8 and 3.5, respectively. A commercial analyzer with high accuracy was used to validate the CO and CO2 measurements derived from the NDIR analyzer prototype in which the new algorithm was embedded. The comparison of the two analyzers show that the prototype works well both within the linear and nonlinear ranges.
Application of Numerical Dispersion Compensation of the Yee-FDTD Algorithm on Elongated Domains
DEFF Research Database (Denmark)
Franek, Ondrej; Zhang, Shuai; Olesen, Kim
2017-01-01
A postprocessing method to compensate for the numerical dispersion of the Yee-FDTD scheme is presented. The method makes use of frequency domain deconvolution of the erroneous phase shift from the obtained results and can be applied on certain specific conditions, such as for simulations on elong......A postprocessing method to compensate for the numerical dispersion of the Yee-FDTD scheme is presented. The method makes use of frequency domain deconvolution of the erroneous phase shift from the obtained results and can be applied on certain specific conditions, such as for simulations...
Indian Academy of Sciences (India)
polynomial) division have been found in Vedic Mathematics which are dated much before Euclid's algorithm. A programming language Is used to describe an algorithm for execution on a computer. An algorithm expressed using a programming.
Hao, Yufang; Xie, Shaodong
2018-03-01
Air quality monitoring networks play a significant role in identifying the spatiotemporal patterns of air pollution, and they need to be deployed efficiently, with a minimum number of sites. The revision and optimal adjustment of existing monitoring networks is crucial for cities that have undergone rapid urban expansion and experience temporal variations in pollution patterns. The approach based on the Weather Research and Forecasting-California PUFF (WRF-CALPUFF) model and genetic algorithm (GA) was developed to design an optimal monitoring network. The maximization of coverage with minimum overlap and the ability to detect violations of standards were developed as the design objectives for redistributed networks. The non-dominated sorting genetic algorithm was applied to optimize the network size and site locations simultaneously for Shijiazhuang city, one of the most polluted cities in China. The assessment on the current network identified the insufficient spatial coverage of SO2 and NO2 monitoring for the expanding city. The optimization results showed that significant improvements were achieved in multiple objectives by redistributing the original network. Efficient coverage of the resulting designs improved to 60.99% and 76.06% of the urban area for SO2 and NO2, respectively. The redistributing design for multi-pollutant including 8 sites was also proposed, with the spatial representation covered 52.30% of the urban area and the overlapped areas decreased by 85.87% compared with the original network. The abilities to detect violations of standards were not improved as much as the other two objectives due to the conflicting nature between the multiple objectives. Additionally, the results demonstrated that the algorithm was slightly sensitive to the parameter settings, with the number of generations presented the most significant effect. Overall, our study presents an effective and feasible procedure for air quality network optimization at a city scale.
Directory of Open Access Journals (Sweden)
Pratap R. Patnaik
2009-10-01
Full Text Available Poly-β-hydroxybutyrate (PHB is an energy-storage polymer whose properties are similar to those of chemical polymers such as polyethylene and polypropylene. Moreover, PHB is biodegradable, absorbed by human tissues and less energy-consuming than synthetic polymers. Although Ralstonia eutropha is widely used to synthesize PHB, it is inefficient in utilizing glucose and similar sugars. Therefore a co-culture of R. eutropha and Lactobacillus delbrueckii is preferred since the latter can convert glucose to lactate, which R. eutropha can metabolize easily. Tohyama et al. [24] maximized PHB production in a well-mixed fed-batch bioreactor with glucose and (NH42SO4 as the primary substrates. Since production-scale bioreactors often deviate from ideal laboratory-scale reactors, a large bioreactor was simulated by means of a dispersion model with the kinetics determined by Tohyama et al. [24] and dispersion set at an optimum Peclet number of 20 [32]. The time-dependent feed rates of the two substrates were determined through a genetic algorithm (GA to maximize PHB production. This bioreactor produced 22.2% more PHB per liter and 12.8% more cell mass than achieved by Tohyama et al. [24]. These results, and similar observations with other fermentations, indicate the feasibility of enhancing the efficiency of large nonideal bioreactors through GA optimizations.
A Low Delay and Fast Converging Improved Proportionate Algorithm for Sparse System Identification
Directory of Open Access Journals (Sweden)
Benesty Jacob
2007-01-01
Full Text Available A sparse system identification algorithm for network echo cancellation is presented. This new approach exploits both the fast convergence of the improved proportionate normalized least mean square (IPNLMS algorithm and the efficient implementation of the multidelay adaptive filtering (MDF algorithm inheriting the beneficial properties of both. The proposed IPMDF algorithm is evaluated using impulse responses with various degrees of sparseness. Simulation results are also presented for both speech and white Gaussian noise input sequences. It has been shown that the IPMDF algorithm outperforms the MDF and IPNLMS algorithms for both sparse and dispersive echo path impulse responses. Computational complexity of the proposed algorithm is also discussed.
Information geometry of Gaussian channels
International Nuclear Information System (INIS)
Monras, Alex; Illuminati, Fabrizio
2010-01-01
We define a local Riemannian metric tensor in the manifold of Gaussian channels and the distance that it induces. We adopt an information-geometric approach and define a metric derived from the Bures-Fisher metric for quantum states. The resulting metric inherits several desirable properties from the Bures-Fisher metric and is operationally motivated by distinguishability considerations: It serves as an upper bound to the attainable quantum Fisher information for the channel parameters using Gaussian states, under generic constraints on the physically available resources. Our approach naturally includes the use of entangled Gaussian probe states. We prove that the metric enjoys some desirable properties like stability and covariance. As a by-product, we also obtain some general results in Gaussian channel estimation that are the continuous-variable analogs of previously known results in finite dimensions. We prove that optimal probe states are always pure and bounded in the number of ancillary modes, even in the presence of constraints on the reduced state input in the channel. This has experimental and computational implications. It limits the complexity of optimal experimental setups for channel estimation and reduces the computational requirements for the evaluation of the metric: Indeed, we construct a converging algorithm for its computation. We provide explicit formulas for computing the multiparametric quantum Fisher information for dissipative channels probed with arbitrary Gaussian states and provide the optimal observables for the estimation of the channel parameters (e.g., bath couplings, squeezing, and temperature).
Indian Academy of Sciences (India)
to as 'divide-and-conquer'. Although there has been a large effort in realizing efficient algorithms, there are not many universally accepted algorithm design paradigms. In this article, we illustrate algorithm design techniques such as balancing, greedy strategy, dynamic programming strategy, and backtracking or traversal of ...
Energy Technology Data Exchange (ETDEWEB)
Hoejstrup, J [NEG Micon Project Development A/S, Randers (Denmark); Hansen, K S [Denmarks Technical Univ., Dept. of Energy Engineering, Lyngby (Denmark); Pedersen, B J [VESTAS Wind Systems A/S, Lem (Denmark); Nielsen, M [Risoe National Lab., Wind Energy and Atmospheric Physics, Roskilde (Denmark)
1999-03-01
The pdf`s of atmospheric turbulence have somewhat wider tails than a Gaussian, especially regarding accelerations, whereas velocities are close to Gaussian. This behaviour is being investigated using data from a large WEB-database in order to quantify the amount of non-Gaussianity. Models for non-Gaussian turbulence have been developed, by which artificial turbulence can be generated with specified distributions, spectra and cross-correlations. The artificial time series will then be used in load models and the resulting loads in the Gaussian and the non-Gaussian cases will be compared. (au)
Aldoghaither, Abeer
2015-12-01
In this paper, a new method, based on the so-called modulating functions, is proposed to estimate average velocity, dispersion coefficient, and differentiation order in a space-fractional advection-dispersion equation, where the average velocity and the dispersion coefficient are space-varying. First, the average velocity and the dispersion coefficient are estimated by applying the modulating functions method, where the problem is transformed into a linear system of algebraic equations. Then, the modulating functions method combined with a Newton\\'s iteration algorithm is applied to estimate the coefficients and the differentiation order simultaneously. The local convergence of the proposed method is proved. Numerical results are presented with noisy measurements to show the effectiveness and robustness of the proposed method. It is worth mentioning that this method can be extended to general fractional partial differential equations.
Aldoghaither, Abeer; Liu, Da-Yan; Laleg-Kirati, Taous-Meriem
2015-01-01
In this paper, a new method, based on the so-called modulating functions, is proposed to estimate average velocity, dispersion coefficient, and differentiation order in a space-fractional advection-dispersion equation, where the average velocity and the dispersion coefficient are space-varying. First, the average velocity and the dispersion coefficient are estimated by applying the modulating functions method, where the problem is transformed into a linear system of algebraic equations. Then, the modulating functions method combined with a Newton's iteration algorithm is applied to estimate the coefficients and the differentiation order simultaneously. The local convergence of the proposed method is proved. Numerical results are presented with noisy measurements to show the effectiveness and robustness of the proposed method. It is worth mentioning that this method can be extended to general fractional partial differential equations.
Mathur, Neha; Glesk, Ivan; Buis, Arjan
2016-10-01
Monitoring of the interface temperature at skin level in lower-limb prosthesis is notoriously complicated. This is due to the flexible nature of the interface liners used impeding the required consistent positioning of the temperature sensors during donning and doffing. Predicting the in-socket residual limb temperature by monitoring the temperature between socket and liner rather than skin and liner could be an important step in alleviating complaints on increased temperature and perspiration in prosthetic sockets. In this work, we propose to implement an adaptive neuro fuzzy inference strategy (ANFIS) to predict the in-socket residual limb temperature. ANFIS belongs to the family of fused neuro fuzzy system in which the fuzzy system is incorporated in a framework which is adaptive in nature. The proposed method is compared to our earlier work using Gaussian processes for machine learning. By comparing the predicted and actual data, results indicate that both the modeling techniques have comparable performance metrics and can be efficiently used for non-invasive temperature monitoring. Copyright © 2016 The Author(s). Published by Elsevier Ltd.. All rights reserved.
Gaussian statistics for palaeomagnetic vectors
Love, J.J.; Constable, C.G.
2003-01-01
With the aim of treating the statistics of palaeomagnetic directions and intensities jointly and consistently, we represent the mean and the variance of palaeomagnetic vectors, at a particular site and of a particular polarity, by a probability density function in a Cartesian three-space of orthogonal magnetic-field components consisting of a single (unimoda) non-zero mean, spherically-symmetrical (isotropic) Gaussian function. For palaeomagnetic data of mixed polarities, we consider a bimodal distribution consisting of a pair of such symmetrical Gaussian functions, with equal, but opposite, means and equal variances. For both the Gaussian and bi-Gaussian distributions, and in the spherical three-space of intensity, inclination, and declination, we obtain analytical expressions for the marginal density functions, the cumulative distributions, and the expected values and variances for each spherical coordinate (including the angle with respect to the axis of symmetry of the distributions). The mathematical expressions for the intensity and off-axis angle are closed-form and especially manageable, with the intensity distribution being Rayleigh-Rician. In the limit of small relative vectorial dispersion, the Gaussian (bi-Gaussian) directional distribution approaches a Fisher (Bingham) distribution and the intensity distribution approaches a normal distribution. In the opposite limit of large relative vectorial dispersion, the directional distributions approach a spherically-uniform distribution and the intensity distribution approaches a Maxwell distribution. We quantify biases in estimating the properties of the vector field resulting from the use of simple arithmetic averages, such as estimates of the intensity or the inclination of the mean vector, or the variances of these quantities. With the statistical framework developed here and using the maximum-likelihood method, which gives unbiased estimates in the limit of large data numbers, we demonstrate how to
Gaussian statistics for palaeomagnetic vectors
Love, J. J.; Constable, C. G.
2003-03-01
With the aim of treating the statistics of palaeomagnetic directions and intensities jointly and consistently, we represent the mean and the variance of palaeomagnetic vectors, at a particular site and of a particular polarity, by a probability density function in a Cartesian three-space of orthogonal magnetic-field components consisting of a single (unimodal) non-zero mean, spherically-symmetrical (isotropic) Gaussian function. For palaeomagnetic data of mixed polarities, we consider a bimodal distribution consisting of a pair of such symmetrical Gaussian functions, with equal, but opposite, means and equal variances. For both the Gaussian and bi-Gaussian distributions, and in the spherical three-space of intensity, inclination, and declination, we obtain analytical expressions for the marginal density functions, the cumulative distributions, and the expected values and variances for each spherical coordinate (including the angle with respect to the axis of symmetry of the distributions). The mathematical expressions for the intensity and off-axis angle are closed-form and especially manageable, with the intensity distribution being Rayleigh-Rician. In the limit of small relative vectorial dispersion, the Gaussian (bi-Gaussian) directional distribution approaches a Fisher (Bingham) distribution and the intensity distribution approaches a normal distribution. In the opposite limit of large relative vectorial dispersion, the directional distributions approach a spherically-uniform distribution and the intensity distribution approaches a Maxwell distribution. We quantify biases in estimating the properties of the vector field resulting from the use of simple arithmetic averages, such as estimates of the intensity or the inclination of the mean vector, or the variances of these quantities. With the statistical framework developed here and using the maximum-likelihood method, which gives unbiased estimates in the limit of large data numbers, we demonstrate how to
Indian Academy of Sciences (India)
ticians but also forms the foundation of computer science. Two ... with methods of developing algorithms for solving a variety of problems but ... applications of computers in science and engineer- ... numerical calculus are as important. We will ...
Approximation problems with the divergence criterion for Gaussian variablesand Gaussian processes
A.A. Stoorvogel; J.H. van Schuppen (Jan)
1996-01-01
textabstractSystem identification for stationary Gaussian processes includes an approximation problem. Currently the subspace algorithm for this problem enjoys much attention. This algorithm is based on a transformation of a finite time series to canonical variable form followed by a truncation.
Indian Academy of Sciences (India)
algorithm design technique called 'divide-and-conquer'. One of ... Turtle graphics, September. 1996. 5. ... whole list named 'PO' is a pointer to the first element of the list; ..... Program for computing matrices X and Y and placing the result in C *).
Indian Academy of Sciences (India)
algorithm that it is implicitly understood that we know how to generate the next natural ..... Explicit comparisons are made in line (1) where maximum and minimum is ... It can be shown that the function T(n) = 3/2n -2 is the solution to the above ...
Variational Gaussian approximation for Poisson data
Arridge, Simon R.; Ito, Kazufumi; Jin, Bangti; Zhang, Chen
2018-02-01
The Poisson model is frequently employed to describe count data, but in a Bayesian context it leads to an analytically intractable posterior probability distribution. In this work, we analyze a variational Gaussian approximation to the posterior distribution arising from the Poisson model with a Gaussian prior. This is achieved by seeking an optimal Gaussian distribution minimizing the Kullback-Leibler divergence from the posterior distribution to the approximation, or equivalently maximizing the lower bound for the model evidence. We derive an explicit expression for the lower bound, and show the existence and uniqueness of the optimal Gaussian approximation. The lower bound functional can be viewed as a variant of classical Tikhonov regularization that penalizes also the covariance. Then we develop an efficient alternating direction maximization algorithm for solving the optimization problem, and analyze its convergence. We discuss strategies for reducing the computational complexity via low rank structure of the forward operator and the sparsity of the covariance. Further, as an application of the lower bound, we discuss hierarchical Bayesian modeling for selecting the hyperparameter in the prior distribution, and propose a monotonically convergent algorithm for determining the hyperparameter. We present extensive numerical experiments to illustrate the Gaussian approximation and the algorithms.
Gaussian entanglement revisited
Lami, Ludovico; Serafini, Alessio; Adesso, Gerardo
2018-02-01
We present a novel approach to the separability problem for Gaussian quantum states of bosonic continuous variable systems. We derive a simplified necessary and sufficient separability criterion for arbitrary Gaussian states of m versus n modes, which relies on convex optimisation over marginal covariance matrices on one subsystem only. We further revisit the currently known results stating the equivalence between separability and positive partial transposition (PPT) for specific classes of Gaussian states. Using techniques based on matrix analysis, such as Schur complements and matrix means, we then provide a unified treatment and compact proofs of all these results. In particular, we recover the PPT-separability equivalence for: (i) Gaussian states of 1 versus n modes; and (ii) isotropic Gaussian states. In passing, we also retrieve (iii) the recently established equivalence between separability of a Gaussian state and and its complete Gaussian extendability. Our techniques are then applied to progress beyond the state of the art. We prove that: (iv) Gaussian states that are invariant under partial transposition are necessarily separable; (v) the PPT criterion is necessary and sufficient for separability for Gaussian states of m versus n modes that are symmetric under the exchange of any two modes belonging to one of the parties; and (vi) Gaussian states which remain PPT under passive optical operations can not be entangled by them either. This is not a foregone conclusion per se (since Gaussian bound entangled states do exist) and settles a question that had been left unanswered in the existing literature on the subject. This paper, enjoyable by both the quantum optics and the matrix analysis communities, overall delivers technical and conceptual advances which are likely to be useful for further applications in continuous variable quantum information theory, beyond the separability problem.
Indian Academy of Sciences (India)
will become clear in the next article when we discuss a simple logo like programming language. ... Rod B may be used as an auxiliary store. The problem is to find an algorithm which performs this task. ... No disks are moved from A to Busing C as auxiliary rod. • move _disk (A, C);. (No + l)th disk is moved from A to C directly ...
International Nuclear Information System (INIS)
Liang, Wen-Quan; Wang, Yan-Fei; Yang, Chang-Chun
2015-01-01
Numerical simulation of the wave equation is widely used to synthesize seismograms theoretically and is also the basis of the reverse time migration and full waveform inversion. For the finite difference methods, grid dispersion often exists because of the discretization of the time and the spatial derivatives in the wave equation. How to suppress the grid dispersion is therefore a key problem for finite difference (FD) approaches. The FD operators for the space derivatives are usually obtained in the space domain. However, the wave equations are discretized in the time and space directions simultaneously. So it would be better to design the FD operators in the time–space domain. We improved the time–space domain method for obtaining the FD operators in an acoustic vertically transversely isotropic (VTI) media so as to cover a much wider range of frequencies. Dispersion analysis and seismic numerical simulation demonstrate the effectiveness of the proposed method. (paper)
CSIR Research Space (South Africa)
Roux, FS
2009-01-01
Full Text Available , t0)} = P(du, dv) {FR{g(u, v, t0)}} Replacement: u→ du = t− t0 i2 ∂ ∂u′ v → dv = t− t0 i2 ∂ ∂v′ CSIR National Laser Centre – p.13/30 Differentiation i.s.o integration Evaluate the integral over the Gaussian beam (once and for all). Then, instead... . Gaussian beams with vortex dipoles CSIR National Laser Centre – p.2/30 Gaussian beam notation Gaussian beam in normalised coordinates: g(u, v, t) = exp ( −u 2 + v2 1− it ) u = xω0 v = yω0 t = zρ ρ = piω20 λ ω0 — 1/e2 beam waist radius; ρ— Rayleigh range ω ω...
Gaussian operations and privacy
International Nuclear Information System (INIS)
Navascues, Miguel; Acin, Antonio
2005-01-01
We consider the possibilities offered by Gaussian states and operations for two honest parties, Alice and Bob, to obtain privacy against a third eavesdropping party, Eve. We first extend the security analysis of the protocol proposed in [Navascues et al. Phys. Rev. Lett. 94, 010502 (2005)]. Then, we prove that a generalized version of this protocol does not allow one to distill a secret key out of bound entangled Gaussian states
Functional Dual Adaptive Control with Recursive Gaussian Process Model
International Nuclear Information System (INIS)
Prüher, Jakub; Král, Ladislav
2015-01-01
The paper deals with dual adaptive control problem, where the functional uncertainties in the system description are modelled by a non-parametric Gaussian process regression model. Current approaches to adaptive control based on Gaussian process models are severely limited in their practical applicability, because the model is re-adjusted using all the currently available data, which keeps growing with every time step. We propose the use of recursive Gaussian process regression algorithm for significant reduction in computational requirements, thus bringing the Gaussian process-based adaptive controllers closer to their practical applicability. In this work, we design a bi-criterial dual controller based on recursive Gaussian process model for discrete-time stochastic dynamic systems given in an affine-in-control form. Using Monte Carlo simulations, we show that the proposed controller achieves comparable performance with the full Gaussian process-based controller in terms of control quality while keeping the computational demands bounded. (paper)
Hougardy, Stefan
2016-01-01
Algorithms play an increasingly important role in nearly all fields of mathematics. This book allows readers to develop basic mathematical abilities, in particular those concerning the design and analysis of algorithms as well as their implementation. It presents not only fundamental algorithms like the sieve of Eratosthenes, the Euclidean algorithm, sorting algorithms, algorithms on graphs, and Gaussian elimination, but also discusses elementary data structures, basic graph theory, and numerical questions. In addition, it provides an introduction to programming and demonstrates in detail how to implement algorithms in C++. This textbook is suitable for students who are new to the subject and covers a basic mathematical lecture course, complementing traditional courses on analysis and linear algebra. Both authors have given this "Algorithmic Mathematics" course at the University of Bonn several times in recent years.
Gaussian capacity of the quantum bosonic memory channel with additive correlated Gaussian noise
International Nuclear Information System (INIS)
Schaefer, Joachim; Karpov, Evgueni; Cerf, Nicolas J.
2011-01-01
We present an algorithm for calculation of the Gaussian classical capacity of a quantum bosonic memory channel with additive Gaussian noise. The algorithm, restricted to Gaussian input states, is applicable to all channels with noise correlations obeying certain conditions and works in the full input energy domain, beyond previous treatments of this problem. As an illustration, we study the optimal input states and capacity of a quantum memory channel with Gauss-Markov noise [J. Schaefer, Phys. Rev. A 80, 062313 (2009)]. We evaluate the enhancement of the transmission rate when using these optimal entangled input states by comparison with a product coherent-state encoding and find out that such a simple coherent-state encoding achieves not less than 90% of the capacity.
Nonclassicality by Local Gaussian Unitary Operations for Gaussian States
Directory of Open Access Journals (Sweden)
Yangyang Wang
2018-04-01
Full Text Available A measure of nonclassicality N in terms of local Gaussian unitary operations for bipartite Gaussian states is introduced. N is a faithful quantum correlation measure for Gaussian states as product states have no such correlation and every non product Gaussian state contains it. For any bipartite Gaussian state ρ A B , we always have 0 ≤ N ( ρ A B < 1 , where the upper bound 1 is sharp. An explicit formula of N for ( 1 + 1 -mode Gaussian states and an estimate of N for ( n + m -mode Gaussian states are presented. A criterion of entanglement is established in terms of this correlation. The quantum correlation N is also compared with entanglement, Gaussian discord and Gaussian geometric discord.
Generalized Gaussian Error Calculus
Grabe, Michael
2010-01-01
For the first time in 200 years Generalized Gaussian Error Calculus addresses a rigorous, complete and self-consistent revision of the Gaussian error calculus. Since experimentalists realized that measurements in general are burdened by unknown systematic errors, the classical, widespread used evaluation procedures scrutinizing the consequences of random errors alone turned out to be obsolete. As a matter of course, the error calculus to-be, treating random and unknown systematic errors side by side, should ensure the consistency and traceability of physical units, physical constants and physical quantities at large. The generalized Gaussian error calculus considers unknown systematic errors to spawn biased estimators. Beyond, random errors are asked to conform to the idea of what the author calls well-defined measuring conditions. The approach features the properties of a building kit: any overall uncertainty turns out to be the sum of a contribution due to random errors, to be taken from a confidence inter...
Learning conditional Gaussian networks
DEFF Research Database (Denmark)
Bøttcher, Susanne Gammelgaard
This paper considers conditional Gaussian networks. The parameters in the network are learned by using conjugate Bayesian analysis. As conjugate local priors, we apply the Dirichlet distribution for discrete variables and the Gaussian-inverse gamma distribution for continuous variables, given...... a configuration of the discrete parents. We assume parameter independence and complete data. Further, to learn the structure of the network, the network score is deduced. We then develop a local master prior procedure, for deriving parameter priors in these networks. This procedure satisfies parameter...... independence, parameter modularity and likelihood equivalence. Bayes factors to be used in model search are introduced. Finally the methods derived are illustrated by a simple example....
Bounded Gaussian process regression
DEFF Research Database (Denmark)
Jensen, Bjørn Sand; Nielsen, Jens Brehm; Larsen, Jan
2013-01-01
We extend the Gaussian process (GP) framework for bounded regression by introducing two bounded likelihood functions that model the noise on the dependent variable explicitly. This is fundamentally different from the implicit noise assumption in the previously suggested warped GP framework. We...... with the proposed explicit noise-model extension....
Quantum information with Gaussian states
International Nuclear Information System (INIS)
Wang Xiangbin; Hiroshima, Tohya; Tomita, Akihisa; Hayashi, Masahito
2007-01-01
Quantum optical Gaussian states are a type of important robust quantum states which are manipulatable by the existing technologies. So far, most of the important quantum information experiments are done with such states, including bright Gaussian light and weak Gaussian light. Extending the existing results of quantum information with discrete quantum states to the case of continuous variable quantum states is an interesting theoretical job. The quantum Gaussian states play a central role in such a case. We review the properties and applications of Gaussian states in quantum information with emphasis on the fundamental concepts, the calculation techniques and the effects of imperfections of the real-life experimental setups. Topics here include the elementary properties of Gaussian states and relevant quantum information device, entanglement-based quantum tasks such as quantum teleportation, quantum cryptography with weak and strong Gaussian states and the quantum channel capacity, mathematical theory of quantum entanglement and state estimation for Gaussian states
Making tensor factorizations robust to non-gaussian noise.
Energy Technology Data Exchange (ETDEWEB)
Chi, Eric C. (Rice University, Houston, TX); Kolda, Tamara Gibson
2011-03-01
Tensors are multi-way arrays, and the CANDECOMP/PARAFAC (CP) tensor factorization has found application in many different domains. The CP model is typically fit using a least squares objective function, which is a maximum likelihood estimate under the assumption of independent and identically distributed (i.i.d.) Gaussian noise. We demonstrate that this loss function can be highly sensitive to non-Gaussian noise. Therefore, we propose a loss function based on the 1-norm because it can accommodate both Gaussian and grossly non-Gaussian perturbations. We also present an alternating majorization-minimization (MM) algorithm for fitting a CP model using our proposed loss function (CPAL1) and compare its performance to the workhorse algorithm for fitting CP models, CP alternating least squares (CPALS).
Gaussian discriminating strength
Rigovacca, L.; Farace, A.; De Pasquale, A.; Giovannetti, V.
2015-10-01
We present a quantifier of nonclassical correlations for bipartite, multimode Gaussian states. It is derived from the Discriminating Strength measure, introduced for finite dimensional systems in Farace et al., [New J. Phys. 16, 073010 (2014), 10.1088/1367-2630/16/7/073010]. As the latter the new measure exploits the quantum Chernoff bound to gauge the susceptibility of the composite system with respect to local perturbations induced by unitary gates extracted from a suitable set of allowed transformations (the latter being identified by posing some general requirements). Closed expressions are provided for the case of two-mode Gaussian states obtained by squeezing or by linearly mixing via a beam splitter a factorized two-mode thermal state. For these density matrices, we study how nonclassical correlations are related with the entanglement present in the system and with its total photon number.
Interconversion of pure Gaussian states requiring non-Gaussian operations
Jabbour, Michael G.; García-Patrón, Raúl; Cerf, Nicolas J.
2015-01-01
We analyze the conditions under which local operations and classical communication enable entanglement transformations between bipartite pure Gaussian states. A set of necessary and sufficient conditions had been found [G. Giedke et al., Quant. Inf. Comput. 3, 211 (2003)] for the interconversion between such states that is restricted to Gaussian local operations and classical communication. Here, we exploit majorization theory in order to derive more general (sufficient) conditions for the interconversion between bipartite pure Gaussian states that goes beyond Gaussian local operations. While our technique is applicable to an arbitrary number of modes for each party, it allows us to exhibit surprisingly simple examples of 2 ×2 Gaussian states that necessarily require non-Gaussian local operations to be transformed into each other.
Yurinsky, Vadim Vladimirovich
1995-01-01
Surveys the methods currently applied to study sums of infinite-dimensional independent random vectors in situations where their distributions resemble Gaussian laws. Covers probabilities of large deviations, Chebyshev-type inequalities for seminorms of sums, a method of constructing Edgeworth-type expansions, estimates of characteristic functions for random vectors obtained by smooth mappings of infinite-dimensional sums to Euclidean spaces. A self-contained exposition of the modern research apparatus around CLT, the book is accessible to new graduate students, and can be a useful reference for researchers and teachers of the subject.
Extended Linear Models with Gaussian Priors
DEFF Research Database (Denmark)
Quinonero, Joaquin
2002-01-01
In extended linear models the input space is projected onto a feature space by means of an arbitrary non-linear transformation. A linear model is then applied to the feature space to construct the model output. The dimension of the feature space can be very large, or even infinite, giving the model...... a very big flexibility. Support Vector Machines (SVM's) and Gaussian processes are two examples of such models. In this technical report I present a model in which the dimension of the feature space remains finite, and where a Bayesian approach is used to train the model with Gaussian priors...... on the parameters. The Relevance Vector Machine, introduced by Tipping, is a particular case of such a model. I give the detailed derivations of the expectation-maximisation (EM) algorithm used in the training. These derivations are not found in the literature, and might be helpful for newcomers....
Dil, Ebrahim Alipanahpour; Ghaedi, Mehrorang; Asfaram, Arash; Mehrabi, Fatemeh; Bazrafshan, Ali Akbar; Ghaedi, Abdol Mohammad
2016-11-01
In this study, ultrasound assisted dispersive solid-phase micro extraction combined with spectrophotometry (USA-DSPME-UV) method based on activated carbon modified with Fe2O3 nanoparticles (Fe2O3-NPs-AC) was developed for pre-concentration and determination of safranin O (SO). It is known that the efficiency of USA-DSPME-UV method may be affected by pH, amount of adsorbent, ultrasound time and eluent volume and the extent and magnitude of their contribution on response (in term of main and interaction part) was studied by using central composite design (CCD) and artificial neural network-genetic algorithms (ANN-GA). Accordingly by adjustment of experimental conditions suggested by ANN-GA at pH 6.5, 1.1mg of adsorbent, 10min ultrasound and 150μL of eluent volume led to achievement of best operation performance like low LOD (6.3ngmL(-1)) and LOQ (17.5ngmL(-1)) in the range of 25-3500ngmL(-1). In following stage, the SO content in real water and wastewater samples with recoveries between 93.27-99.41% with RSD lower than 3% was successfully determined. Copyright © 2016 Elsevier B.V. All rights reserved.
Rotating quantum Gaussian packets
International Nuclear Information System (INIS)
Dodonov, V V
2015-01-01
We study two-dimensional quantum Gaussian packets with a fixed value of mean angular momentum. This value is the sum of two independent parts: the ‘external’ momentum related to the motion of the packet center and the ‘internal’ momentum due to quantum fluctuations. The packets minimizing the mean energy of an isotropic oscillator with the fixed mean angular momentum are found. They exist for ‘co-rotating’ external and internal motions, and they have nonzero correlation coefficients between coordinates and momenta, together with some (moderate) amount of quadrature squeezing. Variances of angular momentum and energy are calculated, too. Differences in the behavior of ‘co-rotating’ and ‘anti-rotating’ packets are shown. The time evolution of rotating Gaussian packets is analyzed, including the cases of a charge in a homogeneous magnetic field and a free particle. In the latter case, the effect of initial shrinking of packets with big enough coordinate-momentum correlation coefficients (followed by the well known expansion) is discovered. This happens due to a competition of ‘focusing’ and ‘de-focusing’ in the orthogonal directions. (paper)
Evolution of super-Gaussian pulses in a nonlinear optical fiber
Bugay, Aleksandr N.; Khalyapin, Vyacheslav A.
2018-04-01
An analytic and numerical study is carried out of the dynamics of parameters of a super-Gaussian pulse whose spectrum can fit both in the region of normal and anomalous dispersion of the group velocity. An analytical solution is found for the parameter characterizing the evolution of the degree of the super-Gaussian momentum. The loss of profile rectangularity is shown to be much faster than the pulse dispersion broadening, and corresponding characteristic length is determined by explicit formula.
Gaussian elimination is not optimal, revisited
DEFF Research Database (Denmark)
Macedo, Hugo Daniel
2016-01-01
We refactor the universal law for the tensor product to express matrix multiplication as the product . MN of two matrices . M and . N thus making possible to use such matrix product to encode and transform algorithms performing matrix multiplication using techniques from linear algebra. We explore...... the end results are equations involving matrix products, our exposition builds upon previous works on the category of matrices (and the related category of finite vector spaces) which we extend by showing: why the direct sum . (⊕,0) monoid is not closed, a biproduct encoding of Gaussian elimination...... such possibility and show two stepwise refinements transforming the composition . MN into the Naïve and Strassen's matrix multiplication algorithms. The inspection of the stepwise transformation of the composition of matrices . MN into the Naïve matrix multiplication algorithm evidences that the steps...
Grazziotin, Pablo Colossi
2016-01-01
This research presents the development of CityZoom UP, the first attempt to extend existing urban planning software in order to assist in modelling urban scenarios and setting up simulation parameters for Gaussian dispersion and CFD models. Based on the previous capabilities and graphic user interfaces of CityZoom to model and validate urban scenarios based on Master Plan regulations, new graphic user interfaces, automatic mesh generation and data conversion algorithms have been created to se...
International Nuclear Information System (INIS)
Pryce, M.H.L.
1985-01-01
A dominant mechanism contributing to hydrodynamic dispersion in fluid flow through rocks is variation of travel speeds within the channels carrying the fluid, whether these be interstices between grains, in granular rocks, or cracks in fractured crystalline rocks. The complex interconnections of the channels ensure a mixing of those parts of the fluid which travel more slowly and those which travel faster. On a macroscopic scale this can be treated statistically in terms of the distribution of times taken by a particle of fluid to move from one surface of constant hydraulic potential to another, lower, potential. The distributions in the individual channels are such that very long travel times make a very important contribution. Indeed, while the mean travel time is related to distance by a well-defined transport speed, the mean square is effectively infinite. This results in an asymmetrical plume which differs markedly from a gaussian shape. The distribution of microscopic travel times is related to the distribution of apertures in the interstices, or in the microcracks, which in turn are affected in a complex way by the stresses acting on the rock matrix
International Nuclear Information System (INIS)
McFadden, Paul; Skenderis, Kostas
2011-01-01
We investigate the non-Gaussianity of primordial cosmological perturbations within our recently proposed holographic description of inflationary universes. We derive a holographic formula that determines the bispectrum of cosmological curvature perturbations in terms of correlation functions of a holographically dual three-dimensional non-gravitational quantum field theory (QFT). This allows us to compute the primordial bispectrum for a universe which started in a non-geometric holographic phase, using perturbative QFT calculations. Strikingly, for a class of models specified by a three-dimensional super-renormalisable QFT, the primordial bispectrum is of exactly the factorisable equilateral form with f NL equil. = 5/36, irrespective of the details of the dual QFT. A by-product of this investigation is a holographic formula for the three-point function of the trace of the stress-energy tensor along general holographic RG flows, which should have applications outside the remit of this work
Turbo Equalization Using Partial Gaussian Approximation
DEFF Research Database (Denmark)
Zhang, Chuanzong; Wang, Zhongyong; Manchón, Carles Navarro
2016-01-01
This letter deals with turbo equalization for coded data transmission over intersymbol interference (ISI) channels. We propose a message-passing algorithm that uses the expectation propagation rule to convert messages passed from the demodulator and decoder to the equalizer and computes messages...... returned by the equalizer by using a partial Gaussian approximation (PGA). We exploit the specific structure of the ISI channel model to compute the latter messages from the beliefs obtained using a Kalman smoother/equalizer. Doing so leads to a significant complexity reduction compared to the initial PGA...
Palm distributions for log Gaussian Cox processes
DEFF Research Database (Denmark)
Coeurjolly, Jean-Francois; Møller, Jesper; Waagepetersen, Rasmus Plenge
2017-01-01
This paper establishes a remarkable result regarding Palm distributions for a log Gaussian Cox process: the reduced Palm distribution for a log Gaussian Cox process is itself a log Gaussian Cox process that only differs from the original log Gaussian Cox process in the intensity function. This new...... result is used to study functional summaries for log Gaussian Cox processes....
Geometry of Gaussian quantum states
International Nuclear Information System (INIS)
Link, Valentin; Strunz, Walter T
2015-01-01
We study the Hilbert–Schmidt measure on the manifold of mixed Gaussian states in multi-mode continuous variable quantum systems. An analytical expression for the Hilbert–Schmidt volume element is derived. Its corresponding probability measure can be used to study typical properties of Gaussian states. It turns out that although the manifold of Gaussian states is unbounded, an ensemble of Gaussian states distributed according to this measure still has a normalizable distribution of symplectic eigenvalues, from which unitarily invariant properties can be obtained. By contrast, we find that for an ensemble of one-mode Gaussian states based on the Bures measure the corresponding distribution cannot be normalized. As important applications, we determine the distribution and the mean value of von Neumann entropy and purity for the Hilbert–Schmidt measure. (paper)
Gaussian elimination in split unitary groups with an application to public-key cryptography
Directory of Open Access Journals (Sweden)
Ayan Mahalanobis
2017-07-01
Full Text Available Gaussian elimination is used in special linear groups to solve the word problem. In this paper, we extend Gaussian elimination to split unitary groups. These algorithms have an application in building a public-key cryptosystem, we demonstrate that.
PSSGP : Program for Simulation of Stationary Gaussian Processes
DEFF Research Database (Denmark)
Sørensen, John Dalsgaard
This report describes the computer program PSSGP. PSSGP can be used to simulate realizations of stationary Gaussian stochastic processes. The simulation algorithm can be coupled with some applications. One possibility is to use PSSGP to estimate the first-passage density function of a given system...
Supervised Gaussian mixture model based remote sensing image ...
African Journals Online (AJOL)
Using the supervised classification technique, both simulated and empirical satellite remote sensing data are used to train and test the Gaussian mixture model algorithm. For the purpose of validating the experiment, the resulting classified satellite image is compared with the ground truth data. For the simulated modelling, ...
Resource theory of non-Gaussian operations
Zhuang, Quntao; Shor, Peter W.; Shapiro, Jeffrey H.
2018-05-01
Non-Gaussian states and operations are crucial for various continuous-variable quantum information processing tasks. To quantitatively understand non-Gaussianity beyond states, we establish a resource theory for non-Gaussian operations. In our framework, we consider Gaussian operations as free operations, and non-Gaussian operations as resources. We define entanglement-assisted non-Gaussianity generating power and show that it is a monotone that is nonincreasing under the set of free superoperations, i.e., concatenation and tensoring with Gaussian channels. For conditional unitary maps, this monotone can be analytically calculated. As examples, we show that the non-Gaussianity of ideal photon-number subtraction and photon-number addition equal the non-Gaussianity of the single-photon Fock state. Based on our non-Gaussianity monotone, we divide non-Gaussian operations into two classes: (i) the finite non-Gaussianity class, e.g., photon-number subtraction, photon-number addition, and all Gaussian-dilatable non-Gaussian channels; and (ii) the diverging non-Gaussianity class, e.g., the binary phase-shift channel and the Kerr nonlinearity. This classification also implies that not all non-Gaussian channels are exactly Gaussian dilatable. Our resource theory enables a quantitative characterization and a first classification of non-Gaussian operations, paving the way towards the full understanding of non-Gaussianity.
XDGMM: eXtreme Deconvolution Gaussian Mixture Modeling
Holoien, Thomas W.-S.; Marshall, Philip J.; Wechsler, Risa H.
2017-08-01
XDGMM uses Gaussian mixtures to do density estimation of noisy, heterogenous, and incomplete data using extreme deconvolution (XD) algorithms which is compatible with the scikit-learn machine learning methods. It implements both the astroML and Bovy et al. (2011) algorithms, and extends the BaseEstimator class from scikit-learn so that cross-validation methods work. It allows the user to produce a conditioned model if values of some parameters are known.
Handbook of Gaussian basis sets
International Nuclear Information System (INIS)
Poirier, R.; Kari, R.; Csizmadia, I.G.
1985-01-01
A collection of a large body of information is presented useful for chemists involved in molecular Gaussian computations. Every effort has been made by the authors to collect all available data for cartesian Gaussian as found in the literature up to July of 1984. The data in this text includes a large collection of polarization function exponents but in this case the collection is not complete. Exponents for Slater type orbitals (STO) were included for completeness. This text offers a collection of Gaussian exponents primarily without criticism. (Auth.)
Fallah-Shorshani, Masoud; Shekarrizfard, Maryam; Hatzopoulou, Marianne
2017-10-01
Dispersion of road transport emissions in urban metropolitan areas is typically simulated using Gaussian models that ignore the turbulence and drag induced by buildings, which are especially relevant for areas with dense downtown cores. To consider the effect of buildings, street canyon models are used but often at the level of single urban corridors and small road networks. In this paper, we compare and validate two dispersion models with widely varying algorithms, across a modelling domain consisting of the City of Montreal, Canada accounting for emissions of more 40,000 roads. The first dispersion model is based on flow decomposition into the urban canopy sub-flow as well as overlying airflow. It takes into account the specific height and geometry of buildings along each road. The second model is a Gaussian puff dispersion model, which handles complex terrain and incorporates three-dimensional meteorology, but accounts for buildings only through variations in the initial vertical mixing coefficient. Validation against surface observations indicated that both models under-predicted measured concentrations. Average weekly exposure surfaces derived from both models were found to be reasonably correlated (r = 0.8) although the Gaussian dispersion model tended to underestimate concentrations around the roadways compared to the street canyon model. In addition, both models were used to estimate exposures of a representative sample of the Montreal population composed of 1319 individuals. Large differences were noted whereby exposures derived from the Gaussian puff model were significantly lower than exposures derived from the street canyon model, an expected result considering the concentration of population around roadways. These differences have large implications for the analyses of health effects associated with NO2 exposure.
Scaled unscented transform Gaussian sum filter: Theory and application
Luo, Xiaodong
2010-05-01
In this work we consider the state estimation problem in nonlinear/non-Gaussian systems. We introduce a framework, called the scaled unscented transform Gaussian sum filter (SUT-GSF), which combines two ideas: the scaled unscented Kalman filter (SUKF) based on the concept of scaled unscented transform (SUT) (Julier and Uhlmann (2004) [16]), and the Gaussian mixture model (GMM). The SUT is used to approximate the mean and covariance of a Gaussian random variable which is transformed by a nonlinear function, while the GMM is adopted to approximate the probability density function (pdf) of a random variable through a set of Gaussian distributions. With these two tools, a framework can be set up to assimilate nonlinear systems in a recursive way. Within this framework, one can treat a nonlinear stochastic system as a mixture model of a set of sub-systems, each of which takes the form of a nonlinear system driven by a known Gaussian random process. Then, for each sub-system, one applies the SUKF to estimate the mean and covariance of the underlying Gaussian random variable transformed by the nonlinear governing equations of the sub-system. Incorporating the estimations of the sub-systems into the GMM gives an explicit (approximate) form of the pdf, which can be regarded as a "complete" solution to the state estimation problem, as all of the statistical information of interest can be obtained from the explicit form of the pdf (Arulampalam et al. (2002) [7]). In applications, a potential problem of a Gaussian sum filter is that the number of Gaussian distributions may increase very rapidly. To this end, we also propose an auxiliary algorithm to conduct pdf re-approximation so that the number of Gaussian distributions can be reduced. With the auxiliary algorithm, in principle the SUT-GSF can achieve almost the same computational speed as the SUKF if the SUT-GSF is implemented in parallel. As an example, we will use the SUT-GSF to assimilate a 40-dimensional system due to
Reconstruction of electrons with the Gaussian-sum filter in the CMS tracker at the LHC
International Nuclear Information System (INIS)
Adam, W; Fruehwirth, R; Strandlie, A; Todorov, T
2005-01-01
The bremsstrahlung energy loss distribution of electrons propagating in matter is highly non-Gaussian. Because the Kalman filter relies solely on Gaussian probability density functions, it is not necessarily the optimal reconstruction algorithm for electron tracks. A Gaussian-sum filter (GSF) algorithm for electron reconstruction in the CMS tracker has therefore been developed and implemented. The basic idea is to model the bremsstrahlung energy loss distribution by a Gaussian mixture rather than by a single Gaussian. It is shown that the GSF is able to improve the momentum resolution of electrons compared to the standard Kalman filter. The momentum resolution and the quality of the error estimate are studied both with a fast simulation, modelling the radiative energy loss in a simplified detector, and the full CMS tracker simulation. (research note from collaboration)
Reconstruction of Electrons with the Gaussian-Sum Filter in the CMS Tracker at the LHC
Adam, Wolfgang; Strandlie, Are; Todor, T
2005-01-01
The bremsstrahlung energy loss distribution of electrons propagating in matter is highly non-Gaussian. Because the Kalman filter relies solely on Gaussian probability density functions, it is not necessarily the optimal reconstruction algorithm for electron tracks. A Gaussian-sum filter (GSF) algorithm for electron reconstruction in the CMS tracker has therefore been developed and implemented. The basic idea is to model the bremsstrahlung energy loss distribution by a Gaussian mixture rather than by a single Gaussian. It is shown that the GSF is able to improve the momentum resolution of electrons compared to the standard Kalman filter. The momentum resolution and the quality of he error estimate are studied both with a fast simulation, modelling the radiative energy loss in a simplified detector, and the full CMS tracker simulation.
International Nuclear Information System (INIS)
Schlegel, H.B.; Binkley, J.S.; Pople, J.A.
1984-01-01
Formulas are developed for the first and second derivatives of two electron integrals over Cartesian Gaussians. Integrals and integral derivatives are evaluated by the Rys polynomial method. Higher angular momentum functions are not used to calculate the integral derivatives; instead the integral formulas are differentiated directly to produce compact and efficient expressions for the integral derivatives. The use of this algorithm in the ab initio molecular orbital programs gaussIan 80 and gaussIan 82 is discussed. Representative timings for some small molecules with several basis sets are presented. This method is compared with previously published algorithms and its computational merits are discussed
Outage performance of cognitive radio systems with Improper Gaussian signaling
Amin, Osama
2015-06-14
Improper Gaussian signaling has proved its ability to improve the achievable rate of the systems that suffer from interference compared with proper Gaussian signaling. In this paper, we first study impact of improper Gaussian signaling on the performance of the cognitive radio system by analyzing the outage probability of both the primary user (PU) and the secondary user (SU). We derive exact expression of the SU outage probability and upper and lower bounds for the PU outage probability. Then, we design the SU signal by adjusting its transmitted power and the circularity coefficient to minimize the SU outage probability while maintaining a certain PU quality-of-service. Finally, we evaluate the proposed bounds and adaptive algorithms by numerical results.
Self-assembled structures of Gaussian nematic particles.
Nikoubashman, Arash; Likos, Christos N
2010-03-17
We investigate the stable crystalline configurations of a nematic liquid crystal made of soft parallel ellipsoidal particles interacting via a repulsive, anisotropic Gaussian potential. For this purpose, we use genetic algorithms (GA) in order to predict all relevant and possible solid phase candidates into which this fluid can freeze. Subsequently we present and discuss the emerging novel structures and the resulting zero-temperature phase diagram of this system. The latter features a variety of crystalline arrangements, in which the elongated Gaussian particles in general do not align with any one of the high-symmetry crystallographic directions, a compromise arising from the interplay and competition between anisotropic repulsions and crystal ordering. Only at very strong degrees of elongation does a tendency of the Gaussian nematics to align with the longest axis of the elementary unit cell emerge.
Gaussian entanglement distribution via satellite
Hosseinidehaj, Nedasadat; Malaney, Robert
2015-02-01
In this work we analyze three quantum communication schemes for the generation of Gaussian entanglement between two ground stations. Communication occurs via a satellite over two independent atmospheric fading channels dominated by turbulence-induced beam wander. In our first scheme, the engineering complexity remains largely on the ground transceivers, with the satellite acting simply as a reflector. Although the channel state information of the two atmospheric channels remains unknown in this scheme, the Gaussian entanglement generation between the ground stations can still be determined. On the ground, distillation and Gaussification procedures can be applied, leading to a refined Gaussian entanglement generation rate between the ground stations. We compare the rates produced by this first scheme with two competing schemes in which quantum complexity is added to the satellite, thereby illustrating the tradeoff between space-based engineering complexity and the rate of ground-station entanglement generation.
Tachyon mediated non-Gaussianity
International Nuclear Information System (INIS)
Dutta, Bhaskar; Leblond, Louis; Kumar, Jason
2008-01-01
We describe a general scenario where primordial non-Gaussian curvature perturbations are generated in models with extra scalar fields. The extra scalars communicate to the inflaton sector mainly through the tachyonic (waterfall) field condensing at the end of hybrid inflation. These models can yield significant non-Gaussianity of the local shape, and both signs of the bispectrum can be obtained. These models have cosmic strings and a nearly flat power spectrum, which together have been recently shown to be a good fit to WMAP data. We illustrate with a model of inflation inspired from intersecting brane models.
IBS for non-gaussian distributions
International Nuclear Information System (INIS)
Fedotov, A.; Sidorin, A.O.; Smirnov, A.V.
2010-01-01
In many situations distribution can significantly deviate from Gaussian which requires accurate treatment of IBS. Our original interest in this problem was motivated by the need to have an accurate description of beam evolution due to IBS while distribution is strongly affected by the external electron cooling force. A variety of models with various degrees of approximation were developed and implemented in BETACOOL in the past to address this topic. A more complete treatment based on the friction coefficient and full 3-D diffusion tensor was introduced in BETACOOL at the end of 2007 under the name 'local IBS model'. Such a model allowed us calculation of IBS for an arbitrary beam distribution. The numerical benchmarking of this local IBS algorithm and its comparison with other models was reported before. In this paper, after briefly describing the model and its limitations, they present its comparison with available experimental data.
The Multivariate Gaussian Probability Distribution
DEFF Research Database (Denmark)
Ahrendt, Peter
2005-01-01
This technical report intends to gather information about the multivariate gaussian distribution, that was previously not (at least to my knowledge) to be found in one place and written as a reference manual. Additionally, some useful tips and tricks are collected that may be useful in practical ...
On Gaussian conditional independence structures
Czech Academy of Sciences Publication Activity Database
Lněnička, Radim; Matúš, František
2007-01-01
Roč. 43, č. 3 (2007), s. 327-342 ISSN 0023-5954 R&D Projects: GA AV ČR IAA100750603 Institutional research plan: CEZ:AV0Z10750506 Keywords : multivariate Gaussian distribution * positive definite matrices * determinants * gaussoids * covariance selection models * Markov perfectness Subject RIV: BA - General Mathematics Impact factor: 0.552, year: 2007
Gaussian processes for machine learning.
Seeger, Matthias
2004-04-01
Gaussian processes (GPs) are natural generalisations of multivariate Gaussian random variables to infinite (countably or continuous) index sets. GPs have been applied in a large number of fields to a diverse range of ends, and very many deep theoretical analyses of various properties are available. This paper gives an introduction to Gaussian processes on a fairly elementary level with special emphasis on characteristics relevant in machine learning. It draws explicit connections to branches such as spline smoothing models and support vector machines in which similar ideas have been investigated. Gaussian process models are routinely used to solve hard machine learning problems. They are attractive because of their flexible non-parametric nature and computational simplicity. Treated within a Bayesian framework, very powerful statistical methods can be implemented which offer valid estimates of uncertainties in our predictions and generic model selection procedures cast as nonlinear optimization problems. Their main drawback of heavy computational scaling has recently been alleviated by the introduction of generic sparse approximations.13,78,31 The mathematical literature on GPs is large and often uses deep concepts which are not required to fully understand most machine learning applications. In this tutorial paper, we aim to present characteristics of GPs relevant to machine learning and to show up precise connections to other "kernel machines" popular in the community. Our focus is on a simple presentation, but references to more detailed sources are provided.
Array processors based on Gaussian fraction-free method
Energy Technology Data Exchange (ETDEWEB)
Peng, S; Sedukhin, S [Aizu Univ., Aizuwakamatsu, Fukushima (Japan); Sedukhin, I
1998-03-01
The design of algorithmic array processors for solving linear systems of equations using fraction-free Gaussian elimination method is presented. The design is based on a formal approach which constructs a family of planar array processors systematically. These array processors are synthesized and analyzed. It is shown that some array processors are optimal in the framework of linear allocation of computations and in terms of number of processing elements and computing time. (author)
State-Space Inference and Learning with Gaussian Processes
Turner, R; Deisenroth, MP; Rasmussen, CE
2010-01-01
18.10.13 KB. Ok to add author version to spiral, authors hold copyright. State-space inference and learning with Gaussian processes (GPs) is an unsolved problem. We propose a new, general methodology for inference and learning in nonlinear state-space models that are described probabilistically by non-parametric GP models. We apply the expectation maximization algorithm to iterate between inference in the latent state-space and learning the parameters of the underlying GP dynamics model. C...
Haupt, Sue Ellen; Beyer-Lout, Anke; Long, Kerrie J.; Young, George S.
Assimilating concentration data into an atmospheric transport and dispersion model can provide information to improve downwind concentration forecasts. The forecast model is typically a one-way coupled set of equations: the meteorological equations impact the concentration, but the concentration does not generally affect the meteorological field. Thus, indirect methods of using concentration data to influence the meteorological variables are required. The problem studied here involves a simple wind field forcing Gaussian dispersion. Two methods of assimilating concentration data to infer the wind direction are demonstrated. The first method is Lagrangian in nature and treats the puff as an entity using feature extraction coupled with nudging. The second method is an Eulerian field approach akin to traditional variational approaches, but minimizes the error by using a genetic algorithm (GA) to directly optimize the match between observations and predictions. Both methods show success at inferring the wind field. The GA-variational method, however, is more accurate but requires more computational time. Dynamic assimilation of a continuous release modeled by a Gaussian plume is also demonstrated using the genetic algorithm approach.
Laguerre Gaussian beam multiplexing through turbulence
CSIR Research Space (South Africa)
Trichili, A
2014-08-17
Full Text Available We analyze the effect of atmospheric turbulence on the propagation of multiplexed Laguerre Gaussian modes. We present a method to multiplex Laguerre Gaussian modes using digital holograms and decompose the resulting field after encountering a...
Analytic matrix elements with shifted correlated Gaussians
DEFF Research Database (Denmark)
Fedorov, D. V.
2017-01-01
Matrix elements between shifted correlated Gaussians of various potentials with several form-factors are calculated analytically. Analytic matrix elements are of importance for the correlated Gaussian method in quantum few-body physics.......Matrix elements between shifted correlated Gaussians of various potentials with several form-factors are calculated analytically. Analytic matrix elements are of importance for the correlated Gaussian method in quantum few-body physics....
Gaussian process regression analysis for functional data
Shi, Jian Qing
2011-01-01
Gaussian Process Regression Analysis for Functional Data presents nonparametric statistical methods for functional regression analysis, specifically the methods based on a Gaussian process prior in a functional space. The authors focus on problems involving functional response variables and mixed covariates of functional and scalar variables.Covering the basics of Gaussian process regression, the first several chapters discuss functional data analysis, theoretical aspects based on the asymptotic properties of Gaussian process regression models, and new methodological developments for high dime
Reproducing kernel Hilbert spaces of Gaussian priors
Vaart, van der A.W.; Zanten, van J.H.; Clarke, B.; Ghosal, S.
2008-01-01
We review definitions and properties of reproducing kernel Hilbert spaces attached to Gaussian variables and processes, with a view to applications in nonparametric Bayesian statistics using Gaussian priors. The rate of contraction of posterior distributions based on Gaussian priors can be described
Inflation in random Gaussian landscapes
Energy Technology Data Exchange (ETDEWEB)
Masoumi, Ali; Vilenkin, Alexander; Yamada, Masaki, E-mail: ali@cosmos.phy.tufts.edu, E-mail: vilenkin@cosmos.phy.tufts.edu, E-mail: Masaki.Yamada@tufts.edu [Institute of Cosmology, Department of Physics and Astronomy, Tufts University, Medford, MA 02155 (United States)
2017-05-01
We develop analytic and numerical techniques for studying the statistics of slow-roll inflation in random Gaussian landscapes. As an illustration of these techniques, we analyze small-field inflation in a one-dimensional landscape. We calculate the probability distributions for the maximal number of e-folds and for the spectral index of density fluctuations n {sub s} and its running α {sub s} . These distributions have a universal form, insensitive to the correlation function of the Gaussian ensemble. We outline possible extensions of our methods to a large number of fields and to models of large-field inflation. These methods do not suffer from potential inconsistencies inherent in the Brownian motion technique, which has been used in most of the earlier treatments.
General Galilei Covariant Gaussian Maps
Gasbarri, Giulio; Toroš, Marko; Bassi, Angelo
2017-09-01
We characterize general non-Markovian Gaussian maps which are covariant under Galilean transformations. In particular, we consider translational and Galilean covariant maps and show that they reduce to the known Holevo result in the Markovian limit. We apply the results to discuss measures of macroscopicity based on classicalization maps, specifically addressing dissipation, Galilean covariance and non-Markovianity. We further suggest a possible generalization of the macroscopicity measure defined by Nimmrichter and Hornberger [Phys. Rev. Lett. 110, 16 (2013)].
Gaussian Embeddings for Collaborative Filtering
Dos Santos , Ludovic; Piwowarski , Benjamin; Gallinari , Patrick
2017-01-01
International audience; Most collaborative ltering systems, such as matrix factorization, use vector representations for items and users. Those representations are deterministic, and do not allow modeling the uncertainty of the learned representation, which can be useful when a user has a small number of rated items (cold start), or when there is connict-ing information about the behavior of a user or the ratings of an item. In this paper, we leverage recent works in learning Gaussian embeddi...
Energy-Driven Image Interpolation Using Gaussian Process Regression
Directory of Open Access Journals (Sweden)
Lingling Zi
2012-01-01
Full Text Available Image interpolation, as a method of obtaining a high-resolution image from the corresponding low-resolution image, is a classical problem in image processing. In this paper, we propose a novel energy-driven interpolation algorithm employing Gaussian process regression. In our algorithm, each interpolated pixel is predicted by a combination of two information sources: first is a statistical model adopted to mine underlying information, and second is an energy computation technique used to acquire information on pixel properties. We further demonstrate that our algorithm can not only achieve image interpolation, but also reduce noise in the original image. Our experiments show that the proposed algorithm can achieve encouraging performance in terms of image visualization and quantitative measures.
Genetic Algorithm Optimized Neural Networks Ensemble as ...
African Journals Online (AJOL)
Marquardt algorithm by varying conditions such as inputs, hidden neurons, initialization, training sets and random Gaussian noise injection to ... Several such ensembles formed the population which was evolved to generate the fittest ensemble.
Gaussian variable neighborhood search for the file transfer scheduling problem
Directory of Open Access Journals (Sweden)
Dražić Zorica
2016-01-01
Full Text Available This paper presents new modifications of Variable Neighborhood Search approach for solving the file transfer scheduling problem. To obtain better solutions in a small neighborhood of a current solution, we implement two new local search procedures. As Gaussian Variable Neighborhood Search showed promising results when solving continuous optimization problems, its implementation in solving the discrete file transfer scheduling problem is also presented. In order to apply this continuous optimization method to solve the discrete problem, mapping of uncountable set of feasible solutions into a finite set is performed. Both local search modifications gave better results for the large size instances, as well as better average performance for medium and large size instances. One local search modification achieved significant acceleration of the algorithm. The numerical experiments showed that the results obtained by Gaussian modifications are comparable with the results obtained by standard VNS based algorithms, developed for combinatorial optimization. In some cases Gaussian modifications gave even better results. [Projekat Ministarstava nauke Republike Srbije, br. 174010
Dispersion relations for 1D high-gain FELs
International Nuclear Information System (INIS)
Webb, S.D.; Litvinenko, V.N.
2010-01-01
We present analytical results for the one-dimensional dispersion relation for high-gain FELs. Using kappa-n distributions, we obtain analytical relations between the dispersion relations for various order kappa distributions. Since an exact solution exists for the kappa-1 (Lorentzian) distribution, this provides some insight into the number of modes on the way to the Gaussian distribution.
Color Texture Segmentation by Decomposition of Gaussian Mixture Model
Czech Academy of Sciences Publication Activity Database
Grim, Jiří; Somol, Petr; Haindl, Michal; Pudil, Pavel
2006-01-01
Roč. 19, č. 4225 (2006), s. 287-296 ISSN 0302-9743. [Iberoamerican Congress on Pattern Recognition. CIARP 2006 /11./. Cancun, 14.11.2006-17.11.2006] R&D Projects: GA AV ČR 1ET400750407; GA MŠk 1M0572; GA MŠk 2C06019 EU Projects: European Commission(XE) 507752 - MUSCLE Institutional research plan: CEZ:AV0Z10750506 Keywords : texture segmentation * gaussian mixture model * EM algorithm Subject RIV: IN - Informatics, Computer Science Impact factor: 0.402, year: 2005 http://library.utia.cas.cz/separaty/historie/grim-color texture segmentation by decomposition of gaussian mixture model.pdf
Nonlinear and non-Gaussian Bayesian based handwriting beautification
Shi, Cao; Xiao, Jianguo; Xu, Canhui; Jia, Wenhua
2013-03-01
A framework is proposed in this paper to effectively and efficiently beautify handwriting by means of a novel nonlinear and non-Gaussian Bayesian algorithm. In the proposed framework, format and size of handwriting image are firstly normalized, and then typeface in computer system is applied to optimize vision effect of handwriting. The Bayesian statistics is exploited to characterize the handwriting beautification process as a Bayesian dynamic model. The model parameters to translate, rotate and scale typeface in computer system are controlled by state equation, and the matching optimization between handwriting and transformed typeface is employed by measurement equation. Finally, the new typeface, which is transformed from the original one and gains the best nonlinear and non-Gaussian optimization, is the beautification result of handwriting. Experimental results demonstrate the proposed framework provides a creative handwriting beautification methodology to improve visual acceptance.
Detecting periodicities with Gaussian processes
Directory of Open Access Journals (Sweden)
Nicolas Durrande
2016-04-01
Full Text Available We consider the problem of detecting and quantifying the periodic component of a function given noise-corrupted observations of a limited number of input/output tuples. Our approach is based on Gaussian process regression, which provides a flexible non-parametric framework for modelling periodic data. We introduce a novel decomposition of the covariance function as the sum of periodic and aperiodic kernels. This decomposition allows for the creation of sub-models which capture the periodic nature of the signal and its complement. To quantify the periodicity of the signal, we derive a periodicity ratio which reflects the uncertainty in the fitted sub-models. Although the method can be applied to many kernels, we give a special emphasis to the Matérn family, from the expression of the reproducing kernel Hilbert space inner product to the implementation of the associated periodic kernels in a Gaussian process toolkit. The proposed method is illustrated by considering the detection of periodically expressed genes in the arabidopsis genome.
Das, Siddhartha; Siopsis, George; Weedbrook, Christian
2018-02-01
With the significant advancement in quantum computation during the past couple of decades, the exploration of machine-learning subroutines using quantum strategies has become increasingly popular. Gaussian process regression is a widely used technique in supervised classical machine learning. Here we introduce an algorithm for Gaussian process regression using continuous-variable quantum systems that can be realized with technology based on photonic quantum computers under certain assumptions regarding distribution of data and availability of efficient quantum access. Our algorithm shows that by using a continuous-variable quantum computer a dramatic speedup in computing Gaussian process regression can be achieved, i.e., the possibility of exponentially reducing the time to compute. Furthermore, our results also include a continuous-variable quantum-assisted singular value decomposition method of nonsparse low rank matrices and forms an important subroutine in our Gaussian process regression algorithm.
Energy Technology Data Exchange (ETDEWEB)
Bulatov, A.I.; Chernov, V.S.; Prokopov, L.I.; Proselkov, Yu.M.; Tikhonov, Yu.P.
1980-01-15
A hydrodynamic disperser is suggested which contains a housing, slit nozzles installed on a circular base arranged opposite from each other, resonators secured opposite the nozzle and outlet sleeve. In order to improve the effectiveness of dispersion by throttling the flow, each resonator is made in the form of a crimped plate with crimpings that decrease in height in a direction towards the nozzle.
Monogamy inequality for distributed gaussian entanglement.
Hiroshima, Tohya; Adesso, Gerardo; Illuminati, Fabrizio
2007-02-02
We show that for all n-mode Gaussian states of continuous variable systems, the entanglement shared among n parties exhibits the fundamental monogamy property. The monogamy inequality is proven by introducing the Gaussian tangle, an entanglement monotone under Gaussian local operations and classical communication, which is defined in terms of the squared negativity in complete analogy with the case of n-qubit systems. Our results elucidate the structure of quantum correlations in many-body harmonic lattice systems.
Breaking Gaussian incompatibility on continuous variable quantum systems
Energy Technology Data Exchange (ETDEWEB)
Heinosaari, Teiko, E-mail: teiko.heinosaari@utu.fi [Turku Centre for Quantum Physics, Department of Physics and Astronomy, University of Turku, FI-20014 Turku (Finland); Kiukas, Jukka, E-mail: jukka.kiukas@aber.ac.uk [Department of Mathematics, Aberystwyth University, Penglais, Aberystwyth, SY23 3BZ (United Kingdom); Schultz, Jussi, E-mail: jussi.schultz@gmail.com [Turku Centre for Quantum Physics, Department of Physics and Astronomy, University of Turku, FI-20014 Turku (Finland); Dipartimento di Matematica, Politecnico di Milano, Piazza Leonardo da Vinci 32, I-20133 Milano (Italy)
2015-08-15
We characterise Gaussian quantum channels that are Gaussian incompatibility breaking, that is, transform every set of Gaussian measurements into a set obtainable from a joint Gaussian observable via Gaussian postprocessing. Such channels represent local noise which renders measurements useless for Gaussian EPR-steering, providing the appropriate generalisation of entanglement breaking channels for this scenario. Understanding the structure of Gaussian incompatibility breaking channels contributes to the resource theory of noisy continuous variable quantum information protocols.
International Nuclear Information System (INIS)
Leung Shingyu; Qian Jianliang
2010-01-01
We propose the backward phase flow method to implement the Fourier-Bros-Iagolnitzer (FBI)-transform-based Eulerian Gaussian beam method for solving the Schroedinger equation in the semi-classical regime. The idea of Eulerian Gaussian beams has been first proposed in . In this paper we aim at two crucial computational issues of the Eulerian Gaussian beam method: how to carry out long-time beam propagation and how to compute beam ingredients rapidly in phase space. By virtue of the FBI transform, we address the first issue by introducing the reinitialization strategy into the Eulerian Gaussian beam framework. Essentially we reinitialize beam propagation by applying the FBI transform to wavefields at intermediate time steps when the beams become too wide. To address the second issue, inspired by the original phase flow method, we propose the backward phase flow method which allows us to compute beam ingredients rapidly. Numerical examples demonstrate the efficiency and accuracy of the proposed algorithms.
Performance of monitoring networks estimated from a Gaussian plume model
International Nuclear Information System (INIS)
Seebregts, A.J.; Hienen, J.F.A.
1990-10-01
In support of the ECN study on monitoring strategies after nuclear accidents, the present report describes the analysis of the performance of a monitoring network in a square grid. This network is used to estimate the distribution of the deposition pattern after a release of radioactivity into the atmosphere. The analysis is based upon a single release, a constant wind direction and an atmospheric dispersion according to a simplified Gaussian plume model. A technique is introduced to estimate the parameters in this Gaussian model based upon measurements at specific monitoring locations and linear regression, although this model is intrinsically non-linear. With these estimated parameters and the Gaussian model the distribution of the contamination due to deposition can be estimated. To investigate the relation between the network and the accuracy of the estimates for the deposition, deposition data have been generated by the Gaussian model, including a measurement error by a Monte Carlo simulation and this procedure has been repeated for several grid sizes, dispersion conditions, number of measurements per location, and errors per single measurement. The present technique has also been applied for the mesh sizes of two networks in the Netherlands, viz. the Landelijk Meetnet Radioaciviteit (National Measurement Network on Radioactivity, mesh size approx. 35 km) and the proposed Landelijk Meetnet Nucleaire Incidenten (National Measurement Network on Nuclear Incidents, mesh size approx. 15 km). The results show accuracies of 11 and 7 percent, respectively, if monitoring locations are used more than 10 km away from the postulated accident site. These figures are based upon 3 measurements per location and a dispersion during neutral weather with a wind velocity of 4 m/s. For stable weather conditions and low wind velocities, i.e. a small plume, the calculated accuracies are at least a factor 1.5 worse.The present type of analysis makes a cost-benefit approach to the
Extragalactic dispersion measures of fast radio bursts
International Nuclear Information System (INIS)
Xu, Jun; Han, J. L.
2015-01-01
Fast radio bursts show large dispersion measures, much larger than the Galactic dispersion measure foreground. Therefore, they evidently have an extragalactic origin. We investigate possible contributions to the dispersion measure from host galaxies. We simulate the spatial distribution of fast radio bursts and calculate the dispersion measures along the sightlines from fast radio bursts to the edge of host galaxies by using the scaled NE2001 model for thermal electron density distributions. We find that contributions to the dispersion measure of fast radio bursts from the host galaxy follow a skew Gaussian distribution. The peak and the width at half maximum of the dispersion measure distribution increase with the inclination angle of a spiral galaxy, to large values when the inclination angle is over 70°. The largest dispersion measure produced by an edge-on spiral galaxy can reach a few thousand pc cm −3 , while the dispersion measures from dwarf galaxies and elliptical galaxies have a maximum of only a few tens of pc cm −3 . Notice, however, that additional dispersion measures of tens to hundreds of pc cm −3 can be produced by high density clumps in host galaxies. Simulations that include dispersion measure contributions from the Large Magellanic Cloud and the Andromeda Galaxy are shown as examples to demonstrate how to extract the dispersion measure from the intergalactic medium. (paper)
International Nuclear Information System (INIS)
McHugh, Derek; Buzek, Vladimir; Ziman, Mario
2006-01-01
We present a class of non-Gaussian two-mode continuous-variable states for which the separability criterion for Gaussian states can be employed to detect whether they are separable or not. These states reduce to the two-mode Gaussian states as a special case
Quantum beamstrahlung from gaussian bunches
International Nuclear Information System (INIS)
Chen, P.
1987-08-01
The method of Baier and Katkov is applied to calculate the correction terms to the Sokolov-Ternov radiation formula due to the variation of the magnetic field strength along the trajectory of a radiating particle. We carry the calculation up to the second order in the power expansion of B tau/B, where tau is the formation time of radiation. The expression is then used to estimate the quantum beamstrahlung average energy loss from e + e - bunches with gaussian distribution in bunch currents. We show that the effect of the field variation is to reduce the average energy loss from previous calculations based on the Sokolov-Ternov formula or its equivalent. Due to the limitation of our method, only an upper bound of the reduction is obtained. 18 refs
Solving Dynamic Traveling Salesman Problem Using Dynamic Gaussian Process Regression
Directory of Open Access Journals (Sweden)
Stephen M. Akandwanaho
2014-01-01
Full Text Available This paper solves the dynamic traveling salesman problem (DTSP using dynamic Gaussian Process Regression (DGPR method. The problem of varying correlation tour is alleviated by the nonstationary covariance function interleaved with DGPR to generate a predictive distribution for DTSP tour. This approach is conjoined with Nearest Neighbor (NN method and the iterated local search to track dynamic optima. Experimental results were obtained on DTSP instances. The comparisons were performed with Genetic Algorithm and Simulated Annealing. The proposed approach demonstrates superiority in finding good traveling salesman problem (TSP tour and less computational time in nonstationary conditions.
Gaussian multiscale aggregation applied to segmentation in hand biometrics.
de Santos Sierra, Alberto; Avila, Carmen Sánchez; Casanova, Javier Guerra; del Pozo, Gonzalo Bailador
2011-01-01
This paper presents an image segmentation algorithm based on Gaussian multiscale aggregation oriented to hand biometric applications. The method is able to isolate the hand from a wide variety of background textures such as carpets, fabric, glass, grass, soil or stones. The evaluation was carried out by using a publicly available synthetic database with 408,000 hand images in different backgrounds, comparing the performance in terms of accuracy and computational cost to two competitive segmentation methods existing in literature, namely Lossy Data Compression (LDC) and Normalized Cuts (NCuts). The results highlight that the proposed method outperforms current competitive segmentation methods with regard to computational cost, time performance, accuracy and memory usage.
Graphical Gaussian models with edge and vertex symmetries
DEFF Research Database (Denmark)
Højsgaard, Søren; Lauritzen, Steffen L
2008-01-01
We introduce new types of graphical Gaussian models by placing symmetry restrictions on the concentration or correlation matrix. The models can be represented by coloured graphs, where parameters that are associated with edges or vertices of the same colour are restricted to being identical. We...... study the properties of such models and derive the necessary algorithms for calculating maximum likelihood estimates. We identify conditions for restrictions on the concentration and correlation matrices being equivalent. This is for example the case when symmetries are generated by permutation...
Buhmann, Stefan Yoshi
2012-01-01
In this book, a modern unified theory of dispersion forces on atoms and bodies is presented which covers a broad range of advanced aspects and scenarios. Macroscopic quantum electrodynamics is shown to provide a powerful framework for dispersion forces which allows for discussing general properties like their non-additivity and the relation between microscopic and macroscopic interactions. It is demonstrated how the general results can be used to obtain dispersion forces on atoms in the presence of bodies of various shapes and materials. Starting with a brief recapitulation of volume I, this volume II deals especially with bodies of irregular shapes, universal scaling laws, dynamical forces on excited atoms, enhanced forces in cavity quantum electrodynamics, non-equilibrium forces in thermal environments and quantum friction. The book gives both the specialist and those new to the field a thorough overview over recent results in the field. It provides a toolbox for studying dispersion forces in various contex...
Limit theorems for functionals of Gaussian vectors
Institute of Scientific and Technical Information of China (English)
Hongshuai DAI; Guangjun SHEN; Lingtao KONG
2017-01-01
Operator self-similar processes,as an extension of self-similar processes,have been studied extensively.In this work,we study limit theorems for functionals of Gaussian vectors.Under some conditions,we determine that the limit of partial sums of functionals of a stationary Gaussian sequence of random vectors is an operator self-similar process.
Palm distributions for log Gaussian Cox processes
DEFF Research Database (Denmark)
Coeurjolly, Jean-Francois; Møller, Jesper; Waagepetersen, Rasmus
This paper reviews useful results related to Palm distributions of spatial point processes and provides a new result regarding the characterization of Palm distributions for the class of log Gaussian Cox processes. This result is used to study functional summary statistics for a log Gaussian Cox...
Gaussian limit of compact spin systems
International Nuclear Information System (INIS)
Bellissard, J.; Angelis, G.F. de
1981-01-01
It is shown that the Wilson and Wilson-Villain U(1) models reproduce, in the low coupling limit, the gaussian lattice approximation of the Euclidean electromagnetic field. By the same methods it is also possible to prove that the plane rotator and the Villain model share a common gaussian behaviour in the low temperature limit. (Auth.)
On the dependence structure of Gaussian queues
Es-Saghouani, A.; Mandjes, M.R.H.
2009-01-01
In this article we study Gaussian queues (that is, queues fed by Gaussian processes, such as fractional Brownian motion (fBm) and the integrated Ornstein-Uhlenbeck (iOU) process), with a focus on the dependence structure of the workload process. The main question is to what extent does the workload
Shedding new light on Gaussian harmonic analysis
Teuwen, J.J.B.
2016-01-01
This dissertation consists out of two rather disjoint parts. One part concerns some results on Gaussian harmonic analysis and the other on an optimization problem in optics. In the first part we study the Ornstein–Uhlenbeck process with respect to the Gaussian measure. We focus on two areas. One is
Generation of correlated finite alphabet waveforms using gaussian random variables
Jardak, Seifallah
2014-09-01
Correlated waveforms have a number of applications in different fields, such as radar and communication. It is very easy to generate correlated waveforms using infinite alphabets, but for some of the applications, it is very challenging to use them in practice. Moreover, to generate infinite alphabet constant envelope correlated waveforms, the available research uses iterative algorithms, which are computationally very expensive. In this work, we propose simple novel methods to generate correlated waveforms using finite alphabet constant and non-constant-envelope symbols. To generate finite alphabet waveforms, the proposed method map the Gaussian random variables onto the phase-shift-keying, pulse-amplitude, and quadrature-amplitude modulation schemes. For such mapping, the probability-density-function of Gaussian random variables is divided into M regions, where M is the number of alphabets in the corresponding modulation scheme. By exploiting the mapping function, the relationship between the cross-correlation of Gaussian and finite alphabet symbols is derived. To generate equiprobable symbols, the area of each region is kept same. If the requirement is to have each symbol with its own unique probability, the proposed scheme allows us that as well. Although, the proposed scheme is general, the main focus of this paper is to generate finite alphabet waveforms for multiple-input multiple-output radar, where correlated waveforms are used to achieve desired beampatterns. © 2014 IEEE.
Entanglement in Gaussian matrix-product states
International Nuclear Information System (INIS)
Adesso, Gerardo; Ericsson, Marie
2006-01-01
Gaussian matrix-product states are obtained as the outputs of projection operations from an ancillary space of M infinitely entangled bonds connecting neighboring sites, applied at each of N sites of a harmonic chain. Replacing the projections by associated Gaussian states, the building blocks, we show that the entanglement range in translationally invariant Gaussian matrix-product states depends on how entangled the building blocks are. In particular, infinite entanglement in the building blocks produces fully symmetric Gaussian states with maximum entanglement range. From their peculiar properties of entanglement sharing, a basic difference with spin chains is revealed: Gaussian matrix-product states can possess unlimited, long-range entanglement even with minimum number of ancillary bonds (M=1). Finally we discuss how these states can be experimentally engineered from N copies of a three-mode building block and N two-mode finitely squeezed states
Gaussian vs non-Gaussian turbulence: impact on wind turbine loads
DEFF Research Database (Denmark)
Berg, Jacob; Natarajan, Anand; Mann, Jakob
2016-01-01
taking into account the safety factor for extreme moments. Other extreme load moments as well as the fatigue loads are not affected because of the use of non-Gaussian turbulent inflow. It is suggested that the turbine thus acts like a low-pass filter that averages out the non-Gaussian behaviour, which......From large-eddy simulations of atmospheric turbulence, a representation of Gaussian turbulence is constructed by randomizing the phases of the individual modes of variability. Time series of Gaussian turbulence are constructed and compared with its non-Gaussian counterpart. Time series from the two...
Mu, Hongqian; Wang, Muguang; Tang, Yu; Zhang, Jing; Jian, Shuisheng
2018-03-01
A novel scheme for the generation of FCC-compliant UWB pulse is proposed based on modified Gaussian quadruplet and incoherent wavelength-to-time conversion. The modified Gaussian quadruplet is synthesized based on linear sum of a broad Gaussian pulse and two narrow Gaussian pulses with the same pulse-width and amplitude peak. Within specific parameter range, FCC-compliant UWB with spectral power efficiency of higher than 39.9% can be achieved. In order to realize the designed waveform, a UWB generator based on spectral shaping and incoherent wavelength-to-time mapping is proposed. The spectral shaper is composed of a Gaussian filter and a programmable filter. Single-mode fiber functions as both dispersion device and transmission medium. Balanced photodetection is employed to combine linearly the broad Gaussian pulse and two narrow Gaussian pulses, and at same time to suppress pulse pedestals that result in low-frequency components. The proposed UWB generator can be reconfigured for UWB doublet by operating the programmable filter as a single-band Gaussian filter. The feasibility of proposed UWB generator is demonstrated experimentally. Measured UWB pulses match well with simulation results. FCC-compliant quadruplet with 10-dB bandwidth of 6.88-GHz, fractional bandwidth of 106.8% and power efficiency of 51% is achieved.
Directory of Open Access Journals (Sweden)
Yun Wang
2016-01-01
Full Text Available Gamma Gaussian inverse Wishart cardinalized probability hypothesis density (GGIW-CPHD algorithm was always used to track group targets in the presence of cluttered measurements and missing detections. A multiple models GGIW-CPHD algorithm based on best-fitting Gaussian approximation method (BFG and strong tracking filter (STF is proposed aiming at the defect that the tracking error of GGIW-CPHD algorithm will increase when the group targets are maneuvering. The best-fitting Gaussian approximation method is proposed to implement the fusion of multiple models using the strong tracking filter to correct the predicted covariance matrix of the GGIW component. The corresponding likelihood functions are deduced to update the probability of multiple tracking models. From the simulation results we can see that the proposed tracking algorithm MM-GGIW-CPHD can effectively deal with the combination/spawning of groups and the tracking error of group targets in the maneuvering stage is decreased.
International Nuclear Information System (INIS)
Borisenko, Konstantin B; Kirkland, Angus I
2014-01-01
We describe an algorithm to reconstruct the electron exit wave of a weak-phase object from single diffraction pattern. The algorithm uses analytic formulations describing the diffraction intensities through a representation of the object exit wave in a Gaussian basis. The reconstruction is achieved by solving an overdetermined system of non-linear equations using an easily parallelisable global multi-start search with Levenberg-Marquard optimisation and analytic derivatives
Increasing Entanglement between Gaussian States by Coherent Photon Subtraction
DEFF Research Database (Denmark)
Ourjoumtsev, Alexei; Dantan, Aurelien Romain; Tualle Brouri, Rosa
2007-01-01
We experimentally demonstrate that the entanglement between Gaussian entangled states can be increased by non-Gaussian operations. Coherent subtraction of single photons from Gaussian quadrature-entangled light pulses, created by a nondegenerate parametric amplifier, produces delocalized states...
Representation of Gaussian semimartingales with applications to the covariance function
DEFF Research Database (Denmark)
Basse-O'Connor, Andreas
2010-01-01
stationary Gaussian semimartingales and their canonical decomposition. Thirdly, we give a new characterization of the covariance function of Gaussian semimartingales, which enable us to characterize the class of martingales and the processes of bounded variation among the Gaussian semimartingales. We...
SNPMClust: Bivariate Gaussian Genotype Clustering and Calling for Illumina Microarrays
Directory of Open Access Journals (Sweden)
Stephen W. Erickson
2016-07-01
Full Text Available SNPMClust is an R package for genotype clustering and calling with Illumina microarrays. It was originally developed for studies using the GoldenGate custom genotyping platform but can be used with other Illumina platforms, including Infinium BeadChip. The algorithm first rescales the fluorescent signal intensity data, adds empirically derived pseudo-data to minor allele genotype clusters, then uses the package mclust for bivariate Gaussian model fitting. We compared the accuracy and sensitivity of SNPMClust to that of GenCall, Illumina's proprietary algorithm, on a data set of 94 whole-genome amplified buccal (cheek swab DNA samples. These samples were genotyped on a custom panel which included 1064 SNPs for which the true genotype was known with high confidence. SNPMClust produced uniformly lower false call rates over a wide range of overall call rates.
Using Geometrical Properties for Fast Indexation of Gaussian Vector Quantizers
Directory of Open Access Journals (Sweden)
Vassilieva EA
2007-01-01
Full Text Available Vector quantization is a classical method used in mobile communications. Each sequence of samples of the discretized vocal signal is associated to the closest -dimensional codevector of a given set called codebook. Only the binary indices of these codevectors (the codewords are transmitted over the channel. Since channels are generally noisy, the codewords received are often slightly different from the codewords sent. In order to minimize the distortion of the original signal due to this noisy transmission, codevectors indexed by one-bit different codewords should have a small mutual Euclidean distance. This paper is devoted to this problem of index assignment of binary codewords to the codevectors. When the vector quantizer has a Gaussian structure, we show that a fast index assignment algorithm based on simple geometrical and combinatorial considerations can improve the SNR at the receiver by 5dB with respect to a purely random assignment. We also show that in the Gaussian case this algorithm outperforms the classical combinatorial approach in the field.
Some continual integrals from gaussian forms
International Nuclear Information System (INIS)
Mazmanishvili, A.S.
1985-01-01
The result summary of continual integration of gaussian functional type is given. The summary contains 124 continual integrals which are the mathematical expectation of the corresponding gaussian form by the continuum of random trajectories of four types: real-valued Ornstein-Uhlenbeck process, Wiener process, complex-valued Ornstein-Uhlenbeck process and the stochastic harmonic one. The summary includes both the known continual integrals and the unpublished before integrals. Mathematical results of the continual integration carried in the work may be applied in the problem of the theory of stochastic process, approaching to the finding of mean from gaussian forms by measures generated by the pointed stochastic processes
Loop corrections to primordial non-Gaussianity
Boran, Sibel; Kahya, E. O.
2018-02-01
We discuss quantum gravitational loop effects to observable quantities such as curvature power spectrum and primordial non-Gaussianity of cosmic microwave background (CMB) radiation. We first review the previously shown case where one gets a time dependence for zeta-zeta correlator due to loop corrections. Then we investigate the effect of loop corrections to primordial non-Gaussianity of CMB. We conclude that, even with a single scalar inflaton, one might get a huge value for non-Gaussianity which would exceed the observed value by at least 30 orders of magnitude. Finally we discuss the consequences of this result for scalar driven inflationary models.
Gaussian Mixture Model of Heart Rate Variability
Costa, Tommaso; Boccignone, Giuseppe; Ferraro, Mario
2012-01-01
Heart rate variability (HRV) is an important measure of sympathetic and parasympathetic functions of the autonomic nervous system and a key indicator of cardiovascular condition. This paper proposes a novel method to investigate HRV, namely by modelling it as a linear combination of Gaussians. Results show that three Gaussians are enough to describe the stationary statistics of heart variability and to provide a straightforward interpretation of the HRV power spectrum. Comparisons have been made also with synthetic data generated from different physiologically based models showing the plausibility of the Gaussian mixture parameters. PMID:22666386
Non-Gaussianity from isocurvature perturbations
Energy Technology Data Exchange (ETDEWEB)
Kawasaki, Masahiro; Nakayama, Kazunori; Sekiguchi, Toyokazu; Suyama, Teruaki [Institute for Cosmic Ray Research, University of Tokyo, Kashiwa 277-8582 (Japan); Takahashi, Fuminobu, E-mail: kawasaki@icrr.u-tokyo.ac.jp, E-mail: nakayama@icrr.u-tokyo.ac.jp, E-mail: sekiguti@icrr.u-tokyo.ac.jp, E-mail: suyama@icrr.u-tokyo.ac.jp, E-mail: fuminobu.takahashi@ipmu.jp [Institute for the Physics and Mathematics of the Universe, University of Tokyo, Kashiwa 277-8568 (Japan)
2008-11-15
We develop a formalism for studying non-Gaussianity in both curvature and isocurvature perturbations. It is shown that non-Gaussianity in the isocurvature perturbation between dark matter and photons leaves distinct signatures in the cosmic microwave background temperature fluctuations, which may be confirmed in future experiments, or possibly even in the currently available observational data. As an explicit example, we consider the quantum chromodynamics axion and show that it can actually induce sizable non-Gaussianity for the inflationary scale, H{sub inf} = O(10{sup 9}-10{sup 11}) GeV.
Gaussian measures of entanglement versus negativities: Ordering of two-mode Gaussian states
International Nuclear Information System (INIS)
Adesso, Gerardo; Illuminati, Fabrizio
2005-01-01
We study the entanglement of general (pure or mixed) two-mode Gaussian states of continuous-variable systems by comparing the two available classes of computable measures of entanglement: entropy-inspired Gaussian convex-roof measures and positive partial transposition-inspired measures (negativity and logarithmic negativity). We first review the formalism of Gaussian measures of entanglement, adopting the framework introduced in M. M. Wolf et al., Phys. Rev. A 69, 052320 (2004), where the Gaussian entanglement of formation was defined. We compute explicitly Gaussian measures of entanglement for two important families of nonsymmetric two-mode Gaussian state: namely, the states of extremal (maximal and minimal) negativities at fixed global and local purities, introduced in G. Adesso et al., Phys. Rev. Lett. 92, 087901 (2004). This analysis allows us to compare the different orderings induced on the set of entangled two-mode Gaussian states by the negativities and by the Gaussian measures of entanglement. We find that in a certain range of values of the global and local purities (characterizing the covariance matrix of the corresponding extremal states), states of minimum negativity can have more Gaussian entanglement of formation than states of maximum negativity. Consequently, Gaussian measures and negativities are definitely inequivalent measures of entanglement on nonsymmetric two-mode Gaussian states, even when restricted to a class of extremal states. On the other hand, the two families of entanglement measures are completely equivalent on symmetric states, for which the Gaussian entanglement of formation coincides with the true entanglement of formation. Finally, we show that the inequivalence between the two families of continuous-variable entanglement measures is somehow limited. Namely, we rigorously prove that, at fixed negativities, the Gaussian measures of entanglement are bounded from below. Moreover, we provide some strong evidence suggesting that they
Rahsepar, Shokouhalsadat; Smit, Martijn P.J.; Murk, Albertinka J.; Rijnaarts, Huub H.M.; Langenhoff, Alette A.M.
2016-01-01
Chemical dispersants were used in response to the Deepwater Horizon oil spill in the Gulf of Mexico, both at the sea surface and the wellhead. Their effect on oil biodegradation is unclear, as studies showed both inhibition and enhancement. This study addresses the effect of Corexit on oil
E-PLE: an Algorithm for Image Inpainting
Directory of Open Access Journals (Sweden)
Yi-Qing Wang
2013-12-01
Full Text Available Gaussian mixture is a powerful tool for modeling the patch prior. In this work, a probabilisticview of an existing algorithm piecewise linear estimation (PLE for image inpainting is presentedwhich leads to several theoretical and numerical improvements based on an effective use ofGaussian mixture.
Optimal unitary dilation for bosonic Gaussian channels
International Nuclear Information System (INIS)
Caruso, Filippo; Eisert, Jens; Giovannetti, Vittorio; Holevo, Alexander S.
2011-01-01
A general quantum channel can be represented in terms of a unitary interaction between the information-carrying system and a noisy environment. In this paper the minimal number of quantum Gaussian environmental modes required to provide a unitary dilation of a multimode bosonic Gaussian channel is analyzed for both pure and mixed environments. We compute this quantity in the case of pure environment corresponding to the Stinespring representation and give an improved estimate in the case of mixed environment. The computations rely, on one hand, on the properties of the generalized Choi-Jamiolkowski state and, on the other hand, on an explicit construction of the minimal dilation for arbitrary bosonic Gaussian channel. These results introduce a new quantity reflecting ''noisiness'' of bosonic Gaussian channels and can be applied to address some issues concerning transmission of information in continuous variables systems.
Phase statistics in non-Gaussian scattering
International Nuclear Information System (INIS)
Watson, Stephen M; Jakeman, Eric; Ridley, Kevin D
2006-01-01
Amplitude weighting can improve the accuracy of frequency measurements in signals corrupted by multiplicative speckle noise. When the speckle field constitutes a circular complex Gaussian process, the optimal function of amplitude weighting is provided by the field intensity, corresponding to the intensity-weighted phase derivative statistic. In this paper, we investigate the phase derivative and intensity-weighted phase derivative returned from a two-dimensional random walk, which constitutes a generic scattering model capable of producing both Gaussian and non-Gaussian fluctuations. Analytical results are developed for the correlation properties of the intensity-weighted phase derivative, as well as limiting probability densities of the scattered field. Numerical simulation is used to generate further probability densities and determine optimal weighting criteria from non-Gaussian fields. The results are relevant to frequency retrieval in radiation scattered from random media
Galaxy bias and primordial non-Gaussianity
Energy Technology Data Exchange (ETDEWEB)
Assassi, Valentin; Baumann, Daniel [DAMTP, Cambridge University, Wilberforce Road, Cambridge CB3 0WA (United Kingdom); Schmidt, Fabian, E-mail: assassi@ias.edu, E-mail: D.D.Baumann@uva.nl, E-mail: fabians@MPA-Garching.MPG.DE [Max-Planck-Institut für Astrophysik, Karl-Schwarzschild-Str. 1, 85748 Garching (Germany)
2015-12-01
We present a systematic study of galaxy biasing in the presence of primordial non-Gaussianity. For a large class of non-Gaussian initial conditions, we define a general bias expansion and prove that it is closed under renormalization, thereby showing that the basis of operators in the expansion is complete. We then study the effects of primordial non-Gaussianity on the statistics of galaxies. We show that the equivalence principle enforces a relation between the scale-dependent bias in the galaxy power spectrum and that in the dipolar part of the bispectrum. This provides a powerful consistency check to confirm the primordial origin of any observed scale-dependent bias. Finally, we also discuss the imprints of anisotropic non-Gaussianity as motivated by recent studies of higher-spin fields during inflation.
Optimal cloning of mixed Gaussian states
International Nuclear Information System (INIS)
Guta, Madalin; Matsumoto, Keiji
2006-01-01
We construct the optimal one to two cloning transformation for the family of displaced thermal equilibrium states of a harmonic oscillator, with a fixed and known temperature. The transformation is Gaussian and it is optimal with respect to the figure of merit based on the joint output state and norm distance. The proof of the result is based on the equivalence between the optimal cloning problem and that of optimal amplification of Gaussian states which is then reduced to an optimization problem for diagonal states of a quantum oscillator. A key concept in finding the optimum is that of stochastic ordering which plays a similar role in the purely classical problem of Gaussian cloning. The result is then extended to the case of n to m cloning of mixed Gaussian states
Encoding information using laguerre gaussian modes
CSIR Research Space (South Africa)
Trichili, A
2015-08-01
Full Text Available The authors experimentally demonstrate an information encoding protocol using the two degrees of freedom of Laguerre Gaussian modes having different radial and azimuthal components. A novel method, based on digital holography, for information...
Interweave Cognitive Radio with Improper Gaussian Signaling
Hedhly, Wafa; Amin, Osama; Alouini, Mohamed-Slim
2018-01-01
Improper Gaussian signaling (IGS) has proven its ability in improving the performance of underlay and overlay cognitive radio paradigms. In this paper, the interweave cognitive radio paradigm is studied when the cognitive user employs IGS
Galaxy bias and primordial non-Gaussianity
International Nuclear Information System (INIS)
Assassi, Valentin; Baumann, Daniel; Schmidt, Fabian
2015-01-01
We present a systematic study of galaxy biasing in the presence of primordial non-Gaussianity. For a large class of non-Gaussian initial conditions, we define a general bias expansion and prove that it is closed under renormalization, thereby showing that the basis of operators in the expansion is complete. We then study the effects of primordial non-Gaussianity on the statistics of galaxies. We show that the equivalence principle enforces a relation between the scale-dependent bias in the galaxy power spectrum and that in the dipolar part of the bispectrum. This provides a powerful consistency check to confirm the primordial origin of any observed scale-dependent bias. Finally, we also discuss the imprints of anisotropic non-Gaussianity as motivated by recent studies of higher-spin fields during inflation
Statistically tuned Gaussian background subtraction technique for ...
Indian Academy of Sciences (India)
temporal median method and mixture of Gaussian model and performance evaluation ... to process the videos captured by unmanned aerial vehicle (UAV). ..... The output is obtained by simulation using MATLAB 2010 in a standalone PC with ...
A non-Gaussian multivariate distribution with all lower-dimensional Gaussians and related families
Dutta, Subhajit
2014-07-28
Several fascinating examples of non-Gaussian bivariate distributions which have marginal distribution functions to be Gaussian have been proposed in the literature. These examples often clarify several properties associated with the normal distribution. In this paper, we generalize this result in the sense that we construct a pp-dimensional distribution for which any proper subset of its components has the Gaussian distribution. However, the jointpp-dimensional distribution is inconsistent with the distribution of these subsets because it is not Gaussian. We study the probabilistic properties of this non-Gaussian multivariate distribution in detail. Interestingly, several popular tests of multivariate normality fail to identify this pp-dimensional distribution as non-Gaussian. We further extend our construction to a class of elliptically contoured distributions as well as skewed distributions arising from selections, for instance the multivariate skew-normal distribution.
A non-Gaussian multivariate distribution with all lower-dimensional Gaussians and related families
Dutta, Subhajit; Genton, Marc G.
2014-01-01
Several fascinating examples of non-Gaussian bivariate distributions which have marginal distribution functions to be Gaussian have been proposed in the literature. These examples often clarify several properties associated with the normal distribution. In this paper, we generalize this result in the sense that we construct a pp-dimensional distribution for which any proper subset of its components has the Gaussian distribution. However, the jointpp-dimensional distribution is inconsistent with the distribution of these subsets because it is not Gaussian. We study the probabilistic properties of this non-Gaussian multivariate distribution in detail. Interestingly, several popular tests of multivariate normality fail to identify this pp-dimensional distribution as non-Gaussian. We further extend our construction to a class of elliptically contoured distributions as well as skewed distributions arising from selections, for instance the multivariate skew-normal distribution.
Gaussian sum rules for optical functions
International Nuclear Information System (INIS)
Kimel, I.
1981-12-01
A new (Gaussian) type of sum rules (GSR) for several optical functions, is presented. The functions considered are: dielectric permeability, refractive index, energy loss function, rotatory power and ellipticity (circular dichroism). While reducing to the usual type of sum rules in a certain limit, the GSR contain in general, a Gaussian factor that serves to improve convergence. GSR might be useful in analysing experimental data. (Author) [pt
Gaussian maximally multipartite-entangled states
Facchi, Paolo; Florio, Giuseppe; Lupo, Cosmo; Mancini, Stefano; Pascazio, Saverio
2009-12-01
We study maximally multipartite-entangled states in the context of Gaussian continuous variable quantum systems. By considering multimode Gaussian states with constrained energy, we show that perfect maximally multipartite-entangled states, which exhibit the maximum amount of bipartite entanglement for all bipartitions, only exist for systems containing n=2 or 3 modes. We further numerically investigate the structure of these states and their frustration for n≤7 .
Gaussian maximally multipartite-entangled states
International Nuclear Information System (INIS)
Facchi, Paolo; Florio, Giuseppe; Pascazio, Saverio; Lupo, Cosmo; Mancini, Stefano
2009-01-01
We study maximally multipartite-entangled states in the context of Gaussian continuous variable quantum systems. By considering multimode Gaussian states with constrained energy, we show that perfect maximally multipartite-entangled states, which exhibit the maximum amount of bipartite entanglement for all bipartitions, only exist for systems containing n=2 or 3 modes. We further numerically investigate the structure of these states and their frustration for n≤7.
Evaluation of Gaussian approximations for data assimilation in reservoir models
Iglesias, Marco A.
2013-07-14
The Bayesian framework is the standard approach for data assimilation in reservoir modeling. This framework involves characterizing the posterior distribution of geological parameters in terms of a given prior distribution and data from the reservoir dynamics, together with a forward model connecting the space of geological parameters to the data space. Since the posterior distribution quantifies the uncertainty in the geologic parameters of the reservoir, the characterization of the posterior is fundamental for the optimal management of reservoirs. Unfortunately, due to the large-scale highly nonlinear properties of standard reservoir models, characterizing the posterior is computationally prohibitive. Instead, more affordable ad hoc techniques, based on Gaussian approximations, are often used for characterizing the posterior distribution. Evaluating the performance of those Gaussian approximations is typically conducted by assessing their ability at reproducing the truth within the confidence interval provided by the ad hoc technique under consideration. This has the disadvantage of mixing up the approximation properties of the history matching algorithm employed with the information content of the particular observations used, making it hard to evaluate the effect of the ad hoc approximations alone. In this paper, we avoid this disadvantage by comparing the ad hoc techniques with a fully resolved state-of-the-art probing of the Bayesian posterior distribution. The ad hoc techniques whose performance we assess are based on (1) linearization around the maximum a posteriori estimate, (2) randomized maximum likelihood, and (3) ensemble Kalman filter-type methods. In order to fully resolve the posterior distribution, we implement a state-of-the art Markov chain Monte Carlo (MCMC) method that scales well with respect to the dimension of the parameter space, enabling us to study realistic forward models, in two space dimensions, at a high level of grid refinement. Our
Dispersant effectiveness: Studies into the causes of effectiveness variations
International Nuclear Information System (INIS)
Fingas, M.F.; Kyle, D.; Tennyson, E.
1995-01-01
Effectiveness, a key issue of using dispersants, is affected by many interrelated factors. The principal factors involved are the oil composition, dispersant formulation, sea surface turbulence and dispersant quantity. Oil composition is a very strong determinant. Current dispersant formulation effectiveness correlates strongly with the amount of saturate component in the oil. The other components of the oil, the asphaltenes, resins or polars and aromatic fractions show a negative correlation with the dispersant effectiveness. Viscosity is also a predictor of dispersant effectiveness and may have an effect because it is in turn determined by oil composition. Dispersant composition is significant and interacts with oil composition. Dispersants show high effectiveness at HLB values near 10. Sea turbulence strongly affects dispersant effectiveness.Effectiveness rises with increasing turbulence to a maximum value. Effectiveness for current commercial dispersants is gaussian around a peak salinity value. Peak effectiveness is achieved at very high dispersant quantities--at a ratio of 1:5, dispersant-to-oil volume. Dispersant effectiveness for those oils tested and under the conditions measured, is approximately logarithmic with dispersant quantity and will reach about 50% of its peak value at a dispersant to oil ratio of about 1:20 and near zero at a ratio of about 1:50
IMPROVED SIMULATION OF NON-GAUSSIAN TEMPERATURE AND POLARIZATION COSMIC MICROWAVE BACKGROUND MAPS
International Nuclear Information System (INIS)
Elsner, Franz; Wandelt, Benjamin D.
2009-01-01
We describe an algorithm to generate temperature and polarization maps of the cosmic microwave background (CMB) radiation containing non-Gaussianity of arbitrary local type. We apply an optimized quadrature scheme that allows us to predict and control integration accuracy, speed up the calculations, and reduce memory consumption by an order of magnitude. We generate 1000 non-Gaussian CMB temperature and polarization maps up to a multipole moment of l max = 1024. We validate the method and code using the power spectrum and the fast cubic (bispectrum) estimator and find consistent results. The simulations are provided to the community.
Non-Gaussian halo assembly bias
International Nuclear Information System (INIS)
Reid, Beth A.; Verde, Licia; Dolag, Klaus; Matarrese, Sabino; Moscardini, Lauro
2010-01-01
The strong dependence of the large-scale dark matter halo bias on the (local) non-Gaussianity parameter, f NL , offers a promising avenue towards constraining primordial non-Gaussianity with large-scale structure surveys. In this paper, we present the first detection of the dependence of the non-Gaussian halo bias on halo formation history using N-body simulations. We also present an analytic derivation of the expected signal based on the extended Press-Schechter formalism. In excellent agreement with our analytic prediction, we find that the halo formation history-dependent contribution to the non-Gaussian halo bias (which we call non-Gaussian halo assembly bias) can be factorized in a form approximately independent of redshift and halo mass. The correction to the non-Gaussian halo bias due to the halo formation history can be as large as 100%, with a suppression of the signal for recently formed halos and enhancement for old halos. This could in principle be a problem for realistic galaxy surveys if observational selection effects were to pick galaxies occupying only recently formed halos. Current semi-analytic galaxy formation models, for example, imply an enhancement in the expected signal of ∼ 23% and ∼ 48% for galaxies at z = 1 selected by stellar mass and star formation rate, respectively
Spectral dispersion and fringe detection in IOTA
Traub, W. A.; Lacasse, M. G.; Carleton, N. P.
1990-01-01
Pupil plane beam combination, spectral dispersion, detection, and fringe tracking are discussed for the IOTA interferometer. A new spectrometer design is presented in which the angular dispersion with respect to wavenumber is nearly constant. The dispersing element is a type of grism, a series combination of grating and prism, in which the constant parts of the dispersion add, but the slopes cancel. This grism is optimized for the display of channelled spectra. The dispersed fringes can be tracked by a matched-filter photon-counting correlator algorithm. This algorithm requires very few arithmetic operations per detected photon, making it well-suited for real-time fringe tracking. The algorithm is able to adapt to different stellar spectral types, intensity levels, and atmospheric time constants. The results of numerical experiments are reported.
Adaptive Laguerre-Gaussian variant of the Gaussian beam expansion method.
Cagniot, Emmanuel; Fromager, Michael; Ait-Ameur, Kamel
2009-11-01
A variant of the Gaussian beam expansion method consists in expanding the Bessel function J0 appearing in the Fresnel-Kirchhoff integral into a finite sum of complex Gaussian functions to derive an analytical expression for a Laguerre-Gaussian beam diffracted through a hard-edge aperture. However, the validity range of the approximation depends on the number of expansion coefficients that are obtained by optimization-computation directly. We propose another solution consisting in expanding J0 onto a set of collimated Laguerre-Gaussian functions whose waist depends on their number and then, depending on its argument, predicting the suitable number of expansion functions to calculate the integral recursively.
Enhanced Particle Swarm Optimization Algorithm: Efficient Training of ReaxFF Reactive Force Fields.
Furman, David; Carmeli, Benny; Zeiri, Yehuda; Kosloff, Ronnie
2018-05-04
Particle swarm optimization is a powerful metaheuristic population-based global optimization algorithm. However, when applied to non-separable objective functions its performance on multimodal landscapes is significantly degraded. Here we show that a significant improvement in the search quality and efficiency on multimodal functions can be achieved by enhancing the basic rotation-invariant particle swarm optimization algorithm with isotropic Gaussian mutation operators. The new algorithm demonstrates a superior performance across several nonlinear, multimodal benchmark functions compared to the rotation-invariant Particle Swam Optimization (PSO) algorithm and the well-established simulated annealing and sequential one-parameter parabolic interpolation methods. A search for the optimal set of parameters for the dispersion interaction model in ReaxFF-lg reactive force field is carried out with respect to accurate DFT-TS calculations. The resulting optimized force field accurately describes the equations of state of several high-energy molecular crystals where such interactions are of crucial importance. The improved algorithm also presents a better performance compared to a Genetic Algorithm optimization method in the optimization of a ReaxFF-lg correction model parameters. The computational framework is implemented in a standalone C++ code that allows a straightforward development of ReaxFF reactive force fields.
Krohling, Renato A; Coelho, Leandro dos Santos
2006-12-01
In this correspondence, an approach based on coevolutionary particle swarm optimization to solve constrained optimization problems formulated as min-max problems is presented. In standard or canonical particle swarm optimization (PSO), a uniform probability distribution is used to generate random numbers for the accelerating coefficients of the local and global terms. We propose a Gaussian probability distribution to generate the accelerating coefficients of PSO. Two populations of PSO using Gaussian distribution are used on the optimization algorithm that is tested on a suite of well-known benchmark constrained optimization problems. Results have been compared with the canonical PSO (constriction factor) and with a coevolutionary genetic algorithm. Simulation results show the suitability of the proposed algorithm in terms of effectiveness and robustness.
Sokolov, R. I.; Abdullin, R. R.
2017-11-01
The use of nonlinear Markov process filtering makes it possible to restore both video stream frames and static photos at the stage of preprocessing. The present paper reflects the results of research in comparison of these types image filtering quality by means of special algorithm when Gaussian or non-Gaussian noises acting. Examples of filter operation at different values of signal-to-noise ratio are presented. A comparative analysis has been performed, and the best filtered kind of noise has been defined. It has been shown the quality of developed algorithm is much better than quality of adaptive one for RGB signal filtering at the same a priori information about the signal. Also, an advantage over median filter takes a place when both fluctuation and pulse noise filtering.
A modified Gaussian model for the thermal plume from a ground-based heat source in a cross-wind
International Nuclear Information System (INIS)
Selander, W.N.; Barry, P.J.; Robertson, E.
1990-06-01
An array of propane burners operating at ground level in a cross-wind was used as a heat source to establish a blown-over thermal plume. A three-dimensional array of thermocouples was used to continuously measure the plume temperature downwind from the source. The resulting data were used to correlate the parameters of a modified Gaussian model for plume rise and dispersion with source strength, wind speed, and atmospheric dispersion parameters
The Gaussian streaming model and convolution Lagrangian effective field theory
Energy Technology Data Exchange (ETDEWEB)
Vlah, Zvonimir [Stanford Institute for Theoretical Physics and Department of Physics, Stanford University, Stanford, CA 94306 (United States); Castorina, Emanuele; White, Martin, E-mail: zvlah@stanford.edu, E-mail: ecastorina@berkeley.edu, E-mail: mwhite@berkeley.edu [Department of Physics, University of California, Berkeley, CA 94720 (United States)
2016-12-01
We update the ingredients of the Gaussian streaming model (GSM) for the redshift-space clustering of biased tracers using the techniques of Lagrangian perturbation theory, effective field theory (EFT) and a generalized Lagrangian bias expansion. After relating the GSM to the cumulant expansion, we present new results for the real-space correlation function, mean pairwise velocity and pairwise velocity dispersion including counter terms from EFT and bias terms through third order in the linear density, its leading derivatives and its shear up to second order. We discuss the connection to the Gaussian peaks formalism. We compare the ingredients of the GSM to a suite of large N-body simulations, and show the performance of the theory on the low order multipoles of the redshift-space correlation function and power spectrum. We highlight the importance of a general biasing scheme, which we find to be as important as higher-order corrections due to non-linear evolution for the halos we consider on the scales of interest to us.
Hamby, D M
2002-01-01
Reconstructed meteorological data are often used in some form of long-term wind trajectory models for estimating the historical impacts of atmospheric emissions. Meteorological data for the straight-line Gaussian plume model are put into a joint frequency distribution, a three-dimensional array describing atmospheric wind direction, speed, and stability. Methods using the Gaussian model and joint frequency distribution inputs provide reasonable estimates of downwind concentration and have been shown to be accurate to within a factor of four. We have used multiple joint frequency distributions and probabilistic techniques to assess the Gaussian plume model and determine concentration-estimate uncertainty and model sensitivity. We examine the straight-line Gaussian model while calculating both sector-averaged and annual-averaged relative concentrations at various downwind distances. The sector-average concentration model was found to be most sensitive to wind speed, followed by horizontal dispersion (sigmaZ), the importance of which increases as stability increases. The Gaussian model is not sensitive to stack height uncertainty. Precision of the frequency data appears to be most important to meteorological inputs when calculations are made for near-field receptors, increasing as stack height increases.
New gaussian points for the solution of first order ordinary ...
African Journals Online (AJOL)
Numerical experiments carried out using the new Gaussian points revealed there efficiency on stiff differential equations. The results also reveal that methods using the new Gaussian points are more accurate than those using the standard Gaussian points on non-stiff initial value problems. Keywords: Gaussian points ...
Accident consequence assessments with different atmospheric dispersion models
International Nuclear Information System (INIS)
Panitz, H.J.
1989-11-01
An essential aim of the improvements of the new program system UFOMOD for Accident Consequence Assessments (ACAs) was to substitute the straight-line Gaussian plume model conventionally used in ACA models by more realistic atmospheric dispersion models. To identify improved models which can be applied in ACA codes and to quantify the implications of different dispersion models on the results of an ACA, probabilistic comparative calculations with different atmospheric dispersion models have been performed. The study showed that there are trajectory models available which can be applied in ACAs and that they provide more realistic results of ACAs than straight-line Gaussian models. This led to a completely novel concept of atmospheric dispersion modelling in which two different distance ranges of validity are distinguished: the near range of some ten kilometres distance and the adjacent far range which are assigned to respective trajectory models. (orig.) [de
Discrete dispersion models and their Tweedie asymptotics
DEFF Research Database (Denmark)
Jørgensen, Bent; Kokonendji, Célestin C.
2016-01-01
The paper introduce a class of two-parameter discrete dispersion models, obtained by combining convolution with a factorial tilting operation, similar to exponential dispersion models which combine convolution and exponential tilting. The equidispersed Poisson model has a special place in this ap......The paper introduce a class of two-parameter discrete dispersion models, obtained by combining convolution with a factorial tilting operation, similar to exponential dispersion models which combine convolution and exponential tilting. The equidispersed Poisson model has a special place...... in this approach, whereas several overdispersed discrete distributions, such as the Neyman Type A, Pólya-Aeppli, negative binomial and Poisson-inverse Gaussian, turn out to be Poisson-Tweedie factorial dispersion models with power dispersion functions, analogous to ordinary Tweedie exponential dispersion models...... with power variance functions. Using the factorial cumulant generating function as tool, we introduce a dilation operation as a discrete analogue of scaling, generalizing binomial thinning. The Poisson-Tweedie factorial dispersion models are closed under dilation, which in turn leads to a Poisson...
A Network of Kalman Filters for MAI and ISI Compensation in a Non-Gaussian Environment
Directory of Open Access Journals (Sweden)
Sayadi Bessem
2005-01-01
Full Text Available This paper develops a new multiuser detector based on a network of kalman filters (NKF dealing with multiple access-interference (MAI, intersymbol Interference (ISI, and an impulsive observation noise. The two proposed schemes are based on the modeling of the DS-CDMA system by a discrete-time linear system that has non-Gaussian state and measurement noises. By approximating the non-Gaussian densities of the noises by a weighted sum of Gaussian terms and under the common MMSE estimation criterion, we first derive an NKF detector. This version is further optimized by introducing a feedback exploiting the ISI interference structure. The resulting scheme is an NKF detector based on a likelihood ratio test (LRT. Monte-Carlo simulations have shown that the NKF and the NKF based on LRT detectors significantly improve the efficiency and the performance of the classical Kalman algorithm.
Underlay Cognitive Radio Systems with Improper Gaussian Signaling: Outage Performance Analysis
Amin, Osama
2016-03-29
Improper Gaussian signaling has the ability over proper (conventional) Gaussian signaling to improve the achievable rate of systems that suffer from interference. In this paper, we study the impact of using improper Gaussian signaling on the performance limits of the underlay cognitive radio system by analyzing the achievable outage probability of both the primary user (PU) and secondary user (SU). We derive the exact outage probability expression of the SU and construct upper and lower bounds of the PU outage probability which results in formulating an approximate expression of the PU outage probability. This allows us to design the SU signal by adjusting its transmitted power and the circularity coefficient to minimize the SU outage probability while maintaining a certain PU quality-of-service. Finally, we evaluate the derived expressions for both the SU and the PU and the corresponding adaptive algorithms by numerical results.
Chen, Zhaoxue; Chen, Hao
2014-01-01
A deconvolution method based on the Gaussian radial basis function (GRBF) interpolation is proposed. Both the original image and Gaussian point spread function are expressed as the same continuous GRBF model, thus image degradation is simplified as convolution of two continuous Gaussian functions, and image deconvolution is converted to calculate the weighted coefficients of two-dimensional control points. Compared with Wiener filter and Lucy-Richardson algorithm, the GRBF method has an obvious advantage in the quality of restored images. In order to overcome such a defect of long-time computing, the method of graphic processing unit multithreading or increasing space interval of control points is adopted, respectively, to speed up the implementation of GRBF method. The experiments show that based on the continuous GRBF model, the image deconvolution can be efficiently implemented by the method, which also has a considerable reference value for the study of three-dimensional microscopic image deconvolution.
Underlay Cognitive Radio Systems with Improper Gaussian Signaling: Outage Performance Analysis
Amin, Osama; Abediseid, Walid; Alouini, Mohamed-Slim
2016-01-01
Improper Gaussian signaling has the ability over proper (conventional) Gaussian signaling to improve the achievable rate of systems that suffer from interference. In this paper, we study the impact of using improper Gaussian signaling on the performance limits of the underlay cognitive radio system by analyzing the achievable outage probability of both the primary user (PU) and secondary user (SU). We derive the exact outage probability expression of the SU and construct upper and lower bounds of the PU outage probability which results in formulating an approximate expression of the PU outage probability. This allows us to design the SU signal by adjusting its transmitted power and the circularity coefficient to minimize the SU outage probability while maintaining a certain PU quality-of-service. Finally, we evaluate the derived expressions for both the SU and the PU and the corresponding adaptive algorithms by numerical results.
Amalia, Junita; Purhadi, Otok, Bambang Widjanarko
2017-11-01
Poisson distribution is a discrete distribution with count data as the random variables and it has one parameter defines both mean and variance. Poisson regression assumes mean and variance should be same (equidispersion). Nonetheless, some case of the count data unsatisfied this assumption because variance exceeds mean (over-dispersion). The ignorance of over-dispersion causes underestimates in standard error. Furthermore, it causes incorrect decision in the statistical test. Previously, paired count data has a correlation and it has bivariate Poisson distribution. If there is over-dispersion, modeling paired count data is not sufficient with simple bivariate Poisson regression. Bivariate Poisson Inverse Gaussian Regression (BPIGR) model is mix Poisson regression for modeling paired count data within over-dispersion. BPIGR model produces a global model for all locations. In another hand, each location has different geographic conditions, social, cultural and economic so that Geographically Weighted Regression (GWR) is needed. The weighting function of each location in GWR generates a different local model. Geographically Weighted Bivariate Poisson Inverse Gaussian Regression (GWBPIGR) model is used to solve over-dispersion and to generate local models. Parameter estimation of GWBPIGR model obtained by Maximum Likelihood Estimation (MLE) method. Meanwhile, hypothesis testing of GWBPIGR model acquired by Maximum Likelihood Ratio Test (MLRT) method.
DEFF Research Database (Denmark)
Bennedsen, Mikkel
Using theory on (conditionally) Gaussian processes with stationary increments developed in Barndorff-Nielsen et al. (2009, 2011), this paper presents a general semiparametric approach to conducting inference on the fractal index, α, of a time series. Our setup encompasses a large class of Gaussian...
Rajabi, Mohammad Mahdi; Ketabchi, Hamed
2017-12-01
Combined simulation-optimization (S/O) schemes have long been recognized as a valuable tool in coastal groundwater management (CGM). However, previous applications have mostly relied on deterministic seawater intrusion (SWI) simulations. This is a questionable simplification, knowing that SWI models are inevitably prone to epistemic and aleatory uncertainty, and hence a management strategy obtained through S/O without consideration of uncertainty may result in significantly different real-world outcomes than expected. However, two key issues have hindered the use of uncertainty-based S/O schemes in CGM, which are addressed in this paper. The first issue is how to solve the computational challenges resulting from the need to perform massive numbers of simulations. The second issue is how the management problem is formulated in presence of uncertainty. We propose the use of Gaussian process (GP) emulation as a valuable tool in solving the computational challenges of uncertainty-based S/O in CGM. We apply GP emulation to the case study of Kish Island (located in the Persian Gulf) using an uncertainty-based S/O algorithm which relies on continuous ant colony optimization and Monte Carlo simulation. In doing so, we show that GP emulation can provide an acceptable level of accuracy, with no bias and low statistical dispersion, while tremendously reducing the computational time. Moreover, five new formulations for uncertainty-based S/O are presented based on concepts such as energy distances, prediction intervals and probabilities of SWI occurrence. We analyze the proposed formulations with respect to their resulting optimized solutions, the sensitivity of the solutions to the intended reliability levels, and the variations resulting from repeated optimization runs.
Relationship between the complex susceptibility and the plasma dispersion function
Energy Technology Data Exchange (ETDEWEB)
Jimenez D, H.; Cabral P, A
1991-04-15
It is shown that when magnetization processes in a spin system and resonant excitation of spin n states occur in the presence of internal and or external random line-broadening mechanisms, the complex magnetic susceptibility of the plasma dispersion function. letter could be useful spin in system is proportional to the relationship found in this spectroscopies such as EPR and NMR, for example, as its fitting to experimental absorption and dispersion profiles produces their Lorentzian and Gaussian contents. (Author)
Relationship between the complex susceptibility and the plasma dispersion function
International Nuclear Information System (INIS)
Jimenez D, H.; Cabral P, A.
1991-04-01
It is shown that when magnetization processes in a spin system and resonant excitation of spin n states occur in the presence of internal and or external random line-broadening mechanisms, the complex magnetic susceptibility of the plasma dispersion function. letter could be useful spin in system is proportional to the relationship found in this spectroscopies such as EPR and NMR, for example, as its fitting to experimental absorption and dispersion profiles produces their Lorentzian and Gaussian contents. (Author)
Graphical calculus for Gaussian pure states
International Nuclear Information System (INIS)
Menicucci, Nicolas C.; Flammia, Steven T.; Loock, Peter van
2011-01-01
We provide a unified graphical calculus for all Gaussian pure states, including graph transformation rules for all local and semilocal Gaussian unitary operations, as well as local quadrature measurements. We then use this graphical calculus to analyze continuous-variable (CV) cluster states, the essential resource for one-way quantum computing with CV systems. Current graphical approaches to CV cluster states are only valid in the unphysical limit of infinite squeezing, and the associated graph transformation rules only apply when the initial and final states are of this form. Our formalism applies to all Gaussian pure states and subsumes these rules in a natural way. In addition, the term 'CV graph state' currently has several inequivalent definitions in use. Using this formalism we provide a single unifying definition that encompasses all of them. We provide many examples of how the formalism may be used in the context of CV cluster states: defining the 'closest' CV cluster state to a given Gaussian pure state and quantifying the error in the approximation due to finite squeezing; analyzing the optimality of certain methods of generating CV cluster states; drawing connections between this graphical formalism and bosonic Hamiltonians with Gaussian ground states, including those useful for CV one-way quantum computing; and deriving a graphical measure of bipartite entanglement for certain classes of CV cluster states. We mention other possible applications of this formalism and conclude with a brief note on fault tolerance in CV one-way quantum computing.
Mode entanglement of Gaussian fermionic states
Spee, C.; Schwaiger, K.; Giedke, G.; Kraus, B.
2018-04-01
We investigate the entanglement of n -mode n -partite Gaussian fermionic states (GFS). First, we identify a reasonable definition of separability for GFS and derive a standard form for mixed states, to which any state can be mapped via Gaussian local unitaries (GLU). As the standard form is unique, two GFS are equivalent under GLU if and only if their standard forms coincide. Then, we investigate the important class of local operations assisted by classical communication (LOCC). These are central in entanglement theory as they allow one to partially order the entanglement contained in states. We show, however, that there are no nontrivial Gaussian LOCC (GLOCC) among pure n -partite (fully entangled) states. That is, any such GLOCC transformation can also be accomplished via GLU. To obtain further insight into the entanglement properties of such GFS, we investigate the richer class of Gaussian stochastic local operations assisted by classical communication (SLOCC). We characterize Gaussian SLOCC classes of pure n -mode n -partite states and derive them explicitly for few-mode states. Furthermore, we consider certain fermionic LOCC and show how to identify the maximally entangled set of pure n -mode n -partite GFS, i.e., the minimal set of states having the property that any other state can be obtained from one state inside this set via fermionic LOCC. We generalize these findings also to the pure m -mode n -partite (for m >n ) case.
Frozen Gaussian approximation for 3D seismic tomography
Chai, Lihui; Tong, Ping; Yang, Xu
2018-05-01
Three-dimensional (3D) wave-equation-based seismic tomography is computationally challenging in large scales and high-frequency regime. In this paper, we apply the frozen Gaussian approximation (FGA) method to compute 3D sensitivity kernels and seismic tomography of high-frequency. Rather than standard ray theory used in seismic inversion (e.g. Kirchhoff migration and Gaussian beam migration), FGA is used to compute the 3D high-frequency sensitivity kernels for travel-time or full waveform inversions. Specifically, we reformulate the equations of the forward and adjoint wavefields for the purpose of convenience to apply FGA, and with this reformulation, one can efficiently compute the Green’s functions whose convolutions with source time function produce wavefields needed for the construction of 3D kernels. Moreover, a fast summation method is proposed based on local fast Fourier transform which greatly improves the speed of reconstruction as the last step of FGA algorithm. We apply FGA to both the travel-time adjoint tomography and full waveform inversion (FWI) on synthetic crosswell seismic data with dominant frequencies as high as those of real crosswell data, and confirm again that FWI requires a more sophisticated initial velocity model for the convergence than travel-time adjoint tomography. We also numerically test the accuracy of applying FGA to local earthquake tomography. This study paves the way to directly apply wave-equation-based seismic tomography methods into real data around their dominant frequencies.
Probabilistic wind power forecasting with online model selection and warped gaussian process
International Nuclear Information System (INIS)
Kou, Peng; Liang, Deliang; Gao, Feng; Gao, Lin
2014-01-01
Highlights: • A new online ensemble model for the probabilistic wind power forecasting. • Quantifying the non-Gaussian uncertainties in wind power. • Online model selection that tracks the time-varying characteristic of wind generation. • Dynamically altering the input features. • Recursive update of base models. - Abstract: Based on the online model selection and the warped Gaussian process (WGP), this paper presents an ensemble model for the probabilistic wind power forecasting. This model provides the non-Gaussian predictive distributions, which quantify the non-Gaussian uncertainties associated with wind power. In order to follow the time-varying characteristics of wind generation, multiple time dependent base forecasting models and an online model selection strategy are established, thus adaptively selecting the most probable base model for each prediction. WGP is employed as the base model, which handles the non-Gaussian uncertainties in wind power series. Furthermore, a regime switch strategy is designed to modify the input feature set dynamically, thereby enhancing the adaptiveness of the model. In an online learning framework, the base models should also be time adaptive. To achieve this, a recursive algorithm is introduced, thus permitting the online updating of WGP base models. The proposed model has been tested on the actual data collected from both single and aggregated wind farms
Methods for calculating population dose from atmospheric dispersion of radioactivity
Energy Technology Data Exchange (ETDEWEB)
Cohen, B L; Jow, H N; Lee, I S [Pittsburgh Univ., PA (USA)
1978-06-01
Curves are computed from which population dose (man-rem) due to dispersal of radioactivity from a point source can be calculated in the gaussian plume model by simple multiplication, and methods of using them and their limitations are considered. Illustrative examples are presented.
Non-Gaussianity in island cosmology
International Nuclear Information System (INIS)
Piao Yunsong
2009-01-01
In this paper we fully calculate the non-Gaussianity of primordial curvature perturbation of the island universe by using the second order perturbation equation. We find that for the spectral index n s ≅0.96, which is favored by current observations, the non-Gaussianity level f NL seen in an island will generally lie between 30 and 60, which may be tested by the coming observations. In the landscape, the island universe is one of anthropically acceptable cosmological histories. Thus the results obtained in some sense mean the coming observations, especially the measurement of non-Gaussianity, will be significant to clarify how our position in the landscape is populated.
Entanglement negativity bounds for fermionic Gaussian states
Eisert, Jens; Eisler, Viktor; Zimborás, Zoltán
2018-04-01
The entanglement negativity is a versatile measure of entanglement that has numerous applications in quantum information and in condensed matter theory. It can not only efficiently be computed in the Hilbert space dimension, but for noninteracting bosonic systems, one can compute the negativity efficiently in the number of modes. However, such an efficient computation does not carry over to the fermionic realm, the ultimate reason for this being that the partial transpose of a fermionic Gaussian state is no longer Gaussian. To provide a remedy for this state of affairs, in this work, we introduce efficiently computable and rigorous upper and lower bounds to the negativity, making use of techniques of semidefinite programming, building upon the Lagrangian formulation of fermionic linear optics, and exploiting suitable products of Gaussian operators. We discuss examples in quantum many-body theory and hint at applications in the study of topological properties at finite temperature.
Invariant measures on multimode quantum Gaussian states
Lupo, C.; Mancini, S.; De Pasquale, A.; Facchi, P.; Florio, G.; Pascazio, S.
2012-12-01
We derive the invariant measure on the manifold of multimode quantum Gaussian states, induced by the Haar measure on the group of Gaussian unitary transformations. To this end, by introducing a bipartition of the system in two disjoint subsystems, we use a parameterization highlighting the role of nonlocal degrees of freedom—the symplectic eigenvalues—which characterize quantum entanglement across the given bipartition. A finite measure is then obtained by imposing a physically motivated energy constraint. By averaging over the local degrees of freedom we finally derive the invariant distribution of the symplectic eigenvalues in some cases of particular interest for applications in quantum optics and quantum information.
Invariant measures on multimode quantum Gaussian states
International Nuclear Information System (INIS)
Lupo, C.; Mancini, S.; De Pasquale, A.; Facchi, P.; Florio, G.; Pascazio, S.
2012-01-01
We derive the invariant measure on the manifold of multimode quantum Gaussian states, induced by the Haar measure on the group of Gaussian unitary transformations. To this end, by introducing a bipartition of the system in two disjoint subsystems, we use a parameterization highlighting the role of nonlocal degrees of freedom—the symplectic eigenvalues—which characterize quantum entanglement across the given bipartition. A finite measure is then obtained by imposing a physically motivated energy constraint. By averaging over the local degrees of freedom we finally derive the invariant distribution of the symplectic eigenvalues in some cases of particular interest for applications in quantum optics and quantum information.
Invariant measures on multimode quantum Gaussian states
Energy Technology Data Exchange (ETDEWEB)
Lupo, C. [School of Science and Technology, Universita di Camerino, I-62032 Camerino (Italy); Mancini, S. [School of Science and Technology, Universita di Camerino, I-62032 Camerino (Italy); Istituto Nazionale di Fisica Nucleare, Sezione di Perugia, I-06123 Perugia (Italy); De Pasquale, A. [NEST, Scuola Normale Superiore and Istituto Nanoscienze-CNR, I-56126 Pisa (Italy); Facchi, P. [Dipartimento di Matematica and MECENAS, Universita di Bari, I-70125 Bari (Italy); Istituto Nazionale di Fisica Nucleare, Sezione di Bari, I-70126 Bari (Italy); Florio, G. [Istituto Nazionale di Fisica Nucleare, Sezione di Bari, I-70126 Bari (Italy); Museo Storico della Fisica e Centro Studi e Ricerche Enrico Fermi, Piazza del Viminale 1, I-00184 Roma (Italy); Dipartimento di Fisica and MECENAS, Universita di Bari, I-70126 Bari (Italy); Pascazio, S. [Istituto Nazionale di Fisica Nucleare, Sezione di Bari, I-70126 Bari (Italy); Dipartimento di Fisica and MECENAS, Universita di Bari, I-70126 Bari (Italy)
2012-12-15
We derive the invariant measure on the manifold of multimode quantum Gaussian states, induced by the Haar measure on the group of Gaussian unitary transformations. To this end, by introducing a bipartition of the system in two disjoint subsystems, we use a parameterization highlighting the role of nonlocal degrees of freedom-the symplectic eigenvalues-which characterize quantum entanglement across the given bipartition. A finite measure is then obtained by imposing a physically motivated energy constraint. By averaging over the local degrees of freedom we finally derive the invariant distribution of the symplectic eigenvalues in some cases of particular interest for applications in quantum optics and quantum information.
Construction of Capacity Achieving Lattice Gaussian Codes
Alghamdi, Wael
2016-04-01
We propose a new approach to proving results regarding channel coding schemes based on construction-A lattices for the Additive White Gaussian Noise (AWGN) channel that yields new characterizations of the code construction parameters, i.e., the primes and dimensions of the codes, as functions of the block-length. The approach we take introduces an averaging argument that explicitly involves the considered parameters. This averaging argument is applied to a generalized Loeliger ensemble [1] to provide a more practical proof of the existence of AWGN-good lattices, and to characterize suitable parameters for the lattice Gaussian coding scheme proposed by Ling and Belfiore [3].
Gaussian processes and constructive scalar field theory
International Nuclear Information System (INIS)
Benfatto, G.; Nicolo, F.
1981-01-01
The last years have seen a very deep progress of constructive euclidean field theory, with many implications in the area of the random fields theory. The authors discuss an approach to super-renormalizable scalar field theories, which puts in particular evidence the connections with the theory of the Gaussian processes associated to the elliptic operators. The paper consists of two parts. Part I treats some problems in the theory of Gaussian processes which arise in the approach to the PHI 3 4 theory. Part II is devoted to the discussion of the ultraviolet stability in the PHI 3 4 theory. (Auth.)
Integration of non-Gaussian fields
DEFF Research Database (Denmark)
Ditlevsen, Ove Dalager; Mohr, Gunnar; Hoffmeyer, Pernille
1996-01-01
The limitations of the validity of the central limit theorem argument as applied to definite integrals of non-Gaussian random fields are empirically explored by way of examples. The purpose is to investigate in specific cases whether the asymptotic convergence to the Gaussian distribution is fast....... and Randrup-Thomsen, S. Reliability of silo ring under lognormal stochastic pressure using stochastic interpolation. Proc. IUTAM Symp., Probabilistic Structural Mechanics: Advances in Structural Reliability Methods, San Antonio, TX, USA, June 1993 (eds.: P. D. Spanos & Y.-T. Wu) pp. 134-162. Springer, Berlin...
Quantum information theory with Gaussian systems
Energy Technology Data Exchange (ETDEWEB)
Krueger, O.
2006-04-06
This thesis applies ideas and concepts from quantum information theory to systems of continuous-variables such as the quantum harmonic oscillator. The focus is on three topics: the cloning of coherent states, Gaussian quantum cellular automata and Gaussian private channels. Cloning was investigated both for finite-dimensional and for continuous-variable systems. We construct a private quantum channel for the sequential encryption of coherent states with a classical key, where the key elements have finite precision. For the case of independent one-mode input states, we explicitly estimate this precision, i.e. the number of key bits needed per input state, in terms of these parameters. (orig.)
Quantum information theory with Gaussian systems
International Nuclear Information System (INIS)
Krueger, O.
2006-01-01
This thesis applies ideas and concepts from quantum information theory to systems of continuous-variables such as the quantum harmonic oscillator. The focus is on three topics: the cloning of coherent states, Gaussian quantum cellular automata and Gaussian private channels. Cloning was investigated both for finite-dimensional and for continuous-variable systems. We construct a private quantum channel for the sequential encryption of coherent states with a classical key, where the key elements have finite precision. For the case of independent one-mode input states, we explicitly estimate this precision, i.e. the number of key bits needed per input state, in terms of these parameters. (orig.)
Model selection for Gaussian kernel PCA denoising
DEFF Research Database (Denmark)
Jørgensen, Kasper Winther; Hansen, Lars Kai
2012-01-01
We propose kernel Parallel Analysis (kPA) for automatic kernel scale and model order selection in Gaussian kernel PCA. Parallel Analysis [1] is based on a permutation test for covariance and has previously been applied for model order selection in linear PCA, we here augment the procedure to also...... tune the Gaussian kernel scale of radial basis function based kernel PCA.We evaluate kPA for denoising of simulated data and the US Postal data set of handwritten digits. We find that kPA outperforms other heuristics to choose the model order and kernel scale in terms of signal-to-noise ratio (SNR...
Gaussian Multiscale Aggregation Applied to Segmentation in Hand Biometrics
Directory of Open Access Journals (Sweden)
Gonzalo Bailador del Pozo
2011-11-01
Full Text Available This paper presents an image segmentation algorithm based on Gaussian multiscale aggregation oriented to hand biometric applications. The method is able to isolate the hand from a wide variety of background textures such as carpets, fabric, glass, grass, soil or stones. The evaluation was carried out by using a publicly available synthetic database with 408,000 hand images in different backgrounds, comparing the performance in terms of accuracy and computational cost to two competitive segmentation methods existing in literature, namely Lossy Data Compression (LDC and Normalized Cuts (NCuts. The results highlight that the proposed method outperforms current competitive segmentation methods with regard to computational cost, time performance, accuracy and memory usage.
SNR Estimation in Linear Systems with Gaussian Matrices
Suliman, Mohamed Abdalla Elhag; Alrashdi, Ayed; Ballal, Tarig; Al-Naffouri, Tareq Y.
2017-01-01
This letter proposes a highly accurate algorithm to estimate the signal-to-noise ratio (SNR) for a linear system from a single realization of the received signal. We assume that the linear system has a Gaussian matrix with one sided left correlation. The unknown entries of the signal and the noise are assumed to be independent and identically distributed with zero mean and can be drawn from any distribution. We use the ridge regression function of this linear model in company with tools and techniques adapted from random matrix theory to achieve, in closed form, accurate estimation of the SNR without prior statistical knowledge on the signal or the noise. Simulation results show that the proposed method is very accurate.
Generating Correlated QPSK Waveforms By Exploiting Real Gaussian Random Variables
Jardak, Seifallah
2012-11-01
The design of waveforms with specified auto- and cross-correlation properties has a number of applications in multiple-input multiple-output (MIMO) radar, one of them is the desired transmit beampattern design. In this work, an algorithm is proposed to generate quadrature phase shift- keying (QPSK) waveforms with required cross-correlation properties using real Gaussian random-variables (RV’s). This work can be considered as the extension of what was presented in [1] to generate BPSK waveforms. This work will be extended for the generation of correlated higher-order phase shift-keying (PSK) and quadrature amplitude modulation (QAM) schemes that can better approximate the desired beampattern.
SNR Estimation in Linear Systems with Gaussian Matrices
Suliman, Mohamed Abdalla Elhag
2017-09-27
This letter proposes a highly accurate algorithm to estimate the signal-to-noise ratio (SNR) for a linear system from a single realization of the received signal. We assume that the linear system has a Gaussian matrix with one sided left correlation. The unknown entries of the signal and the noise are assumed to be independent and identically distributed with zero mean and can be drawn from any distribution. We use the ridge regression function of this linear model in company with tools and techniques adapted from random matrix theory to achieve, in closed form, accurate estimation of the SNR without prior statistical knowledge on the signal or the noise. Simulation results show that the proposed method is very accurate.
Inferring time derivatives including cell growth rates using Gaussian processes
Swain, Peter S.; Stevenson, Keiran; Leary, Allen; Montano-Gutierrez, Luis F.; Clark, Ivan B. N.; Vogel, Jackie; Pilizota, Teuta
2016-12-01
Often the time derivative of a measured variable is of as much interest as the variable itself. For a growing population of biological cells, for example, the population's growth rate is typically more important than its size. Here we introduce a non-parametric method to infer first and second time derivatives as a function of time from time-series data. Our approach is based on Gaussian processes and applies to a wide range of data. In tests, the method is at least as accurate as others, but has several advantages: it estimates errors both in the inference and in any summary statistics, such as lag times, and allows interpolation with the corresponding error estimation. As illustrations, we infer growth rates of microbial cells, the rate of assembly of an amyloid fibril and both the speed and acceleration of two separating spindle pole bodies. Our algorithm should thus be broadly applicable.
Generating Correlated QPSK Waveforms By Exploiting Real Gaussian Random Variables
Jardak, Seifallah; Ahmed, Sajid; Alouini, Mohamed-Slim
2012-01-01
The design of waveforms with specified auto- and cross-correlation properties has a number of applications in multiple-input multiple-output (MIMO) radar, one of them is the desired transmit beampattern design. In this work, an algorithm is proposed to generate quadrature phase shift- keying (QPSK) waveforms with required cross-correlation properties using real Gaussian random-variables (RV’s). This work can be considered as the extension of what was presented in [1] to generate BPSK waveforms. This work will be extended for the generation of correlated higher-order phase shift-keying (PSK) and quadrature amplitude modulation (QAM) schemes that can better approximate the desired beampattern.
International Nuclear Information System (INIS)
Jimenez-Dominguez, H.; Flores-Llamas, H.; Cabral-Prieto, C.; Bravo-Ortega, A.
1989-01-01
A relationship is presented between the Lorentzian-Gaussian profile convolutions and the plasma dispersion function; thus, the methods available to calculate the latter serve also to calculate the Voigt profile. (orig.)
International Nuclear Information System (INIS)
Jimenez D, H.; Flores L, H.; Cabral P, A.; Bravo O, A.
1990-05-01
A relationship among the Lorentzian-Gaussian profile convolution and the Plasma Dispersion Function is presented; thus, the methods available to calculate the latter serve also to calculate the Voigt profile. (Author)
International Nuclear Information System (INIS)
Scattergood, R.O.; Das, E.S.P.
1976-01-01
Using digital computer-based methods, models for dispersion strengthening can now be developed which take into account many of the important effects that have been neglected in the past. In particular, the self interaction of a dislocation can be treated, and a computer simulation method was developed to determine the flow stress of a random distribution of circular, impenetrable obstacles, taking into account all such interactions. The flow stress values depended on the obstacle sizes and spacings, over and above the usual 1/L dependence where L is the average obstacle spacing. From an analysis of the results, it was found that the main effects of the self interactions can be captured in a line tension analogue in which the obstacles appear to be penetrable
International Nuclear Information System (INIS)
Ji, Se-Wan; Nha, Hyunchul; Kim, M S
2015-01-01
It is a topic of fundamental and practical importance how a quantum correlated state can be reliably distributed through a noisy channel for quantum information processing. The concept of quantum steering recently defined in a rigorous manner is relevant to study it under certain circumstances and here we address quantum steerability of Gaussian states to this aim. In particular, we attempt to reformulate the criterion for Gaussian steering in terms of local and global purities and show that it is sufficient and necessary for the case of steering a 1-mode system by an N-mode system. It subsequently enables us to reinforce a strong monogamy relation under which only one party can steer a local system of 1-mode. Moreover, we show that only a negative partial-transpose state can manifest quantum steerability by Gaussian measurements in relation to the Peres conjecture. We also discuss our formulation for the case of distributing a two-mode squeezed state via one-way quantum channels making dissipation and amplification effects, respectively. Finally, we extend our approach to include non-Gaussian measurements, more precisely, all orders of higher-order squeezing measurements, and find that this broad set of non-Gaussian measurements is not useful to demonstrate steering for Gaussian states beyond Gaussian measurements. (paper)
Multimodal Estimation of Distribution Algorithms.
Yang, Qiang; Chen, Wei-Neng; Li, Yun; Chen, C L Philip; Xu, Xiang-Min; Zhang, Jun
2016-02-15
Taking the advantage of estimation of distribution algorithms (EDAs) in preserving high diversity, this paper proposes a multimodal EDA. Integrated with clustering strategies for crowding and speciation, two versions of this algorithm are developed, which operate at the niche level. Then these two algorithms are equipped with three distinctive techniques: 1) a dynamic cluster sizing strategy; 2) an alternative utilization of Gaussian and Cauchy distributions to generate offspring; and 3) an adaptive local search. The dynamic cluster sizing affords a potential balance between exploration and exploitation and reduces the sensitivity to the cluster size in the niching methods. Taking advantages of Gaussian and Cauchy distributions, we generate the offspring at the niche level through alternatively using these two distributions. Such utilization can also potentially offer a balance between exploration and exploitation. Further, solution accuracy is enhanced through a new local search scheme probabilistically conducted around seeds of niches with probabilities determined self-adaptively according to fitness values of these seeds. Extensive experiments conducted on 20 benchmark multimodal problems confirm that both algorithms can achieve competitive performance compared with several state-of-the-art multimodal algorithms, which is supported by nonparametric tests. Especially, the proposed algorithms are very promising for complex problems with many local optima.
Assessment of the GPC Control Quality Using Non–Gaussian Statistical Measures
Directory of Open Access Journals (Sweden)
Domański Paweł D.
2017-06-01
Full Text Available This paper presents an alternative approach to the task of control performance assessment. Various statistical measures based on Gaussian and non-Gaussian distribution functions are evaluated. The analysis starts with the review of control error histograms followed by their statistical analysis using probability distribution functions. Simulation results obtained for a control system with the generalized predictive controller algorithm are considered. The proposed approach using Cauchy and Lévy α-stable distributions shows robustness against disturbances and enables effective control loop quality evaluation. Tests of the predictive algorithm prove its ability to detect the impact of the main controller parameters, such as the model gain, the dynamics or the prediction horizon.
How Gaussian can our Universe be?
Cabass, G.; Pajer, E.; Schmidt, F.
2017-01-01
Gravity is a non-linear theory, and hence, barring cancellations, the initial super-horizon perturbations produced by inflation must contain some minimum amount of mode coupling, or primordial non-Gaussianity. In single-field slow-roll models, where this lower bound is saturated, non-Gaussianity is controlled by two observables: the tensor-to-scalar ratio, which is uncertain by more than fifty orders of magnitude; and the scalar spectral index, or tilt, which is relatively well measured. It is well known that to leading and next-to-leading order in derivatives, the contributions proportional to the tilt disappear from any local observable, and suspicion has been raised that this might happen to all orders, allowing for an arbitrarily low amount of primordial non-Gaussianity. Employing Conformal Fermi Coordinates, we show explicitly that this is not the case. Instead, a contribution of order the tilt appears in local observables. In summary, the floor of physical primordial non-Gaussianity in our Universe has a squeezed-limit scaling of kl2/ks2, similar to equilateral and orthogonal shapes, and a dimensionless amplitude of order 0.1 × (ns-1).
Gaussian vector fields on triangulated surfaces
DEFF Research Database (Denmark)
Ipsen, John H
2016-01-01
proven to be very useful to resolve the complex interplay between in-plane ordering of membranes and membrane conformations. In the present work we have developed a procedure for realistic representations of Gaussian models with in-plane vector degrees of freedoms on a triangulated surface. The method...
The Wehrl entropy has Gaussian optimizers
DEFF Research Database (Denmark)
De Palma, Giacomo
2018-01-01
We determine the minimum Wehrl entropy among the quantum states with a given von Neumann entropy and prove that it is achieved by thermal Gaussian states. This result determines the relation between the von Neumann and the Wehrl entropies. The key idea is proving that the quantum-classical channel...
How Gaussian can our Universe be?
Energy Technology Data Exchange (ETDEWEB)
Cabass, G. [Physics Department and INFN, Università di Roma ' ' La Sapienza' ' , P.le Aldo Moro 2, 00185, Rome (Italy); Pajer, E. [Institute for Theoretical Physics and Center for Extreme Matter and Emergent Phenomena, Utrecht University, Princetonplein 5, 3584 CC Utrecht (Netherlands); Schmidt, F., E-mail: giovanni.cabass@roma1.infn.it, E-mail: e.pajer@uu.nl, E-mail: fabians@mpa-garching.mpg.de [Max-Planck-Institut für Astrophysik, Karl-Schwarzschild-Str. 1, 85741 Garching (Germany)
2017-01-01
Gravity is a non-linear theory, and hence, barring cancellations, the initial super-horizon perturbations produced by inflation must contain some minimum amount of mode coupling, or primordial non-Gaussianity. In single-field slow-roll models, where this lower bound is saturated, non-Gaussianity is controlled by two observables: the tensor-to-scalar ratio, which is uncertain by more than fifty orders of magnitude; and the scalar spectral index, or tilt, which is relatively well measured. It is well known that to leading and next-to-leading order in derivatives, the contributions proportional to the tilt disappear from any local observable, and suspicion has been raised that this might happen to all orders, allowing for an arbitrarily low amount of primordial non-Gaussianity. Employing Conformal Fermi Coordinates, we show explicitly that this is not the case. Instead, a contribution of order the tilt appears in local observables. In summary, the floor of physical primordial non-Gaussianity in our Universe has a squeezed-limit scaling of k {sub ℓ}{sup 2}/ k {sub s} {sup 2}, similar to equilateral and orthogonal shapes, and a dimensionless amplitude of order 0.1 × ( n {sub s}−1).
Gaussian shaping filter for nuclear spectrometry
International Nuclear Information System (INIS)
Menezes, A.S.C. de.
1980-01-01
A theorical study of a gaussian shaping filter, using Pade approximation, for using in gamma spectroscopy is presented. This approximation has proved superior to the classical cascade RC integrators approximation in therms of signal-to-noise ratio and pulse simmetry. An experimental filter was designed, simulated in computer, constructed, and tested in the laboratory. (author) [pt
Asymptotic expansions for the Gaussian unitary ensemble
DEFF Research Database (Denmark)
Haagerup, Uffe; Thorbjørnsen, Steen
2012-01-01
Let g : R ¿ C be a C8-function with all derivatives bounded and let trn denote the normalized trace on the n × n matrices. In Ref. 3 Ercolani and McLaughlin established asymptotic expansions of the mean value ¿{trn(g(Xn))} for a rather general class of random matrices Xn, including the Gaussian U...
Chimera states in Gaussian coupled map lattices
Li, Xiao-Wen; Bi, Ran; Sun, Yue-Xiang; Zhang, Shuo; Song, Qian-Qian
2018-04-01
We study chimera states in one-dimensional and two-dimensional Gaussian coupled map lattices through simulations and experiments. Similar to the case of global coupling oscillators, individual lattices can be regarded as being controlled by a common mean field. A space-dependent order parameter is derived from a self-consistency condition in order to represent the collective state.
Gaussian curvature on hyperelliptic Riemann surfaces
Indian Academy of Sciences (India)
Indian Acad. Sci. (Math. Sci.) Vol. 124, No. 2, May 2014, pp. 155–167. c Indian Academy of Sciences. Gaussian curvature on hyperelliptic Riemann surfaces. ABEL CASTORENA. Centro de Ciencias Matemáticas (Universidad Nacional Autónoma de México,. Campus Morelia) Apdo. Postal 61-3 Xangari, C.P. 58089 Morelia,.
Additivity properties of a Gaussian channel
International Nuclear Information System (INIS)
Giovannetti, Vittorio; Lloyd, Seth
2004-01-01
The Amosov-Holevo-Werner conjecture implies the additivity of the minimum Renyi entropies at the output of a channel. The conjecture is proven true for all Renyi entropies of integer order greater than two in a class of Gaussian bosonic channel where the input signal is randomly displaced or where it is coupled linearly to an external environment
Modeling text with generalizable Gaussian mixtures
DEFF Research Database (Denmark)
Hansen, Lars Kai; Sigurdsson, Sigurdur; Kolenda, Thomas
2000-01-01
We apply and discuss generalizable Gaussian mixture (GGM) models for text mining. The model automatically adapts model complexity for a given text representation. We show that the generalizability of these models depends on the dimensionality of the representation and the sample size. We discuss...
Improving the gaussian effective potential: quantum mechanics
International Nuclear Information System (INIS)
Eboli, O.J.P.; Thomaz, M.T.; Lemos, N.A.
1990-08-01
In order to gain intuition for variational problems in field theory, we analyze variationally the quantum-mechanical anharmonic oscillator [(V(x)sup(k) - sub(2) x sup(2) + sup(λ) - sub(4) λ sup(4)]. Special attention is paid to improvements to the Gaussian effective potential. (author)
Open problems in Gaussian fluid queueing theory
Dȩbicki, K.; Mandjes, M.
2011-01-01
We present three challenging open problems that originate from the analysis of the asymptotic behavior of Gaussian fluid queueing models. In particular, we address the problem of characterizing the correlation structure of the stationary buffer content process, the speed of convergence to
Oracle Wiener filtering of a Gaussian signal
Babenko, A.; Belitser, E.
2011-01-01
We study the problem of filtering a Gaussian process whose trajectories, in some sense, have an unknown smoothness ß0 from the white noise of small intensity e. If we knew the parameter ß0, we would use the Wiener filter which has the meaning of oracle. Our goal is now to mimic the oracle, i.e.,
Oracle Wiener filtering of a Gaussian signal
Babenko, A.; Belitser, E.N.
2011-01-01
We study the problem of filtering a Gaussian process whose trajectories, in some sense, have an unknown smoothness β0 from the white noise of small intensity . If we knew the parameter β0, we would use the Wiener filter which has the meaning of oracle. Our goal is now to mimic the oracle, i.e.,
Perfusion Quantification Using Gaussian Process Deconvolution
DEFF Research Database (Denmark)
Andersen, Irene Klærke; Have, Anna Szynkowiak; Rasmussen, Carl Edward
2002-01-01
The quantification of perfusion using dynamic susceptibility contrast MRI (DSC-MRI) requires deconvolution to obtain the residual impulse response function (IRF). In this work, a method using the Gaussian process for deconvolution (GPD) is proposed. The fact that the IRF is smooth is incorporated...
Modelling Inverse Gaussian Data with Censored Response Values: EM versus MCMC
Directory of Open Access Journals (Sweden)
R. S. Sparks
2011-01-01
Full Text Available Low detection limits are common in measure environmental variables. Building models using data containing low or high detection limits without adjusting for the censoring produces biased models. This paper offers approaches to estimate an inverse Gaussian distribution when some of the data used are censored because of low or high detection limits. Adjustments for the censoring can be made if there is between 2% and 20% censoring using either the EM algorithm or MCMC. This paper compares these approaches.
Estimators for local non-Gaussianities
International Nuclear Information System (INIS)
Creminelli, P.; Senatore, L.; Zaldarriaga, M.
2006-05-01
We study the Likelihood function of data given f NL for the so-called local type of non-Gaussianity. In this case the curvature perturbation is a non-linear function, local in real space, of a Gaussian random field. We compute the Cramer-Rao bound for f NL and show that for small values of f NL the 3- point function estimator saturates the bound and is equivalent to calculating the full Likelihood of the data. However, for sufficiently large f NL , the naive 3-point function estimator has a much larger variance than previously thought. In the limit in which the departure from Gaussianity is detected with high confidence, error bars on f NL only decrease as 1/ln N pix rather than N pix -1/2 as the size of the data set increases. We identify the physical origin of this behavior and explain why it only affects the local type of non- Gaussianity, where the contribution of the first multipoles is always relevant. We find a simple improvement to the 3-point function estimator that makes the square root of its variance decrease as N pix -1/2 even for large f NL , asymptotically approaching the Cramer-Rao bound. We show that using the modified estimator is practically equivalent to computing the full Likelihood of f NL given the data. Thus other statistics of the data, such as the 4-point function and Minkowski functionals, contain no additional information on f NL . In particular, we explicitly show that the recent claims about the relevance of the 4-point function are not correct. By direct inspection of the Likelihood, we show that the data do not contain enough information for any statistic to be able to constrain higher order terms in the relation between the Gaussian field and the curvature perturbation, unless these are orders of magnitude larger than the size suggested by the current limits on f NL . (author)
Cosmological information in Gaussianized weak lensing signals
Joachimi, B.; Taylor, A. N.; Kiessling, A.
2011-11-01
Gaussianizing the one-point distribution of the weak gravitational lensing convergence has recently been shown to increase the signal-to-noise ratio contained in two-point statistics. We investigate the information on cosmology that can be extracted from the transformed convergence fields. Employing Box-Cox transformations to determine optimal transformations to Gaussianity, we develop analytical models for the transformed power spectrum, including effects of noise and smoothing. We find that optimized Box-Cox transformations perform substantially better than an offset logarithmic transformation in Gaussianizing the convergence, but both yield very similar results for the signal-to-noise ratio. None of the transformations is capable of eliminating correlations of the power spectra between different angular frequencies, which we demonstrate to have a significant impact on the errors in cosmology. Analytic models of the Gaussianized power spectrum yield good fits to the simulations and produce unbiased parameter estimates in the majority of cases, where the exceptions can be traced back to the limitations in modelling the higher order correlations of the original convergence. In the ideal case, without galaxy shape noise, we find an increase in the cumulative signal-to-noise ratio by a factor of 2.6 for angular frequencies up to ℓ= 1500, and a decrease in the area of the confidence region in the Ωm-σ8 plane, measured in terms of q-values, by a factor of 4.4 for the best performing transformation. When adding a realistic level of shape noise, all transformations perform poorly with little decorrelation of angular frequencies, a maximum increase in signal-to-noise ratio of 34 per cent, and even slightly degraded errors on cosmological parameters. We argue that to find Gaussianizing transformations of practical use, it will be necessary to go beyond transformations of the one-point distribution of the convergence, extend the analysis deeper into the non
Learning non-Gaussian Time Series using the Box-Cox Gaussian Process
Rios, Gonzalo; Tobar, Felipe
2018-01-01
Gaussian processes (GPs) are Bayesian nonparametric generative models that provide interpretability of hyperparameters, admit closed-form expressions for training and inference, and are able to accurately represent uncertainty. To model general non-Gaussian data with complex correlation structure, GPs can be paired with an expressive covariance kernel and then fed into a nonlinear transformation (or warping). However, overparametrising the kernel and the warping is known to, respectively, hin...
Application of Gaussian cubature to model two-dimensional population balances
Directory of Open Access Journals (Sweden)
Bałdyga Jerzy
2017-09-01
Full Text Available In many systems of engineering interest the moment transformation of population balance is applied. One of the methods to solve the transformed population balance equations is the quadrature method of moments. It is based on the approximation of the density function in the source term by the Gaussian quadrature so that it preserves the moments of the original distribution. In this work we propose another method to be applied to the multivariate population problem in chemical engineering, namely a Gaussian cubature (GC technique that applies linear programming for the approximation of the multivariate distribution. Examples of the application of the Gaussian cubature (GC are presented for four processes typical for chemical engineering applications. The first and second ones are devoted to crystallization modeling with direction-dependent two-dimensional and three-dimensional growth rates, the third one represents drop dispersion accompanied by mass transfer in liquid-liquid dispersions and finally the fourth case regards the aggregation and sintering of particle populations.
DEFF Research Database (Denmark)
Mahnke, Martina; Uprichard, Emma
2014-01-01
Imagine sailing across the ocean. The sun is shining, vastness all around you. And suddenly [BOOM] you’ve hit an invisible wall. Welcome to the Truman Show! Ever since Eli Pariser published his thoughts on a potential filter bubble, this movie scenario seems to have become reality, just with slight...... changes: it’s not the ocean, it’s the internet we’re talking about, and it’s not a TV show producer, but algorithms that constitute a sort of invisible wall. Building on this assumption, most research is trying to ‘tame the algorithmic tiger’. While this is a valuable and often inspiring approach, we...
International Nuclear Information System (INIS)
Mumovic, D.; Crowther, J.M.; Stevanovic, Z.
2003-01-01
Since 1950 the world population has more than doubled but meanwhile the global number of cars has increased by a factor of 10. In that same period the fraction of people living in urban areas has increased by a factor of 4. Apart from large point-sources of local air pollution, traffic induced pollution is now the most significant contributor to urban air quality in city centres, particularly for carbon monoxide, oxides of nitrogen and fine particulate matter. Until recently, pollutant dispersion in urban areas has usually been numerically investigated by using empirical models, such as the Gaussian plume model, or by extensions of this technique to line sources and multiple sources. More recently, advanced computational fluid dynamics (CFD) simulations have been attempted but have been mainly two-dimensional and often encompassing only a single street canyon. This paper provides a comprehensive, critical evaluation of dispersion of pollutants in urban areas. A three-dimensional flow model has been set-up for a staggered crossroad, using the Navier-Stokes equations and the conservation equation for species concentration. The effect of using several different turbulence models, including the k-ε model, modifications and extensions, has been investigated. Cartesian coordinates have been used in connection with the Partial Solution Algorithm (PARSOL) and Body Fitted Coordinates (BFC). The effects of several different numerical algorithms for discretization of differential equations have also been studied. More than thirty cases are analysed, and the main results are compared with wind tunnel experiments. The numerical results are presented as non-dimensional values to facilitate comparison between experimental and numerical studies. It has been shown that the numerical studies have been able to simulate the air-flow in urban areas and confirm, qualitatively, the previous field observations and wind tunnel results. This success encouraged the authors to extend such
Energy Technology Data Exchange (ETDEWEB)
Mumovic, D.; Crowther, J.M. [Glasgow Caledonian Univ., School of Built and Natural Environment, Glasgow (United Kingdom)]. E-mail: dmumov10@caledonian.ac.uk; Stevanovic, Z. [Univ. of Belgrade, Inst. of Nuclear Sciences, Belgrade (Serbia and Montenegro)
2003-07-01
Since 1950 the world population has more than doubled but meanwhile the global number of cars has increased by a factor of 10. In that same period the fraction of people living in urban areas has increased by a factor of 4. Apart from large point-sources of local air pollution, traffic induced pollution is now the most significant contributor to urban air quality in city centres, particularly for carbon monoxide, oxides of nitrogen and fine particulate matter. Until recently, pollutant dispersion in urban areas has usually been numerically investigated by using empirical models, such as the Gaussian plume model, or by extensions of this technique to line sources and multiple sources. More recently, advanced computational fluid dynamics (CFD) simulations have been attempted but have been mainly two-dimensional and often encompassing only a single street canyon. This paper provides a comprehensive, critical evaluation of dispersion of pollutants in urban areas. A three-dimensional flow model has been set-up for a staggered crossroad, using the Navier-Stokes equations and the conservation equation for species concentration. The effect of using several different turbulence models, including the k-{epsilon} model, modifications and extensions, has been investigated. Cartesian coordinates have been used in connection with the Partial Solution Algorithm (PARSOL) and Body Fitted Coordinates (BFC). The effects of several different numerical algorithms for discretization of differential equations have also been studied. More than thirty cases are analysed, and the main results are compared with wind tunnel experiments. The numerical results are presented as non-dimensional values to facilitate comparison between experimental and numerical studies. It has been shown that the numerical studies have been able to simulate the air-flow in urban areas and confirm, qualitatively, the previous field observations and wind tunnel results. This success encouraged the authors to extend
Improving IUE High Dispersion Extraction
Lawton, Patricia J.; VanSteenberg, M. E.; Massa, D.
2007-01-01
We present a different method to extract high dispersion International Ultraviolet Explorer (IUE) spectra from the New Spectral Image Processing System (NEWSIPS) geometrically and photometrically corrected (SI HI) images of the echellogram. The new algorithm corrects many of the deficiencies that exist in the NEWSIPS high dispersion (SIHI) spectra . Specifically, it does a much better job of accounting for the overlap of the higher echelle orders, it eliminates a significant time dependency in the extracted spectra (which can be traced to the background model used in the NEWSIPS extractions), and it can extract spectra from echellogram images that are more highly distorted than the NEWSIPS extraction routines can handle. Together, these improvements yield a set of IUE high dispersion spectra whose scientific integrity is sign ificantly better than the NEWSIPS products. This work has been supported by NASA ADP grants.
Comparison of Pilot Symbol Embedded Channel Estimation Algorithms
Directory of Open Access Journals (Sweden)
P. Kadlec
2009-12-01
Full Text Available In the paper, algorithms of the pilot symbol embedded channel estimation are compared. Attention is turned to the Least Square (LS channel estimation and the Sliding Correlator (SC algorithm. Both algorithms are implemented in Matlab to estimate the Channel Impulse Response (CIR of a channel exhibiting multi-path propagation. Algorithms are compared from the viewpoint of computational demands, influence of the Additive White Gaussian Noise (AWGN, an embedded pilot symbol and a computed CIR over the estimation error.
The Dispersal and Persistence of Invasive Marine Species
Glick, E. R.; Pringle, J.
2007-12-01
The spread of invasive marine species is a continuing problem throughout the world, though not entirely understood. Why do some species invade more easily than the rest? How are the range limits of these species set? Recent research (Byers & Pringle 2006, Pringle & Wares 2007) has produced retention criteria that determine whether a coastal species with a benthic adult stage and planktonic larvae can be retained within its range and invade in the direction opposite that of the mean current experienced by the larvae (i.e. upstream). These results however, are only accurate for Gaussian dispersal kernels. For kernels whose kurtosis differs from a Gaussian's, the retention criteria becomes increasingly inaccurate as the mean current increases. Using recent results of Lutscher (2006), we find an improved retention criterion which is much more accurate for non- Gaussian dispersal kernels. The importance of considering non-Gaussian kernels is illustrated for a number of commonly used dispersal kernels, and the relevance of these calculations is illustrated by considering the northward limit of invasion of Hemigrapsus sanguineus, an important invader in the Gulf of Maine.
An Analytical Method for the Abel Inversion of Asymmetrical Gaussian Profiles
International Nuclear Information System (INIS)
Xu Guosheng; Wan Baonian
2007-01-01
An analytical algorithm for fast calculation of the Abel inversion for density profile measurement in tokamak is developed. Based upon the assumptions that the particle source is negligibly small in the plasma core region, density profiles can be approximated by an asymmetrical Gaussian distribution controlled only by one parameter V 0 /D and V 0 /D is constant along the radial direction, the analytical algorithm is presented and examined against a testing profile. The validity is confirmed by benchmark with the standard Abel inversion method and the theoretical profile. The scope of application as well as the error analysis is also discussed in detail
Using Gaussian Process Annealing Particle Filter for 3D Human Tracking
Directory of Open Access Journals (Sweden)
Michael Rudzsky
2008-01-01
Full Text Available We present an approach for human body parts tracking in 3D with prelearned motion models using multiple cameras. Gaussian process annealing particle filter is proposed for tracking in order to reduce the dimensionality of the problem and to increase the tracker's stability and robustness. Comparing with a regular annealed particle filter-based tracker, we show that our algorithm can track better for low frame rate videos. We also show that our algorithm is capable of recovering after a temporal target loss.
Large deviations for Gaussian processes in Hoelder norm
International Nuclear Information System (INIS)
Fatalov, V R
2003-01-01
Some results are proved on the exact asymptotic representation of large deviation probabilities for Gaussian processes in the Hoeder norm. The following classes of processes are considered: the Wiener process, the Brownian bridge, fractional Brownian motion, and stationary Gaussian processes with power-law covariance function. The investigation uses the method of double sums for Gaussian fields
Phase space structure of generalized Gaussian cat states
International Nuclear Information System (INIS)
Nicacio, Fernando; Maia, Raphael N.P.; Toscano, Fabricio; Vallejos, Raul O.
2010-01-01
We analyze generalized Gaussian cat states obtained by superposing arbitrary Gaussian states. The structure of the interference term of the Wigner function is always hyperbolic, surviving the action of a thermal reservoir. We also consider certain superpositions of mixed Gaussian states. An application to semiclassical dynamics is discussed.
Linking network usage patterns to traffic Gaussianity fit
de Oliveira Schmidt, R.; Sadre, R.; Melnikov, Nikolay; Schönwälder, Jürgen; Pras, Aiko
Gaussian traffic models are widely used in the domain of network traffic modeling. The central assumption is that traffic aggregates are Gaussian distributed. Due to its importance, the Gaussian character of network traffic has been extensively assessed by researchers in the past years. In 2001,
MESOI, an interactive atmospheric dispersion model for emergency response applications
International Nuclear Information System (INIS)
Ramsdell, J.V.; Athey, G.F.; Glantz, C.S.
1984-01-01
MESOI is an interactive atmospheric dispersion model that has been developed for use by the U.S. Department of Energy, and the U.S. Nuclear Regulatory Commission in responding to emergencies at nuclear facilities. MESOI uses both straight-line Gaussian plume and Lagrangian trajectory Gaussian puff models to estimate time-integrated ground-level air and surface concentrations. Puff trajectories are determined from temporally and spatially varying horizontal wind fields that are defined in 3 dimensions. Other processes treated in MESOI include dry deposition, wet deposition and radioactive decay
A Cooperative Framework for Fireworks Algorithm
Zheng, Shaoqiu; Li, Junzhi; Janecek, Andreas; Tan, Ying
2015-01-01
This paper presents a cooperative framework for fireworks algorithm (CoFFWA). A detailed analysis of existing fireworks algorithm (FWA) and its recently developed variants has revealed that (i) the selection strategy lead to the contribution of the firework with the best fitness (core firework) for the optimization overwhelms the contributions of the rest of fireworks (non-core fireworks) in the explosion operator, (ii) the Gaussian mutation operator is not as effective as it is designed to b...
Stellar atmospheric parameter estimation using Gaussian process regression
Bu, Yude; Pan, Jingchang
2015-02-01
As is well known, it is necessary to derive stellar parameters from massive amounts of spectral data automatically and efficiently. However, in traditional automatic methods such as artificial neural networks (ANNs) and kernel regression (KR), it is often difficult to optimize the algorithm structure and determine the optimal algorithm parameters. Gaussian process regression (GPR) is a recently developed method that has been proven to be capable of overcoming these difficulties. Here we apply GPR to derive stellar atmospheric parameters from spectra. Through evaluating the performance of GPR on Sloan Digital Sky Survey (SDSS) spectra, Medium resolution Isaac Newton Telescope Library of Empirical Spectra (MILES) spectra, ELODIE spectra and the spectra of member stars of galactic globular clusters, we conclude that GPR can derive stellar parameters accurately and precisely, especially when we use data preprocessed with principal component analysis (PCA). We then compare the performance of GPR with that of several widely used regression methods (ANNs, support-vector regression and KR) and find that with GPR it is easier to optimize structures and parameters and more efficient and accurate to extract atmospheric parameters.
Optimisation of centroiding algorithms for photon event counting imaging
International Nuclear Information System (INIS)
Suhling, K.; Airey, R.W.; Morgan, B.L.
1999-01-01
Approaches to photon event counting imaging in which the output events of an image intensifier are located using a centroiding technique have long been plagued by fixed pattern noise in which a grid of dimensions similar to those of the CCD pixels is superimposed on the image. This is caused by a mismatch between the photon event shape and the centroiding algorithm. We have used hyperbolic cosine, Gaussian, Lorentzian, parabolic as well as 3-, 5-, and 7-point centre of gravity algorithms, and hybrids thereof, to assess means of minimising this fixed pattern noise. We show that fixed pattern noise generated by the widely used centre of gravity centroiding is due to intrinsic features of the algorithm. Our results confirm that the recently proposed use of Gaussian centroiding does indeed show a significant reduction of fixed pattern noise compared to centre of gravity centroiding (Michel et al., Mon. Not. R. Astron. Soc. 292 (1997) 611-620). However, the disadvantage of a Gaussian algorithm is a centroiding failure for small pulses, caused by a division by zero, which leads to a loss of detective quantum efficiency (DQE) and to small amounts of residual fixed pattern noise. Using both real data from an image intensifier system employing a progressive scan camera, framegrabber and PC, and also synthetic data from Monte-Carlo simulations, we find that hybrid centroiding algorithms can reduce the fixed pattern noise without loss of resolution or loss of DQE. Imaging a test pattern to assess the features of the different algorithms shows that a hybrid of Gaussian and 3-point centre of gravity centroiding algorithms results in an optimum combination of low fixed pattern noise (lower than a simple Gaussian), high DQE, and high resolution. The Lorentzian algorithm gives the worst results in terms of high fixed pattern noise and low resolution, and the Gaussian and hyperbolic cosine algorithms have the lowest DQEs
On Alternate Relaying with Improper Gaussian Signaling
Gaafar, Mohamed
2016-06-06
In this letter, we investigate the potential benefits of adopting improper Gaussian signaling (IGS) in a two-hop alternate relaying (AR) system. Given the known benefits of using IGS in interference-limited networks, we propose to use IGS to relieve the inter-relay interference (IRI) impact on the AR system assuming no channel state information is available at the source. In this regard, we assume that the two relays use IGS and the source uses proper Gaussian signaling (PGS). Then, we optimize the degree of impropriety of the relays signal, measured by the circularity coefficient, to maximize the total achievable rate. Simulation results show that using IGS yields a significant performance improvement over PGS, especially when the first hop is a bottleneck due to weak source-relay channel gains and/or strong IRI.
On Alternate Relaying with Improper Gaussian Signaling
Gaafar, Mohamed; Amin, Osama; Ikhlef, Aissa; Chaaban, Anas; Alouini, Mohamed-Slim
2016-01-01
In this letter, we investigate the potential benefits of adopting improper Gaussian signaling (IGS) in a two-hop alternate relaying (AR) system. Given the known benefits of using IGS in interference-limited networks, we propose to use IGS to relieve the inter-relay interference (IRI) impact on the AR system assuming no channel state information is available at the source. In this regard, we assume that the two relays use IGS and the source uses proper Gaussian signaling (PGS). Then, we optimize the degree of impropriety of the relays signal, measured by the circularity coefficient, to maximize the total achievable rate. Simulation results show that using IGS yields a significant performance improvement over PGS, especially when the first hop is a bottleneck due to weak source-relay channel gains and/or strong IRI.
Direct Importance Estimation with Gaussian Mixture Models
Yamada, Makoto; Sugiyama, Masashi
The ratio of two probability densities is called the importance and its estimation has gathered a great deal of attention these days since the importance can be used for various data processing purposes. In this paper, we propose a new importance estimation method using Gaussian mixture models (GMMs). Our method is an extention of the Kullback-Leibler importance estimation procedure (KLIEP), an importance estimation method using linear or kernel models. An advantage of GMMs is that covariance matrices can also be learned through an expectation-maximization procedure, so the proposed method — which we call the Gaussian mixture KLIEP (GM-KLIEP) — is expected to work well when the true importance function has high correlation. Through experiments, we show the validity of the proposed approach.
Fractional Diffusion in Gaussian Noisy Environment
Directory of Open Access Journals (Sweden)
Guannan Hu
2015-03-01
Full Text Available We study the fractional diffusion in a Gaussian noisy environment as described by the fractional order stochastic heat equations of the following form: \\(D_t^{(\\alpha} u(t, x=\\textit{B}u+u\\cdot \\dot W^H\\, where \\(D_t^{(\\alpha}\\ is the Caputo fractional derivative of order \\(\\alpha\\in (0,1\\ with respect to the time variable \\(t\\, \\(\\textit{B}\\ is a second order elliptic operator with respect to the space variable \\(x\\in\\mathbb{R}^d\\ and \\(\\dot W^H\\ a time homogeneous fractional Gaussian noise of Hurst parameter \\(H=(H_1, \\cdots, H_d\\. We obtain conditions satisfied by \\(\\alpha\\ and \\(H\\, so that the square integrable solution \\(u\\ exists uniquely.
Interweave Cognitive Radio with Improper Gaussian Signaling
Hedhly, Wafa
2018-01-15
Improper Gaussian signaling (IGS) has proven its ability in improving the performance of underlay and overlay cognitive radio paradigms. In this paper, the interweave cognitive radio paradigm is studied when the cognitive user employs IGS. The instantaneous achievable rate performance of both the primary and secondary users are analyzed for specific secondary user sensing and detection capabilities. Next, the IGS scheme is optimized to maximize the achievable rate secondary user while satisfying a target minimum rate requirement for the primary user. Proper Gaussian signaling (PGS) scheme design is also derived to be used as benchmark of the IGS scheme design. Finally, different numerical results are introduced to show the gain reaped from adopting IGS over PGS under different system parameters. The main advantage of employing IGS is observed at low sensing and detection capabilities of the SU, lower PU direct link and higher SU interference on the PU side.
Image reconstruction under non-Gaussian noise
DEFF Research Database (Denmark)
Sciacchitano, Federica
During acquisition and transmission, images are often blurred and corrupted by noise. One of the fundamental tasks of image processing is to reconstruct the clean image from a degraded version. The process of recovering the original image from the data is an example of inverse problem. Due...... to the ill-posedness of the problem, the simple inversion of the degradation model does not give any good reconstructions. Therefore, to deal with the ill-posedness it is necessary to use some prior information on the solution or the model and the Bayesian approach. Additive Gaussian noise has been......D thesis intends to solve some of the many open questions for image restoration under non-Gaussian noise. The two main kinds of noise studied in this PhD project are the impulse noise and the Cauchy noise. Impulse noise is due to for instance the malfunctioning pixel elements in the camera sensors, errors...
Non-Markovianity of Gaussian Channels.
Torre, G; Roga, W; Illuminati, F
2015-08-14
We introduce a necessary and sufficient criterion for the non-Markovianity of Gaussian quantum dynamical maps based on the violation of divisibility. The criterion is derived by defining a general vectorial representation of the covariance matrix which is then exploited to determine the condition for the complete positivity of partial maps associated with arbitrary time intervals. Such construction does not rely on the Choi-Jamiolkowski representation and does not require optimization over states.
Log Gaussian Cox processes on the sphere
DEFF Research Database (Denmark)
Pacheco, Francisco Andrés Cuevas; Møller, Jesper
We define and study the existence of log Gaussian Cox processes (LGCPs) for the description of inhomogeneous and aggregated/clustered point patterns on the d-dimensional sphere, with d = 2 of primary interest. Useful theoretical properties of LGCPs are studied and applied for the description of sky...... positions of galaxies, in comparison with previous analysis using a Thomas process. We focus on simple estimation procedures and model checking based on functional summary statistics and the global envelope test....
Recognition of Images Degraded by Gaussian Blur
Czech Academy of Sciences Publication Activity Database
Flusser, Jan; Farokhi, Sajad; Höschl, Cyril; Suk, Tomáš; Zitová, Barbara; Pedone, M.
2016-01-01
Roč. 25, č. 2 (2016), s. 790-806 ISSN 1057-7149 R&D Projects: GA ČR(CZ) GA15-16928S Institutional support: RVO:67985556 Keywords : Blurred image * object recognition * blur invariant comparison * Gaussian blur * projection operators * image moments * moment invariants Subject RIV: JD - Computer Applications, Robotics Impact factor: 4.828, year: 2016 http://library.utia.cas.cz/separaty/2016/ZOI/flusser-0454335.pdf
Adaptive multiple importance sampling for Gaussian processes
Czech Academy of Sciences Publication Activity Database
Xiong, X.; Šmídl, Václav; Filippone, M.
2017-01-01
Roč. 87, č. 8 (2017), s. 1644-1665 ISSN 0094-9655 R&D Projects: GA MŠk(CZ) 7F14287 Institutional support: RVO:67985556 Keywords : Gaussian Process * Bayesian estimation * Adaptive importance sampling Subject RIV: BB - Applied Statistics, Operational Research OBOR OECD: Statistics and probability Impact factor: 0.757, year: 2016 http://library.utia.cas.cz/separaty/2017/AS/smidl-0469804.pdf
Middleton, Beth; van Diggelen, Rudy; Jensen, Kai
Question: How does seed dispersal reduce fen isolation and contribute to biodiversity? Location: European and North American fens. Methods: This paper reviews the literature on seed dispersal to fens. Results: Landscape fragmentation may reduce dispersal opportunities thereby isolating fens and
Neutron inverse kinetics via Gaussian Processes
International Nuclear Information System (INIS)
Picca, Paolo; Furfaro, Roberto
2012-01-01
Highlights: ► A novel technique for the interpretation of experiments in ADS is presented. ► The technique is based on Bayesian regression, implemented via Gaussian Processes. ► GPs overcome the limits of classical methods, based on PK approximation. ► Results compares GPs and ANN performance, underlining similarities and differences. - Abstract: The paper introduces the application of Gaussian Processes (GPs) to determine the subcriticality level in accelerator-driven systems (ADSs) through the interpretation of pulsed experiment data. ADSs have peculiar kinetic properties due to their special core design. For this reason, classical – inversion techniques based on point kinetic (PK) generally fail to generate an accurate estimate of reactor subcriticality. Similarly to Artificial Neural Networks (ANNs), Gaussian Processes can be successfully trained to learn the underlying inverse neutron kinetic model and, as such, they are not limited to the model choice. Importantly, GPs are strongly rooted into the Bayes’ theorem which makes them a powerful tool for statistical inference. Here, GPs have been designed and trained on a set of kinetics models (e.g. point kinetics and multi-point kinetics) for homogeneous and heterogeneous settings. The results presented in the paper show that GPs are very efficient and accurate in predicting the reactivity for ADS-like systems. The variance computed via GPs may provide an indication on how to generate additional data as function of the desired accuracy.
Resonant non-Gaussianity with equilateral properties
International Nuclear Information System (INIS)
Gwyn, Rhiannon; Rummel, Markus
2012-11-01
We discuss the effect of superimposing multiple sources of resonant non-Gaussianity, which arise for instance in models of axion inflation. The resulting sum of oscillating shape contributions can be used to ''Fourier synthesize'' different non-oscillating shapes in the bispectrum. As an example we reproduce an approximately equilateral shape from the superposition of O(10) oscillatory contributions with resonant shape. This implies a possible degeneracy between the equilateral-type non-Gaussianity typical of models with non-canonical kinetic terms, such as DBI inflation, and an equilateral-type shape arising from a superposition of resonant-type contributions in theories with canonical kinetic terms. The absence of oscillations in the 2-point function together with the structure of the resonant N-point functions, imply that detection of equilateral non-Gaussianity at a level greater than the PLANCK sensitivity of f NL ∝O(5) will rule out a resonant origin. We comment on the questions arising from possible embeddings of this idea in a string theory setting.
Unitarily localizable entanglement of Gaussian states
International Nuclear Information System (INIS)
Serafini, Alessio; Adesso, Gerardo; Illuminati, Fabrizio
2005-01-01
We consider generic (mxn)-mode bipartitions of continuous-variable systems, and study the associated bisymmetric multimode Gaussian states. They are defined as (m+n)-mode Gaussian states invariant under local mode permutations on the m-mode and n-mode subsystems. We prove that such states are equivalent, under local unitary transformations, to the tensor product of a two-mode state and of m+n-2 uncorrelated single-mode states. The entanglement between the m-mode and the n-mode blocks can then be completely concentrated on a single pair of modes by means of local unitary operations alone. This result allows us to prove that the PPT (positivity of the partial transpose) condition is necessary and sufficient for the separability of (m+n)-mode bisymmetric Gaussian states. We determine exactly their negativity and identify a subset of bisymmetric states whose multimode entanglement of formation can be computed analytically. We consider explicit examples of pure and mixed bisymmetric states and study their entanglement scaling with the number of modes
Gaussian Hypothesis Testing and Quantum Illumination.
Wilde, Mark M; Tomamichel, Marco; Lloyd, Seth; Berta, Mario
2017-09-22
Quantum hypothesis testing is one of the most basic tasks in quantum information theory and has fundamental links with quantum communication and estimation theory. In this paper, we establish a formula that characterizes the decay rate of the minimal type-II error probability in a quantum hypothesis test of two Gaussian states given a fixed constraint on the type-I error probability. This formula is a direct function of the mean vectors and covariance matrices of the quantum Gaussian states in question. We give an application to quantum illumination, which is the task of determining whether there is a low-reflectivity object embedded in a target region with a bright thermal-noise bath. For the asymmetric-error setting, we find that a quantum illumination transmitter can achieve an error probability exponent stronger than a coherent-state transmitter of the same mean photon number, and furthermore, that it requires far fewer trials to do so. This occurs when the background thermal noise is either low or bright, which means that a quantum advantage is even easier to witness than in the symmetric-error setting because it occurs for a larger range of parameters. Going forward from here, we expect our formula to have applications in settings well beyond those considered in this paper, especially to quantum communication tasks involving quantum Gaussian channels.
Resonant non-Gaussianity with equilateral properties
Energy Technology Data Exchange (ETDEWEB)
Gwyn, Rhiannon [Max-Planck-Institut fuer Gravitationsphysik (Albert-Einstein-Institut), Potsdam (Germany); Rummel, Markus [Hamburg Univ. (Germany). 2. Inst. fuer Theoretische Physik; Westphal, Alexander [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany)
2012-11-15
We discuss the effect of superimposing multiple sources of resonant non-Gaussianity, which arise for instance in models of axion inflation. The resulting sum of oscillating shape contributions can be used to ''Fourier synthesize'' different non-oscillating shapes in the bispectrum. As an example we reproduce an approximately equilateral shape from the superposition of O(10) oscillatory contributions with resonant shape. This implies a possible degeneracy between the equilateral-type non-Gaussianity typical of models with non-canonical kinetic terms, such as DBI inflation, and an equilateral-type shape arising from a superposition of resonant-type contributions in theories with canonical kinetic terms. The absence of oscillations in the 2-point function together with the structure of the resonant N-point functions, imply that detection of equilateral non-Gaussianity at a level greater than the PLANCK sensitivity of f{sub NL} {proportional_to}O(5) will rule out a resonant origin. We comment on the questions arising from possible embeddings of this idea in a string theory setting.
Gaussian Process-Mixture Conditional Heteroscedasticity.
Platanios, Emmanouil A; Chatzis, Sotirios P
2014-05-01
Generalized autoregressive conditional heteroscedasticity (GARCH) models have long been considered as one of the most successful families of approaches for volatility modeling in financial return series. In this paper, we propose an alternative approach based on methodologies widely used in the field of statistical machine learning. Specifically, we propose a novel nonparametric Bayesian mixture of Gaussian process regression models, each component of which models the noise variance process that contaminates the observed data as a separate latent Gaussian process driven by the observed data. This way, we essentially obtain a Gaussian process-mixture conditional heteroscedasticity (GPMCH) model for volatility modeling in financial return series. We impose a nonparametric prior with power-law nature over the distribution of the model mixture components, namely the Pitman-Yor process prior, to allow for better capturing modeled data distributions with heavy tails and skewness. Finally, we provide a copula-based approach for obtaining a predictive posterior for the covariances over the asset returns modeled by means of a postulated GPMCH model. We evaluate the efficacy of our approach in a number of benchmark scenarios, and compare its performance to state-of-the-art methodologies.
Non-Gaussian conductivity fluctuations in semiconductors
International Nuclear Information System (INIS)
Melkonyan, S.V.
2010-01-01
A theoretical study is presented on the statistical properties of conductivity fluctuations caused by concentration and mobility fluctuations of the current carriers. It is established that mobility fluctuations result from random deviations in the thermal equilibrium distribution of the carriers. It is shown that mobility fluctuations have generation-recombination and shot components which do not satisfy the requirements of the central limit theorem, in contrast to the current carrier's concentration fluctuation and intraband component of the mobility fluctuation. It is shown that in general the mobility fluctuation consist of thermal (or intraband) Gaussian and non-thermal (or generation-recombination, shot, etc.) non-Gaussian components. The analyses of theoretical results and experimental data from literature show that the statistical properties of mobility fluctuation and of 1/f-noise fully coincide. The deviation from Gaussian statistics of the mobility or 1/f fluctuations goes hand in hand with the magnitude of non-thermal noise (generation-recombination, shot, burst, pulse noises, etc.).
Perturbative Gaussianizing transforms for cosmological fields
Hall, Alex; Mead, Alexander
2018-01-01
Constraints on cosmological parameters from large-scale structure have traditionally been obtained from two-point statistics. However, non-linear structure formation renders these statistics insufficient in capturing the full information content available, necessitating the measurement of higher order moments to recover information which would otherwise be lost. We construct quantities based on non-linear and non-local transformations of weakly non-Gaussian fields that Gaussianize the full multivariate distribution at a given order in perturbation theory. Our approach does not require a model of the fields themselves and takes as input only the first few polyspectra, which could be modelled or measured from simulations or data, making our method particularly suited to observables lacking a robust perturbative description such as the weak-lensing shear. We apply our method to simulated density fields, finding a significantly reduced bispectrum and an enhanced correlation with the initial field. We demonstrate that our method reconstructs a large proportion of the linear baryon acoustic oscillations, improving the information content over the raw field by 35 per cent. We apply the transform to toy 21 cm intensity maps, showing that our method still performs well in the presence of complications such as redshift-space distortions, beam smoothing, pixel noise and foreground subtraction. We discuss how this method might provide a route to constructing a perturbative model of the fully non-Gaussian multivariate likelihood function.
Hierarchical heuristic search using a Gaussian mixture model for UAV coverage planning.
Lin, Lanny; Goodrich, Michael A
2014-12-01
During unmanned aerial vehicle (UAV) search missions, efficient use of UAV flight time requires flight paths that maximize the probability of finding the desired subject. The probability of detecting the desired subject based on UAV sensor information can vary in different search areas due to environment elements like varying vegetation density or lighting conditions, making it likely that the UAV can only partially detect the subject. This adds another dimension of complexity to the already difficult (NP-Hard) problem of finding an optimal search path. We present a new class of algorithms that account for partial detection in the form of a task difficulty map and produce paths that approximate the payoff of optimal solutions. The algorithms use the mode goodness ratio heuristic that uses a Gaussian mixture model to prioritize search subregions. The algorithms search for effective paths through the parameter space at different levels of resolution. We compare the performance of the new algorithms against two published algorithms (Bourgault's algorithm and LHC-GW-CONV algorithm) in simulated searches with three real search and rescue scenarios, and show that the new algorithms outperform existing algorithms significantly and can yield efficient paths that yield payoffs near the optimal.
Neutron study of non-Gaussian self dynamics in liquid parahydrogen
International Nuclear Information System (INIS)
Bafile, Ubaldo; Celli, Milva; Colognesi, Daniele; Zoppi, Marco; Guarini, Eleonora; De Francesco, Alessio; Formisano, Ferdinando; Neumann, Martin
2012-01-01
A time-honoured approach to single-molecule, or self, dynamics of liquids is based on the so-called Gaussian approximation (GA), where it is assumed that, in the whole dynamical range between hydrodynamic diffusion and free-particle streaming, the motion of a particle is fully determined by a unique function of time directly related to the velocity autocorrelation function. An evident support to the GA is offered by the fact that the approximation becomes exact in both above limit conditions. Yet, experimental inquiries into the presence of non-Gaussian dynamics are very scarce, particularly in liquid parahydrogen in spite of its importance as the prototype of a 'quantum Boltzmann liquid' which has also served as a benchmark for the development of quantum dynamics simulation algorithms. Though experimental evidence of the breakdown of the GA was obtained by some of the authors a few years ago, the localization in Q space of non-Gaussian behaviour was still undetermined, and no quantitative assessment of the effect was ever obtained. These issues have been tackled and solved by a new neutron investigation, which provides the first determination of non-Gaussian behaviour in the framework of the well-known theoretical approach by Rahman, Singwi and Sjölander.
Directory of Open Access Journals (Sweden)
Zhu Xiao
2016-05-01
Full Text Available In this paper, a novel nonlinear framework of smoothing method, non-Gaussian delayed particle smoother (nGDPS, is proposed, which enables vehicle state estimation (VSE with high accuracy taking into account the non-Gaussianity of the measurement and process noises. Within the proposed method, the multivariate Student’s t-distribution is adopted in order to compute the probability distribution function (PDF related to the process and measurement noises, which are assumed to be non-Gaussian distributed. A computation approach based on Ensemble Kalman Filter (EnKF is designed to cope with the mean and the covariance matrix of the proposal non-Gaussian distribution. A delayed Gibbs sampling algorithm, which incorporates smoothing of the sampled trajectories over a fixed-delay, is proposed to deal with the sample degeneracy of particles. The performance is investigated based on the real-world data, which is collected by low-cost on-board vehicle sensors. The comparison study based on the real-world experiments and the statistical analysis demonstrates that the proposed nGDPS has significant improvement on the vehicle state accuracy and outperforms the existing filtering and smoothing methods.
Epileptic Seizure Detection with Log-Euclidean Gaussian Kernel-Based Sparse Representation.
Yuan, Shasha; Zhou, Weidong; Wu, Qi; Zhang, Yanli
2016-05-01
Epileptic seizure detection plays an important role in the diagnosis of epilepsy and reducing the massive workload of reviewing electroencephalography (EEG) recordings. In this work, a novel algorithm is developed to detect seizures employing log-Euclidean Gaussian kernel-based sparse representation (SR) in long-term EEG recordings. Unlike the traditional SR for vector data in Euclidean space, the log-Euclidean Gaussian kernel-based SR framework is proposed for seizure detection in the space of the symmetric positive definite (SPD) matrices, which form a Riemannian manifold. Since the Riemannian manifold is nonlinear, the log-Euclidean Gaussian kernel function is applied to embed it into a reproducing kernel Hilbert space (RKHS) for performing SR. The EEG signals of all channels are divided into epochs and the SPD matrices representing EEG epochs are generated by covariance descriptors. Then, the testing samples are sparsely coded over the dictionary composed by training samples utilizing log-Euclidean Gaussian kernel-based SR. The classification of testing samples is achieved by computing the minimal reconstructed residuals. The proposed method is evaluated on the Freiburg EEG dataset of 21 patients and shows its notable performance on both epoch-based and event-based assessments. Moreover, this method handles multiple channels of EEG recordings synchronously which is more speedy and efficient than traditional seizure detection methods.
Uncertainty in perception and the Hierarchical Gaussian Filter
Directory of Open Access Journals (Sweden)
Christoph Daniel Mathys
2014-11-01
Full Text Available In its full sense, perception rests on an agent’s model of how its sensory input comes about and the inferences it draws based on this model. These inferences are necessarily uncertain. Here, we illustrate how the hierarchical Gaussian filter (HGF offers a principled and generic way to deal with the several forms that uncertainty in perception takes. The HGF is a recent derivation of one-step update equations from Bayesian principles that rests on a hierarchical generative model of the environment and its (instability. It is computationally highly efficient, allows for online estimates of hidden states, and has found numerous applications to experimental data from human subjects. In this paper, we generalize previous descriptions of the HGF and its account of perceptual uncertainty. First, we explicitly formulate the extension of the HGF’s hierarchy to any number of levels; second, we discuss how various forms of uncertainty are accommodated by the minimization of variational free energy as encoded in the update equations; third, we combine the HGF with decision models and demonstrate the inversion of this combination; finally, we report a simulation study that compared four optimization methods for inverting the HGF/decision model combination at different noise levels. These four methods (Nelder-Mead simplex algorithm, Gaussian process-based global optimization, variational Bayes and Markov chain Monte Carlo sampling all performed well even under considerable noise, with variational Bayes offering the best combination of efficiency and informativeness of inference. Our results demonstrate that the HGF provides a principled, flexible, and efficient - but at the same time intuitive - framework for the resolution of perceptual uncertainty in behaving agents.
Sharba, A. B.; Chekhlov, O.; Wyatt, A. S.; Pattathil, R.; Borghesi, M.; Sarri, G.
2018-03-01
We present a new phase characterization technique for ultrashort laser pulses that employs self-phase modulation (SPM) in the dispersion scan approach. The method can be implemented by recording a set of nonlinearly modulated spectra generated with a set of known chirp values. The unknown phase of the pulse is retrieved by linking the recorded spectra to the initial spectrum of the pulse via a phase function guessed by a function minimization iterative algorithm. This technique has many advantages over the dispersion scan techniques that use frequency conversion processes. Mainly, the use of SPM cancels out the phase and group velocity mismatch errors and dramatically widens the spectral acceptance of the nonlinear medium and the range of working wavelength. The robustness of the technique is demonstrated with smooth and complex phase retrievals using numerical examples. The method is shown to be not affected by the spatial distribution of the beam or the presence of nonlinear absorption process. In addition, we present an efficient method for phase representation based on a summation of a set of Gaussian functions. The independence of the functions from each other prevents phase coupling of any kind and facilitates a flexible phase representation.
Dispersion modeling in assessing air quality of industrial projects under Indian regulatory regime
Energy Technology Data Exchange (ETDEWEB)
Bandyopadhyay, Amitava [Department of Chemical Engineering, University of Calcutta, 92, A.P.C.Road, Kolkata 700 009 (India)
2010-07-01
Environmental impact assessment (EIA) studies conducted over the years as a part of obtaining environmental clearance in accordance with Indian regulation have been given significant attention towards carrying out Gaussian dispersion modeling for predicting the ground level concentration (GLC) of pollutants, especially for SO{sub 2}. Making any adhoc decision towards recommending flue gas desulfurization (FGD) system in Indian fossil fuel combustion operations is not realistic considering the usage of fuel with low sulfur content. Thus a predictive modeling is imperative prior to making any conclusive decision. In the light of this finding, dispersion modeling has been accorded in Indian environmental regulations. This article aims at providing approaches to ascertain pollution potential for proposed power plant operation either alone or in presence of other industrial operations under different conditions. In order to assess the performance of the computational work four different cases were analyzed based on worst scenario. Results obtained through predictions were compared with National Ambient Air Quality Standards (NAAQS) of India. One specific case found to overshoot the ambient air quality adversely in respect of SO2 and was therefore, suggested to install a FGD system with at least 80 % SO2 removal efficiency. With this recommendation, the cumulative prediction yielded a very conservative resultant value of 24 hourly maximum GLC of SO2 as against a value that exceeded well above the stipulated value without considering the FGD system. The computational algorithm developed can therefore, be gainfully utilized for the purpose of EIA analysis in Indian condition.
Background based Gaussian mixture model lesion segmentation in PET
Energy Technology Data Exchange (ETDEWEB)
Soffientini, Chiara Dolores, E-mail: chiaradolores.soffientini@polimi.it; Baselli, Giuseppe [DEIB, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, Milan 20133 (Italy); De Bernardi, Elisabetta [Department of Medicine and Surgery, Tecnomed Foundation, University of Milano—Bicocca, Monza 20900 (Italy); Zito, Felicia; Castellani, Massimo [Nuclear Medicine Department, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, via Francesco Sforza 35, Milan 20122 (Italy)
2016-05-15
Purpose: Quantitative {sup 18}F-fluorodeoxyglucose positron emission tomography is limited by the uncertainty in lesion delineation due to poor SNR, low resolution, and partial volume effects, subsequently impacting oncological assessment, treatment planning, and follow-up. The present work develops and validates a segmentation algorithm based on statistical clustering. The introduction of constraints based on background features and contiguity priors is expected to improve robustness vs clinical image characteristics such as lesion dimension, noise, and contrast level. Methods: An eight-class Gaussian mixture model (GMM) clustering algorithm was modified by constraining the mean and variance parameters of four background classes according to the previous analysis of a lesion-free background volume of interest (background modeling). Hence, expectation maximization operated only on the four classes dedicated to lesion detection. To favor the segmentation of connected objects, a further variant was introduced by inserting priors relevant to the classification of neighbors. The algorithm was applied to simulated datasets and acquired phantom data. Feasibility and robustness toward initialization were assessed on a clinical dataset manually contoured by two expert clinicians. Comparisons were performed with respect to a standard eight-class GMM algorithm and to four different state-of-the-art methods in terms of volume error (VE), Dice index, classification error (CE), and Hausdorff distance (HD). Results: The proposed GMM segmentation with background modeling outperformed standard GMM and all the other tested methods. Medians of accuracy indexes were VE <3%, Dice >0.88, CE <0.25, and HD <1.2 in simulations; VE <23%, Dice >0.74, CE <0.43, and HD <1.77 in phantom data. Robustness toward image statistic changes (±15%) was shown by the low index changes: <26% for VE, <17% for Dice, and <15% for CE. Finally, robustness toward the user-dependent volume initialization was
Searching for non-Gaussianity in the WMAP data
International Nuclear Information System (INIS)
Bernui, A.; Reboucas, M. J.
2009-01-01
Some analyses of recent cosmic microwave background (CMB) data have provided hints that there are deviations from Gaussianity in the WMAP CMB temperature fluctuations. Given the far-reaching consequences of such a non-Gaussianity for our understanding of the physics of the early universe, it is important to employ alternative indicators in order to determine whether the reported non-Gaussianity is of cosmological origin, and/or extract further information that may be helpful for identifying its causes. We propose two new non-Gaussianity indicators, based on skewness and kurtosis of large-angle patches of CMB maps, which provide a measure of departure from Gaussianity on large angular scales. A distinctive feature of these indicators is that they provide sky maps of non-Gaussianity of the CMB temperature data, thus allowing a possible additional window into their origins. Using these indicators, we find no significant deviation from Gaussianity in the three and five-year WMAP Internal Linear Combination (ILC) map with KQ75 mask, while the ILC unmasked map exhibits deviation from Gaussianity, quantifying therefore the WMAP team recommendation to employ the new mask KQ75 for tests of Gaussianity. We also use our indicators to test for Gaussianity the single frequency foreground unremoved WMAP three and five-year maps, and show that the K and Ka maps exhibit a clear indication of deviation from Gaussianity even with the KQ75 mask. We show that our findings are robust with respect to the details of the method.
Performance in population models for count data, part II: a new SAEM algorithm
Savic, Radojka; Lavielle, Marc
2009-01-01
Analysis of count data from clinical trials using mixed effect analysis has recently become widely used. However, algorithms available for the parameter estimation, including LAPLACE and Gaussian quadrature (GQ), are associated with certain limitations, including bias in parameter estimates and the long analysis runtime. The stochastic approximation expectation maximization (SAEM) algorithm has proven to be a very efficient and powerful tool in the analysis of continuous data. The aim of this study was to implement and investigate the performance of a new SAEM algorithm for application to count data. A new SAEM algorithm was implemented in MATLAB for estimation of both, parameters and the Fisher information matrix. Stochastic Monte Carlo simulations followed by re-estimation were performed according to scenarios used in previous studies (part I) to investigate properties of alternative algorithms (1). A single scenario was used to explore six probability distribution models. For parameter estimation, the relative bias was less than 0.92% and 4.13 % for fixed and random effects, for all models studied including ones accounting for over- or under-dispersion. Empirical and estimated relative standard errors were similar, with distance between them being <1.7 % for all explored scenarios. The longest CPU time was 95s for parameter estimation and 56s for SE estimation. The SAEM algorithm was extended for analysis of count data. It provides accurate estimates of both, parameters and standard errors. The estimation is significantly faster compared to LAPLACE and GQ. The algorithm is implemented in Monolix 3.1, (beta-version available in July 2009). PMID:19680795
Koc Ut-Va
2005-01-01
The widely-used LMS algorithm for coefficient updates in adaptive (feedforward/decision-feedback) equalizers is found to be suboptimal for ASE-dominant systems but various coefficient-dithering approaches suffer from slow adaptation rate without guarantee of convergence. In view of the non-Gaussian nature of optical noise after the square-law optoelectronic conversion, we propose to apply the higher-order least-mean 2 th-order (LMN) algorithms resulting in OSNR penalty which is 1.5–2 d...
Lee, Soojeong; Rajan, Sreeraman; Jeon, Gwanggil; Chang, Joon-Hyuk; Dajani, Hilmi R; Groza, Voicu Z
2017-06-01
Blood pressure (BP) is one of the most important vital indicators and plays a key role in determining the cardiovascular activity of patients. This paper proposes a hybrid approach consisting of nonparametric bootstrap (NPB) and machine learning techniques to obtain the characteristic ratios (CR) used in the blood pressure estimation algorithm to improve the accuracy of systolic blood pressure (SBP) and diastolic blood pressure (DBP) estimates and obtain confidence intervals (CI). The NPB technique is used to circumvent the requirement for large sample set for obtaining the CI. A mixture of Gaussian densities is assumed for the CRs and Gaussian mixture model (GMM) is chosen to estimate the SBP and DBP ratios. The K-means clustering technique is used to obtain the mixture order of the Gaussian densities. The proposed approach achieves grade "A" under British Society of Hypertension testing protocol and is superior to the conventional approach based on maximum amplitude algorithm (MAA) that uses fixed CR ratios. The proposed approach also yields a lower mean error (ME) and the standard deviation of the error (SDE) in the estimates when compared to the conventional MAA method. In addition, CIs obtained through the proposed hybrid approach are also narrower with a lower SDE. The proposed approach combining the NPB technique with the GMM provides a methodology to derive individualized characteristic ratio. The results exhibit that the proposed approach enhances the accuracy of SBP and DBP estimation and provides narrower confidence intervals for the estimates. Copyright © 2015 Elsevier Ltd. All rights reserved.
Estimating plume dispersion: a comparison of several sigma schemes
International Nuclear Information System (INIS)
Irwin, J.S.
1983-01-01
The lateral and vertical Gaussian plume dispersion parameters are estimated and compared with field tracer data collected at 11 sites. The dispersion parameter schemes used in this analysis include Cramer's scheme, suggested for tall stack dispersion estimates, Draxler's scheme, suggested for elevated and surface releases, Pasquill's scheme, suggested for interim use in dispersion estimates, and the Pasquill--Gifford scheme using Turner's technique for assigning stability categories. The schemes suggested by Cramer, Draxler and Pasquill estimate the dispersion parameters using onsite measurements of the vertical and lateral wind-velocity variances at the effective release height. The performances of these schemes in estimating the dispersion parameters are compared with that of the Pasquill--Gifford scheme, using the Prairie Grass and Karlsruhe data. For these two experiments, the estimates of the dispersion parameters using Draxler's scheme correlate better with the measurements than did estimates using the Pasquill--Gifford scheme. Comparison of the dispersion parameter estimates with the measurement suggests that Draxler's scheme for characterizing the dispersion results in the smallest mean fractional error in the estimated dispersion parameters and the smallest variance of the fractional errors
De Götzen , Amalia; Mion , Luca; Tache , Olivier
2007-01-01
International audience; We call sound algorithms the categories of algorithms that deal with digital sound signal. Sound algorithms appeared in the very infancy of computer. Sound algorithms present strong specificities that are the consequence of two dual considerations: the properties of the digital sound signal itself and its uses, and the properties of auditory perception.
Wang, Lui; Bayer, Steven E.
1991-01-01
Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic genetic algorithms concepts are introduced, genetic algorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of genetic algorithm technology.
40Gbit/s MDM-WDM Laguerre-Gaussian Mode with Equalization for Multimode Fiber in Access Networks
Fazea, Yousef; Amphawan, Angela
2018-04-01
Modal dispersion is seen as the primary impairment for multimode fiber. Mode division multiplexing (MDM) is a promising technology that has been realized as a favorable technology for considerably upsurges the capacity and distance of multimode fiber in conjunction with Wavelength Division Multiplexing (WDM) for fiber-to-the-home. This paper reveals the importance of an equalization technique in conjunction with controlling the modes spacing of mode division multiplexing-wavelength division multiplexing of Laguerre-Gaussian modes to alleviate modal dispersion for multimode fiber. The effects of channel spacing of 20 channels MDM-WDM were examined through controlling the azimuthal mode number and the radial mode number of Laguerre-Gaussian modes. A data rate of 40Gbit/s was achieved for a distance of 1,500 m for MDM-WDM.
High-Order Local Pooling and Encoding Gaussians Over a Dictionary of Gaussians.
Li, Peihua; Zeng, Hui; Wang, Qilong; Shiu, Simon C K; Zhang, Lei
2017-07-01
Local pooling (LP) in configuration (feature) space proposed by Boureau et al. explicitly restricts similar features to be aggregated, which can preserve as much discriminative information as possible. At the time it appeared, this method combined with sparse coding achieved competitive classification results with only a small dictionary. However, its performance lags far behind the state-of-the-art results as only the zero-order information is exploited. Inspired by the success of high-order statistical information in existing advanced feature coding or pooling methods, we make an attempt to address the limitation of LP. To this end, we present a novel method called high-order LP (HO-LP) to leverage the information higher than the zero-order one. Our idea is intuitively simple: we compute the first- and second-order statistics per configuration bin and model them as a Gaussian. Accordingly, we employ a collection of Gaussians as visual words to represent the universal probability distribution of features from all classes. Our problem is naturally formulated as encoding Gaussians over a dictionary of Gaussians as visual words. This problem, however, is challenging since the space of Gaussians is not a Euclidean space but forms a Riemannian manifold. We address this challenge by mapping Gaussians into the Euclidean space, which enables us to perform coding with common Euclidean operations rather than complex and often expensive Riemannian operations. Our HO-LP preserves the advantages of the original LP: pooling only similar features and using a small dictionary. Meanwhile, it achieves very promising performance on standard benchmarks, with either conventional, hand-engineered features or deep learning-based features.
Area of isodensity contours in Gaussian and non-Gaussian fields
International Nuclear Information System (INIS)
Ryden, B.S.
1988-01-01
The area of isodensity contours in a smoothed density field can be measured by the contour-crossing statistic N1, the number of times per unit length that a line drawn through the density field pierces an isodensity contour. The contour-crossing statistic distinguishes between Gaussian and non-Gaussian fields and provides a measure of the effective slope of the power spectrum. The statistic is easy to apply and can be used on pencil beams and slices as well as on a three-dimensional field. 10 references
Stochastic differential calculus for Gaussian and non-Gaussian noises: A critical review
Falsone, G.
2018-03-01
In this paper a review of the literature works devoted to the study of stochastic differential equations (SDEs) subjected to Gaussian and non-Gaussian white noises and to fractional Brownian noises is given. In these cases, particular attention must be paid in treating the SDEs because the classical rules of the differential calculus, as the Newton-Leibnitz one, cannot be applied or are applicable with many difficulties. Here all the principal approaches solving the SDEs are reported for any kind of noise, highlighting the negative and positive properties of each one and making the comparisons, where it is possible.
International Nuclear Information System (INIS)
Liu Shixiong; Guo Hong; Liu Mingwei; Wu Guohua
2004-01-01
Propagation characteristics of focused Gaussian beam (FoGB) and fundamental Gaussian beam (FuGB) propagating in vacuum are investigated. Based on the Fourier transform and the angular spectral analysis, the transverse component and the second-order approximate longitudinal component of the electric field are obtained in the paraxial approximation. The electric field components, the phase velocity and the group velocity of FoGB are compared with those of FuGB. The spot size of FoGB is also discussed
International Nuclear Information System (INIS)
Tan, Cheng-Yang; Fermilab
2006-01-01
One common way for measuring the emittance of an electron beam is with the slits method. The usual approach for analyzing the data is to calculate an emittance that is a subset of the parent emittance. This paper shows an alternative way by using the method of correlations which ties the parameters derived from the beamlets to the actual parameters of the parent emittance. For parent distributions that are Gaussian, this method yields exact results. For non-Gaussian beam distributions, this method yields an effective emittance that can serve as a yardstick for emittance comparisons
Photoplethysmogram signal quality estimation using repeated Gaussian filters and cross-correlation
International Nuclear Information System (INIS)
Karlen, W; Kobayashi, K; Dumont, G A; Ansermino, J M
2012-01-01
Pulse oximeters are monitors that noninvasively measure heart rate and blood oxygen saturation (SpO 2 ). Unfortunately, pulse oximetry is prone to artifacts which negatively impact the accuracy of the measurement and can cause a significant number of false alarms. We have developed an algorithm to segment pulse oximetry signals into pulses and estimate the signal quality in real time. The algorithm iteratively calculates a signal quality index (SQI) ranging from 0 to 100. In the presence of artifacts and irregular signal morphology, the algorithm outputs a low SQI number. The pulse segmentation algorithm uses the derivative of the signal to find pulse slopes and an adaptive set of repeated Gaussian filters to select the correct slopes. Cross-correlation of consecutive pulse segments is used to estimate signal quality. Experimental results using two different benchmark data sets showed a good pulse detection rate with a sensitivity of 96.21% and a positive predictive value of 99.22%, which was equivalent to the available reference algorithm. The novel SQI algorithm was effective and produced significantly lower SQI values in the presence of artifacts compared to SQI values during clean signals. The SQI algorithm may help to guide untrained pulse oximeter users and also help in the design of advanced algorithms for generating smart alarms. (paper)
Photoplethysmogram signal quality estimation using repeated Gaussian filters and cross-correlation.
Karlen, W; Kobayashi, K; Ansermino, J M; Dumont, G A
2012-10-01
Pulse oximeters are monitors that noninvasively measure heart rate and blood oxygen saturation (SpO2). Unfortunately, pulse oximetry is prone to artifacts which negatively impact the accuracy of the measurement and can cause a significant number of false alarms. We have developed an algorithm to segment pulse oximetry signals into pulses and estimate the signal quality in real time. The algorithm iteratively calculates a signal quality index (SQI) ranging from 0 to 100. In the presence of artifacts and irregular signal morphology, the algorithm outputs a low SQI number. The pulse segmentation algorithm uses the derivative of the signal to find pulse slopes and an adaptive set of repeated Gaussian filters to select the correct slopes. Cross-correlation of consecutive pulse segments is used to estimate signal quality. Experimental results using two different benchmark data sets showed a good pulse detection rate with a sensitivity of 96.21% and a positive predictive value of 99.22%, which was equivalent to the available reference algorithm. The novel SQI algorithm was effective and produced significantly lower SQI values in the presence of artifacts compared to SQI values during clean signals. The SQI algorithm may help to guide untrained pulse oximeter users and also help in the design of advanced algorithms for generating smart alarms.
International Nuclear Information System (INIS)
Essa, K.S.M.
2009-01-01
The analytical solution of the atmospheric diffusion equation for a point source gives the ground-level concentration profiles. It depends on the wind speed ua nd vertical dispersion coefficient σ z expressed by Pasquill power laws. Both σ z and u are functions of downwind distance, stability and source elevation, while for the ground-level emission u is constant. In the neutral and stable conditions, the Gaussian plume model and finite difference numerical methods with wind speed in power law and the vertical dispersion coefficient in exponential law are estimated. This work shows that the estimated ground-level concentrations of the Gaussian model for high-level source and numerical finite difference method are very match fit to the observed ground-level concentrations of the Gaussian model
International Nuclear Information System (INIS)
Nowak, S.; Orefice, A.
1994-01-01
In today's high frequency systems employed for plasma diagnostics, power heating, and current drive the behavior of the wave beams is appreciably affected by the self-diffraction phenomena due to their narrow collimation. In the present article the three-dimensional propagation of Gaussian beams in inhomogeneous and anisotropic media is analyzed, starting from a properly formulated dispersion relation. Particular attention is paid, in the case of electromagnetic electron cyclotron (EC) waves, to the toroidal geometry characterizing tokamak plasmas, to the power density evolution on the advancing wave fronts, and to the absorption features occurring when a beam crosses an EC resonant layer
Alan Turing and the origins of modern Gaussian elimination
Directory of Open Access Journals (Sweden)
Dopico, Froilán M.
2013-12-01
Full Text Available The solution of a system of linear equations is by far the most important problem in Applied Mathematics. It is important both in itself and because it is an intermediate step in many other important problems. Gaussian elimination is nowadays the standard method for solving this problem numerically on a computer and it was the first numerical algorithm to be subjected to rounding error analysis. In 1948, Alan Turing published a remarkable paper on this topic: “Rounding-off errors in matrix processes” (Quart. J. Mech. Appl. Math. 1, pp. 287-308. In this paper, Turing formulated Gaussian elimination as the matrix LU factorization and introduced the “condition number of a matrix”, both of them fundamental notions of modern Numerical Analysis. In addition, Turing presented an error analysis of Gaussian elimination for general matrices that deeply influenced the spirit of the definitive analysis developed by James Wilkinson in 1961. Alan Turing’s work on Gaussian elimination appears in a fascinating period for modern Numerical Analysis. Other giants of Mathematics, as John von Neumann, Herman Goldstine, and Harold Hotelling were also working in the mid-1940s on Gaussian elimination. The goal of these researchers was to find an efficient and reliable method for solving systems of linear equations in modern “automatic computers”. At that time, it was not clear at all whether Gaussian elimination was a right choice or not. The purpose of this paper is to revise, at an introductory level, the contributions of Alan Turing and other authors to the error analysis of Gaussian elimination, the historical context of these contributions, and their influence on modern Numerical Analysis.La resolución de sistemas de ecuaciones lineales es sin duda el problema más importante en Matemática Aplicada. Es importante en sí mismo y también porque es un paso intermedio en la resolución de muchos otros problemas de gran relevancia. La eliminaci
Non-Gaussianity from Broken Symmetries
Kolb, Edward W; Vallinotto, A; Kolb, Edward W.; Riotto, Antonio; Vallinotto, Alberto
2006-01-01
Recently we studied inflation models in which the inflaton potential is characterized by an underlying approximate global symmetry. In the first work we pointed out that in such a model curvature perturbations are generated after the end of the slow-roll phase of inflation. In this work we develop further the observational implications of the model and compute the degree of non-Gaussianity predicted in the scenario. We find that the corresponding nonlinearity parameter, $f_{NL}$, can be as large as 10^2.
First Passage Time Intervals of Gaussian Processes
Perez, Hector; Kawabata, Tsutomu; Mimaki, Tadashi
1987-08-01
The first passage time problem of a stationary Guassian process is theretically and experimentally studied. Renewal functions are derived for a time-dependent boundary and numerically calculated for a Gaussian process having a seventh-order Butterworth spectrum. The results show a multipeak property not only for the constant boundary but also for a linearly increasing boundary. The first passage time distribution densities were experimentally determined for a constant boundary. The renewal functions were shown to be a fairly good approximation to the distribution density over a limited range.
CMB constraints on running non-Gaussianity
Oppizzi, Filippo; Liguori, Michele; Renzi, Alessandro; Arroja, Frederico; Bartolo, Nicola
2017-01-01
We develop a complete set of tools for CMB forecasting, simulation and estimation of primordial running bispectra, arising from a variety of curvaton and single-field (DBI) models of Inflation. We validate our pipeline using mock CMB running non-Gaussianity realizations and test it on real data by obtaining experimental constraints on the $f_{\\rm NL}$ running spectral index, $n_{\\rm NG}$, using WMAP 9-year data. Our final bounds (68\\% C.L.) read $-0.3< n_{\\rm NG}
Optical trapping with Super-Gaussian beams
CSIR Research Space (South Africa)
Mc
2013-04-01
Full Text Available stream_source_info McLaren1_2013.pdf.txt stream_content_type text/plain stream_size 2236 Content-Encoding UTF-8 stream_name McLaren1_2013.pdf.txt Content-Type text/plain; charset=UTF-8 JT2A.34.pdf Optics in the Life... Sciences Congress Technical Digest © 2013 The Optical Society (OSA) Optical trapping with Super-Gaussian beams Melanie McLaren, Thulile Khanyile, Patience Mthunzi and Andrew Forbes* National Laser Centre, Council for Scientific and Industrial Research...
Bregman Cost for Non-Gaussian Noise
DEFF Research Database (Denmark)
Burger, Martin; Dong, Yiqiu; Sciacchitano, Federica
estimator for the Bregman cost if the image is corrupted by Gaussian noise. In this work we extend this result to other noise models with log-concave likelihood density, by introducing two related Bregman cost functions for which the CM and the MAP estimates are proper Bayes estima-tors. Moreover, we also....... From a theoretical point of view it has been argued that the MAP estimate is only in an asymptotic sense a Bayes estimator for the uniform cost function, while the CM estimate is a Bayes estimator for the means squared cost function. Recently, it has been proven that the MAP estimate is a proper Bayes...
Wilhelm, Jan; Seewald, Patrick; Del Ben, Mauro; Hutter, Jürg
2016-12-13
We present an algorithm for computing the correlation energy in the random phase approximation (RPA) in a Gaussian basis requiring [Formula: see text] operations and [Formula: see text] memory. The method is based on the resolution of the identity (RI) with the overlap metric, a reformulation of RI-RPA in the Gaussian basis, imaginary time, and imaginary frequency integration techniques, and the use of sparse linear algebra. Additional memory reduction without extra computations can be achieved by an iterative scheme that overcomes the memory bottleneck of canonical RPA implementations. We report a massively parallel implementation that is the key for the application to large systems. Finally, cubic-scaling RPA is applied to a thousand water molecules using a correlation-consistent triple-ζ quality basis.
A Monte Carlo simulation model for stationary non-Gaussian processes
DEFF Research Database (Denmark)
Grigoriu, M.; Ditlevsen, Ove Dalager; Arwade, S. R.
2003-01-01
includes translation processes and is useful for both Monte Carlo simulation and analytical studies. As for translation processes, the mixture of translation processes can have a wide range of marginal distributions and correlation functions. Moreover, these processes can match a broader range of second...... athe proposed Monte Carlo algorithm and compare features of translation processes and mixture of translation processes. Keywords: Monte Carlo simulation, non-Gaussian processes, sampling theorem, stochastic processes, translation processes......A class of stationary non-Gaussian processes, referred to as the class of mixtures of translation processes, is defined by their finite dimensional distributions consisting of mixtures of finite dimensional distributions of translation processes. The class of mixtures of translation processes...
International Nuclear Information System (INIS)
Bertschinger, E.
1987-01-01
Path integrals may be used to describe the statistical properties of a random field such as the primordial density perturbation field. In this framework the probability distribution is given for a Gaussian random field subjected to constraints such as the presence of a protovoid or supercluster at a specific location in the initial conditions. An algorithm has been constructed for generating samples of a constrained Gaussian random field on a lattice using Monte Carlo techniques. The method makes possible a systematic study of the density field around peaks or other constrained regions in the biased galaxy formation scenario, and it is effective for generating initial conditions for N-body simulations with rare objects in the computational volume. 21 references
Speech Enhancement by MAP Spectral Amplitude Estimation Using a Super-Gaussian Speech Model
Directory of Open Access Journals (Sweden)
Lotter Thomas
2005-01-01
Full Text Available This contribution presents two spectral amplitude estimators for acoustical background noise suppression based on maximum a posteriori estimation and super-Gaussian statistical modelling of the speech DFT amplitudes. The probability density function of the speech spectral amplitude is modelled with a simple parametric function, which allows a high approximation accuracy for Laplace- or Gamma-distributed real and imaginary parts of the speech DFT coefficients. Also, the statistical model can be adapted to optimally fit the distribution of the speech spectral amplitudes for a specific noise reduction system. Based on the super-Gaussian statistical model, computationally efficient maximum a posteriori speech estimators are derived, which outperform the commonly applied Ephraim-Malah algorithm.
On signal design by the R/0/ criterion for non-white Gaussian noise channels
Bordelon, D. L.
1977-01-01
The use of the cut-off rate criterion for modulation system design is investigated for channels with non-white Gaussian noise. A signal space representation of the waveform channel is developed, and the cut-off rate for vector channels with additive non-white Gaussian noise and unquantized demodulation is derived. When the signal input to the channel is a continuous random vector, maximization of the cut-off rate with constrained average signal energy leads to a water-filling interpretation of optimal energy distribution in signal space. The necessary condition for a finite signal set to maximize the cut-off rate with constrained energy and an equally likely probability assignment of signal vectors is presented, and an algorithm is outlined for numerically computing the optimum signal set. As an example, the rectangular signal set which has the water-filling average energy distribution and the optimum rectangular set are compared.
Open Burn/Open Detonation Dispersion Model (OBODM) User's Guide. Volume I. User's Instructions
National Research Council Canada - National Science Library
Bjorklund, Jay
1998-01-01
...) of obsolete munitions and solid propellants. OBODM uses loud/plume rise, dispersion, and deposition algorithms taken from existing models for instantaneous and quasi-continuous sources to predict the downwind transport and dispersion...
Characterisation of random Gaussian and non-Gaussian stress processes in terms of extreme responses
Directory of Open Access Journals (Sweden)
Colin Bruno
2015-01-01
Full Text Available In the field of military land vehicles, random vibration processes generated by all-terrain wheeled vehicles in motion are not classical stochastic processes with a stationary and Gaussian nature. Non-stationarity of processes induced by the variability of the vehicle speed does not form a major difficulty because the designer can have good control over the vehicle speed by characterising the histogram of instantaneous speed of the vehicle during an operational situation. Beyond this non-stationarity problem, the hard point clearly lies in the fact that the random processes are not Gaussian and are generated mainly by the non-linear behaviour of the undercarriage and the strong occurrence of shocks generated by roughness of the terrain. This non-Gaussian nature is expressed particularly by very high flattening levels that can affect the design of structures under extreme stresses conventionally acquired by spectral approaches, inherent to Gaussian processes and based essentially on spectral moments of stress processes. Due to these technical considerations, techniques for characterisation of random excitation processes generated by this type of carrier need to be changed, by proposing innovative characterisation methods based on time domain approaches as described in the body of the text rather than spectral domain approaches.
DEFF Research Database (Denmark)
Møller, Jesper; Jacobsen, Robert Dahl
We introduce a promising alternative to the usual hidden Markov tree model for Gaussian wavelet coefficients, where their variances are specified by the hidden states and take values in a finite set. In our new model, the hidden states have a similar dependence structure but they are jointly Gaus...
DEFF Research Database (Denmark)
Jacobsen, Christian Robert Dahl; Møller, Jesper
2017-01-01
We introduce new estimation methods for a subclass of the Gaussian scale mixture models for wavelet trees by Wainwright, Simoncelli and Willsky that rely on modern results for composite likelihoods and approximate Bayesian inference. Our methodology is illustrated for denoising and edge detection...
Comparison of Gaussian and non-Gaussian Atmospheric Profile Retrievals from Satellite Microwave Data
Kliewer, A.; Forsythe, J. M.; Fletcher, S. J.; Jones, A. S.
2017-12-01
The Cooperative Institute for Research in the Atmosphere at Colorado State University has recently developed two different versions of a mixed-distribution (lognormal combined with a Gaussian) based microwave temperature and mixing ratio retrieval system as well as the original Gaussian-based approach. These retrieval systems are based upon 1DVAR theory but have been adapted to use different descriptive statistics of the lognormal distribution to minimize the background errors. The input radiance data is from the AMSU-A and MHS instruments on the NOAA series of spacecraft. To help illustrate how the three retrievals are affected by the change in the distribution we are in the process of creating a new website to show the output from the different retrievals. Here we present initial results from different dynamical situations to show how the tool could be used by forecasters as well as for educators. However, as the new retrieved values are from a non-Gaussian based 1DVAR then they will display non-Gaussian behaviors that need to pass a quality control measure that is consistent with this distribution, and these new measures are presented here along with initial results for checking the retrievals.
DEFF Research Database (Denmark)
Matacchiera, F.; Manes, C.; Beaven, R. P.
2018-01-01
that measurements are taken where the plumes of a released tracer-gas and landfill-gas are well-mixed. However, the distance at which full mixing of the gases occurs is generally unknown prior to any experimental campaign. To overcome this problem the present paper demonstrates that, for any specific TDM...
Gaussian elimination is not optimal, revisited
DEFF Research Database (Denmark)
Macedo, Hugo Daniel
2016-01-01
We refactor the universal law for the tensor product to express matrix multiplication as the product . MN of two matrices . M and . N thus making possible to use such matrix product to encode and transform algorithms performing matrix multiplication using techniques from linear algebra. We explore......, and how to further apply it in the derivation of linear algebra algorithms....
Theoretical analysis of non-Gaussian heterogeneity effects on subsurface flow and transport
Riva, Monica; Guadagnini, Alberto; Neuman, Shlomo P.
2017-04-01
upon perturbing K=eY to second order in σY even as the corresponding series diverges. Our analysis is rendered mathematically tractable by considering mean-uniform steady state flow in an unbounded, two-dimensional domain of mildly heterogeneous Y with a single-scale function G having an isotropic exponential covariance. Results consist of expressions for (a) lead-order autocovariance and cross-covariance functions of hydraulic head, velocity, and advective particle displacement and (b) analogues of preasymptotic as well as asymptotic Fickian dispersion coefficients. We compare these theoretically and graphically with corresponding expressions developed in the literature for Gaussian Y. We find the former to differ from the latter by a factor k = /2 ( denoting ensemble expectation) and the GSG covariance of longitudinal velocity to contain an additional nugget term depending on this same factor. In the limit as Y becomes Gaussian, k reduces to one and the nugget term drops out.
Joux, Antoine
2009-01-01
Illustrating the power of algorithms, Algorithmic Cryptanalysis describes algorithmic methods with cryptographically relevant examples. Focusing on both private- and public-key cryptographic algorithms, it presents each algorithm either as a textual description, in pseudo-code, or in a C code program.Divided into three parts, the book begins with a short introduction to cryptography and a background chapter on elementary number theory and algebra. It then moves on to algorithms, with each chapter in this section dedicated to a single topic and often illustrated with simple cryptographic applic
A Gaussian Approximation Potential for Silicon
Bernstein, Noam; Bartók, Albert; Kermode, James; Csányi, Gábor
We present an interatomic potential for silicon using the Gaussian Approximation Potential (GAP) approach, which uses the Gaussian process regression method to approximate the reference potential energy surface as a sum of atomic energies. Each atomic energy is approximated as a function of the local environment around the atom, which is described with the smooth overlap of atomic environments (SOAP) descriptor. The potential is fit to a database of energies, forces, and stresses calculated using density functional theory (DFT) on a wide range of configurations from zero and finite temperature simulations. These include crystalline phases, liquid, amorphous, and low coordination structures, and diamond-structure point defects, dislocations, surfaces, and cracks. We compare the results of the potential to DFT calculations, as well as to previously published models including Stillinger-Weber, Tersoff, modified embedded atom method (MEAM), and ReaxFF. We show that it is very accurate as compared to the DFT reference results for a wide range of properties, including low energy bulk phases, liquid structure, as well as point, line, and plane defects in the diamond structure.
Statistics of peaks of Gaussian random fields
International Nuclear Information System (INIS)
Bardeen, J.M.; Bond, J.R.; Kaiser, N.; Szalay, A.S.; Stanford Univ., CA; California Univ., Berkeley; Cambridge Univ., England; Fermi National Accelerator Lab., Batavia, IL)
1986-01-01
A set of new mathematical results on the theory of Gaussian random fields is presented, and the application of such calculations in cosmology to treat questions of structure formation from small-amplitude initial density fluctuations is addressed. The point process equation is discussed, giving the general formula for the average number density of peaks. The problem of the proper conditional probability constraints appropriate to maxima are examined using a one-dimensional illustration. The average density of maxima of a general three-dimensional Gaussian field is calculated as a function of heights of the maxima, and the average density of upcrossing points on density contour surfaces is computed. The number density of peaks subject to the constraint that the large-scale density field be fixed is determined and used to discuss the segregation of high peaks from the underlying mass distribution. The machinery to calculate n-point peak-peak correlation functions is determined, as are the shapes of the profiles about maxima. 67 references
Overlay Spectrum Sharing using Improper Gaussian Signaling
Amin, Osama
2016-11-30
Improper Gaussian signaling (IGS) scheme has been recently shown to provide performance improvements in interference limited networks as opposed to the conventional proper Gaussian signaling (PGS) scheme. In this paper, we implement the IGS scheme in overlay cognitive radio system, where the secondary transmitter broadcasts a mixture of two different signals. The first signal is selected from the PGS scheme to match the primary message transmission. On the other hand, the second signal is chosen to be from the IGS scheme in order to reduce the interference effect on the primary receiver. We then optimally design the overlay cognitive radio to maximize the secondary link achievable rate while satisfying the primary network quality of service requirements. In particular, we consider full and partial channel knowledge scenarios and derive the feasibility conditions of operating the overlay cognitive radio systems. Moreover, we derive the superiority conditions of the IGS schemes over the PGS schemes supported with closed form expressions for the corresponding power distribution and the circularity coefficient and parameters. Simulation results are provided to support our theoretical derivations.
Versatile Gaussian probes for squeezing estimation
Rigovacca, Luca; Farace, Alessandro; Souza, Leonardo A. M.; De Pasquale, Antonella; Giovannetti, Vittorio; Adesso, Gerardo
2017-05-01
We consider an instance of "black-box" quantum metrology in the Gaussian framework, where we aim to estimate the amount of squeezing applied on an input probe, without previous knowledge on the phase of the applied squeezing. By taking the quantum Fisher information (QFI) as the figure of merit, we evaluate its average and variance with respect to this phase in order to identify probe states that yield good precision for many different squeezing directions. We first consider the case of single-mode Gaussian probes with the same energy, and find that pure squeezed states maximize the average quantum Fisher information (AvQFI) at the cost of a performance that oscillates strongly as the squeezing direction is changed. Although the variance can be brought to zero by correlating the probing system with a reference mode, the maximum AvQFI cannot be increased in the same way. A different scenario opens if one takes into account the effects of photon losses: coherent states represent the optimal single-mode choice when losses exceed a certain threshold and, moreover, correlated probes can now yield larger AvQFI values than all single-mode states, on top of having zero variance.
Finite Range Decomposition of Gaussian Processes
Brydges, C D; Mitter, P K
2003-01-01
Let $D$ be the finite difference Laplacian associated to the lattice $bZ^{d}$. For dimension $dge 3$, $age 0$ and $L$ a sufficiently large positive dyadic integer, we prove that the integral kernel of the resolvent $G^{a}:=(a-D)^{-1}$ can be decomposed as an infinite sum of positive semi-definite functions $ V_{n} $ of finite range, $ V_{n} (x-y) = 0$ for $|x-y|ge O(L)^{n}$. Equivalently, the Gaussian process on the lattice with covariance $G^{a}$ admits a decomposition into independent Gaussian processes with finite range covariances. For $a=0$, $ V_{n} $ has a limiting scaling form $L^{-n(d-2)}Gamma_{ c,ast }{bigl (frac{x-y}{ L^{n}}bigr )}$ as $nrightarrow infty$. As a corollary, such decompositions also exist for fractional powers $(-D)^{-alpha/2}$, $0
A Local Scalable Distributed EM Algorithm for Large P2P Networks
National Aeronautics and Space Administration — his paper describes a local and distributed expectation maximization algorithm for learning parameters of Gaussian mixture models (GMM) in large peer-to-peer (P2P)...
A Local Scalable Distributed Expectation Maximization Algorithm for Large Peer-to-Peer Networks
National Aeronautics and Space Administration — This paper describes a local and distributed expectation maximization algorithm for learning parameters of Gaussian mixture models (GMM) in large peer-to-peer (P2P)...
Another higher order Langevin algorithm for QCD
International Nuclear Information System (INIS)
Kronfeld, A.S.
1986-01-01
This note provides an algorithm for integrating the Langevin equation which is second order. It introduces a term into the drift force which is a product of the Gaussian noise and a second derivative of the action. The specific application presented here is for nonabelian gauge theories interacting with fermions, e.g. QCD, for which it requires less memory than the Runge-Kutta algorithm of the same order. The memory and computational requirements of Euler, Runge-Kutta, and the present algorithm are compared. (orig.)
Variable Selection for Nonparametric Gaussian Process Priors: Models and Computational Strategies.
Savitsky, Terrance; Vannucci, Marina; Sha, Naijun
2011-02-01
This paper presents a unified treatment of Gaussian process models that extends to data from the exponential dispersion family and to survival data. Our specific interest is in the analysis of data sets with predictors that have an a priori unknown form of possibly nonlinear associations to the response. The modeling approach we describe incorporates Gaussian processes in a generalized linear model framework to obtain a class of nonparametric regression models where the covariance matrix depends on the predictors. We consider, in particular, continuous, categorical and count responses. We also look into models that account for survival outcomes. We explore alternative covariance formulations for the Gaussian process prior and demonstrate the flexibility of the construction. Next, we focus on the important problem of selecting variables from the set of possible predictors and describe a general framework that employs mixture priors. We compare alternative MCMC strategies for posterior inference and achieve a computationally efficient and practical approach. We demonstrate performances on simulated and benchmark data sets.
Directory of Open Access Journals (Sweden)
Georgios C Manikis
Full Text Available The purpose of this study was to compare the performance of four diffusion models, including mono and bi-exponential both Gaussian and non-Gaussian models, in diffusion weighted imaging of rectal cancer.Nineteen patients with rectal adenocarcinoma underwent MRI examination of the rectum before chemoradiation therapy including a 7 b-value diffusion sequence (0, 25, 50, 100, 500, 1000 and 2000 s/mm2 at a 1.5T scanner. Four different diffusion models including mono- and bi-exponential Gaussian (MG and BG and non-Gaussian (MNG and BNG were applied on whole tumor volumes of interest. Two different statistical criteria were recruited to assess their fitting performance, including the adjusted-R2 and Root Mean Square Error (RMSE. To decide which model better characterizes rectal cancer, model selection was relied on Akaike Information Criteria (AIC and F-ratio.All candidate models achieved a good fitting performance with the two most complex models, the BG and the BNG, exhibiting the best fitting performance. However, both criteria for model selection indicated that the MG model performed better than any other model. In particular, using AIC Weights and F-ratio, the pixel-based analysis demonstrated that tumor areas better described by the simplest MG model in an average area of 53% and 33%, respectively. Non-Gaussian behavior was illustrated in an average area of 37% according to the F-ratio, and 7% using AIC Weights. However, the distributions of the pixels best fitted by each of the four models suggest that MG failed to perform better than any other model in all patients, and the overall tumor area.No single diffusion model evaluated herein could accurately describe rectal tumours. These findings probably can be explained on the basis of increased tumour heterogeneity, where areas with high vascularity could be fitted better with bi-exponential models, and areas with necrosis would mostly follow mono-exponential behavior.
Relative entropy as a measure of entanglement for Gaussian states
Institute of Scientific and Technical Information of China (English)
Lu Huai-Xin; Zhao Bo
2006-01-01
In this paper, we derive an explicit analytic expression of the relative entropy between two general Gaussian states. In the restriction of the set for Gaussian states and with the help of relative entropy formula and Peres-Simon separability criterion, one can conveniently obtain the relative entropy entanglement for Gaussian states. As an example,the relative entanglement for a two-mode squeezed thermal state has been obtained.
Automatic arrival time detection for earthquakes based on Modified Laplacian of Gaussian filter
Saad, Omar M.; Shalaby, Ahmed; Samy, Lotfy; Sayed, Mohammed S.
2018-04-01
Precise identification of onset time for an earthquake is imperative in the right figuring of earthquake's location and different parameters that are utilized for building seismic catalogues. P-wave arrival detection of weak events or micro-earthquakes cannot be precisely determined due to background noise. In this paper, we propose a novel approach based on Modified Laplacian of Gaussian (MLoG) filter to detect the onset time even in the presence of very weak signal-to-noise ratios (SNRs). The proposed algorithm utilizes a denoising-filter algorithm to smooth the background noise. In the proposed algorithm, we employ the MLoG mask to filter the seismic data. Afterward, we apply a Dual-threshold comparator to detect the onset time of the event. The results show that the proposed algorithm can detect the onset time for micro-earthquakes accurately, with SNR of -12 dB. The proposed algorithm achieves an onset time picking accuracy of 93% with a standard deviation error of 0.10 s for 407 field seismic waveforms. Also, we compare the results with short and long time average algorithm (STA/LTA) and the Akaike Information Criterion (AIC), and the proposed algorithm outperforms them.
HGSYSTEMUF6, Simulating Dispersion Due to Atmospheric Release of Uranium Hexafluoride (UF6)
International Nuclear Information System (INIS)
Hanna, G; Chang, J.C.; Zhang, J.X.; Bloom, S.G.; Goode, W.D. Jr; Lombardi, D.A.; Yambert, M.W.
2001-01-01
1 - Description of program or function: HGSYSTEMUF6 is a suite of models designed for use in estimating consequences associated with accidental, atmospheric release of Uranium Hexafluoride (UF 6 ) and its reaction products, namely Hydrogen Fluoride (HF), and other non-reactive contaminants which are either negatively, neutrally, or positively buoyant. It is based on HGSYSTEM Version 3.0 of Shell Research LTD., and contains specific algorithms for the treatment of UF 6 chemistry and thermodynamics. HGSYSTEMUF6 contains algorithms for the treatment of dense gases, dry and wet deposition, effects due to the presence of buildings (canyon and wake), plume lift-off, and the effects of complex terrain. The models components of the suite include (1) AEROPLUME/RK, used to model near-field dispersion from pressurized two-phase jet releases of UF6 and its reaction products, (2) HEGADAS/UF6 for simulating dense, ground based release of UF 6 , (3) PGPLUME for simulation of passive, neutrally buoyant plumes (4) UF6Mixer for modeling warm, potentially reactive, ground-level releases of UF 6 from buildings, and (5) WAKE, used to model elevated and ground-level releases into building wake cavities of non-reactive plumes that are either neutrally or positively buoyant. 2 - Methods: The atmospheric release and transport of UF 6 is a complicated process involving the interaction between dispersion, chemical and thermodynamic processes. This process is characterized by four separate stages (flash, sublimation, chemical reaction entrainment and passive dispersion) in which one or more of these processes dominate. The various models contained in the suite are applicable to one or more of these stages. For example, for modeling reactive, multiphase releases of UF 6 , the AEROPLUME/RK component employs a process-splitting scheme which numerically integrates the differential equations governing dispersion, UF 6 chemistry, and thermodynamics. This algorithm is based on the assumption that
Prediction and retrodiction with continuously monitored Gaussian states
DEFF Research Database (Denmark)
Zhang, Jinglei; Mølmer, Klaus
2017-01-01
Gaussian states of quantum oscillators are fully characterized by the mean values and the covariance matrix of their quadrature observables. We consider the dynamics of a system of oscillators subject to interactions, damping, and continuous probing which maintain their Gaussian state property......(t)$ to Gaussian states implies that the matrix $E(t)$ is also fully characterized by a vector of mean values and a covariance matrix. We derive the dynamical equations for these quantities and we illustrate their use in the retrodiction of measurements on Gaussian systems....
Geometry of perturbed Gaussian states and quantum estimation
International Nuclear Information System (INIS)
Genoni, Marco G; Giorda, Paolo; Paris, Matteo G A
2011-01-01
We address the non-Gaussianity (nG) of states obtained by weakly perturbing a Gaussian state and investigate the relationships with quantum estimation. For classical perturbations, i.e. perturbations to eigenvalues, we found that the nG of the perturbed state may be written as the quantum Fisher information (QFI) distance minus a term depending on the infinitesimal energy change, i.e. it provides a lower bound to statistical distinguishability. Upon moving on isoenergetic surfaces in a neighbourhood of a Gaussian state, nG thus coincides with a proper distance in the Hilbert space and exactly quantifies the statistical distinguishability of the perturbations. On the other hand, for perturbations leaving the covariance matrix unperturbed, we show that nG provides an upper bound to the QFI. Our results show that the geometry of non-Gaussian states in the neighbourhood of a Gaussian state is definitely not trivial and cannot be subsumed by a differential structure. Nevertheless, the analysis of perturbations to a Gaussian state reveals that nG may be a resource for quantum estimation. The nG of specific families of perturbed Gaussian states is analysed in some detail with the aim of finding the maximally non-Gaussian state obtainable from a given Gaussian one. (fast track communication)
Gaussian polynomials and content ideal in trivial extensions
International Nuclear Information System (INIS)
Bakkari, C.; Mahdou, N.
2006-12-01
The goal of this paper is to exhibit a class of Gaussian non-coherent rings R (with zero-divisors) such that wdim(R) = ∞ and fPdim(R) is always at most one and also exhibits a new class of rings (with zerodivisors) which are neither locally Noetherian nor locally domain where Gaussian polynomials have a locally principal content. For this purpose, we study the possible transfer of the 'Gaussian' property and the property 'the content ideal of a Gaussian polynomial is locally principal' to various trivial extension contexts. This article includes a brief discussion of the scopes and limits of our result. (author)
Energy Technology Data Exchange (ETDEWEB)
Wang, Yuan-Mei; Li, Jun-Gang, E-mail: jungl@bit.edu.cn; Zou, Jian
2017-06-15
Highlights: • Adaptive measurement strategy is used to detect the presence of a magnetic field. • Gaussian Ornstein–Uhlenbeck noise and non-Gaussian noise have been considered. • Weaker magnetic fields may be more easily detected than some stronger ones. - Abstract: By using the adaptive measurement method we study how to detect whether a weak magnetic field is actually present or not under Gaussian noise and non-Gaussian noise. We find that the adaptive measurement method can effectively improve the detection accuracy. For the case of Gaussian noise, we find the stronger the magnetic field strength, the easier for us to detect the magnetic field. Counterintuitively, for non-Gaussian noise, some weaker magnetic fields are more likely to be detected rather than some stronger ones. Finally, we give a reasonable physical interpretation.
Wu, Pingping; Tan, Handong; Peng, Miao; Ma, Huan; Wang, Mao
2018-05-01
Magnetotellurics and seismic surface waves are two prominent geophysical methods for deep underground exploration. Joint inversion of these two datasets can help enhance the accuracy of inversion. In this paper, we describe a method for developing an improved multi-objective genetic algorithm (NSGA-SBX) and applying it to two numerical tests to verify the advantages of the algorithm. Our findings show that joint inversion with the NSGA-SBX method can improve the inversion results by strengthening structural coupling when the discontinuities of the electrical and velocity models are consistent, and in case of inconsistent discontinuities between these models, joint inversion can retain the advantages of individual inversions. By applying the algorithm to four detection points along the Longmenshan fault zone, we observe several features. The Sichuan Basin demonstrates low S-wave velocity and high conductivity in the shallow crust probably due to thick sedimentary layers. The eastern margin of the Tibetan Plateau shows high velocity and high resistivity in the shallow crust, while two low velocity layers and a high conductivity layer are observed in the middle lower crust, probably indicating the mid-crustal channel flow. Along the Longmenshan fault zone, a high conductivity layer from 8 to 20 km is observed beneath the northern segment and decreases with depth beneath the middle segment, which might be caused by the elevated fluid content of the fault zone.
Tel, G.
We define the notion of total algorithms for networks of processes. A total algorithm enforces that a "decision" is taken by a subset of the processes, and that participation of all processes is required to reach this decision. Total algorithms are an important building block in the design of
New Information Dispersal Techniques for Trustworthy Computing
Parakh, Abhishek
2011-01-01
Information dispersal algorithms (IDA) are used for distributed data storage because they simultaneously provide security, reliability and space efficiency, constituting a trustworthy computing framework for many critical applications, such as cloud computing, in the information society. In the most general sense, this is achieved by dividing data…
Transport and Dispersion of Nanoparticles in Periodic Nanopost Arrays
He, Kai; Retterer, Scott T.; Srijanto, Bernadeta R.; Conrad, Jacinta C.; Krishnamoorti, Ramanan
2014-01-01
Nanoparticles transported through highly confined porous media exhibit faster breakthrough than small molecule tracers. Despite important technological applications in advanced materials, human health, energy, and environment, the microscale mechanisms leading to early breakthrough have not been identified. Here, we measure dispersion of nanoparticles at the single-particle scale in regular arrays of nanoposts and show that for highly confined flows of dilute suspensions of nanoparticles the longitudinal and transverse velocities exhibit distinct scaling behaviors. The distributions of transverse particle velocities become narrower and more non-Gaussian when the particles are strongly confined. As a result, the transverse dispersion of highly confined nanoparticles at low Péclet numbers is significantly less important than longitudinal dispersion, leading to early breakthrough. This finding suggests a fundamental mechanism by which to control dispersion and thereby improve efficacy of nanoparticles applied for advanced polymer nanocomposites, drug delivery, hydrocarbon production, and environmental remediation. © 2014 American Chemical Society.
Transport and Dispersion of Nanoparticles in Periodic Nanopost Arrays
He, Kai
2014-05-27
Nanoparticles transported through highly confined porous media exhibit faster breakthrough than small molecule tracers. Despite important technological applications in advanced materials, human health, energy, and environment, the microscale mechanisms leading to early breakthrough have not been identified. Here, we measure dispersion of nanoparticles at the single-particle scale in regular arrays of nanoposts and show that for highly confined flows of dilute suspensions of nanoparticles the longitudinal and transverse velocities exhibit distinct scaling behaviors. The distributions of transverse particle velocities become narrower and more non-Gaussian when the particles are strongly confined. As a result, the transverse dispersion of highly confined nanoparticles at low Péclet numbers is significantly less important than longitudinal dispersion, leading to early breakthrough. This finding suggests a fundamental mechanism by which to control dispersion and thereby improve efficacy of nanoparticles applied for advanced polymer nanocomposites, drug delivery, hydrocarbon production, and environmental remediation. © 2014 American Chemical Society.
Secure Degrees of Freedom of the Gaussian Z Channel with Single Antenna
Directory of Open Access Journals (Sweden)
Xianzhong XIE
2014-03-01
Full Text Available This paper presents the secrecy capacity and the secure degrees of freedom of Gaussian Z channel with single antenna and confidential information. Firstly, we analysis the secrecy capacity and the upper bound of secure degrees of freedom of this channel in theory. Then, we respectively discuss the security pre-coding scheme for real Gaussian channel model and frequency selection channel model. Under the first model, through real interference alignment and cooperative jamming, we obtain the secrecy capacity and secure degrees of freedom, proving that it can reach the upper bound of secure degrees of freedom in theory. While, under the second one, a strong security pre-coding algorithm is proposed, which is based on the fact that sparse matrix has strong hash property. Next, we arrange interference with interference alignment and the receivers process their received signal through zero forcing algorithm. At last, the messages are reconstructed with maximum likelihood decoding, where it shows that the algorithm can asymptotically achieve the optimal secrecy capacity.
Yu, Wangyang; Chen, Xiangguang; Wu, Lei
2015-04-01
Passive millimeter wave (PMMW) imaging has become one of the most effective means to detect the objects concealed under clothing. Due to the limitations of the available hardware and the inherent physical properties of PMMW imaging systems, images often exhibit poor contrast and low signal-to-noise ratios. Thus, it is difficult to achieve ideal results by using a general segmentation algorithm. In this paper, an advanced Gaussian Mixture Model (GMM) algorithm for the segmentation of concealed objects in PMMW images is presented. Our work is concerned with the fact that the GMM is a parametric statistical model, which is often used to characterize the statistical behavior of images. Our approach is three-fold: First, we remove the noise from the image using both a notch reject filter and a total variation filter. Next, we use an adaptive parameter initialization GMM algorithm (APIGMM) for simulating the histogram of images. The APIGMM provides an initial number of Gaussian components and start with more appropriate parameter. Bayesian decision is employed to separate the pixels of concealed objects from other areas. At last, the confidence interval (CI) method, alongside local gradient information, is used to extract the concealed objects. The proposed hybrid segmentation approach detects the concealed objects more accurately, even compared to two other state-of-the-art segmentation methods.
Moenster, Mathias; Steinmeyer, Günter; Iliew, Rumen; Lederer, Falk; Petermann, Klaus
2006-11-15
For optical fibers exhibiting a radially symmetric refractive index profile, there exists an analytical relation that connects waveguide dispersion and the Petermann-II mode field radius. We extend the usefulness of this relation to the nonradially symmetric case of microstructure fibers in the anomalous dispersion regime, yielding a simple relation between dispersion and effective mode field area. Assuming a Gaussian mode distribution, we derive a fundamental upper limit for the effective mode field area that is required to obtain a certain amount of anomalous waveguide dispersion. This relation is demonstrated to show excellent agreement for fiber designs suited for supercontinuum generation and soliton lasers in the near infrared.
Yan, Yuan
2017-07-13
Gaussian likelihood inference has been studied and used extensively in both statistical theory and applications due to its simplicity. However, in practice, the assumption of Gaussianity is rarely met in the analysis of spatial data. In this paper, we study the effect of non-Gaussianity on Gaussian likelihood inference for the parameters of the Matérn covariance model. By using Monte Carlo simulations, we generate spatial data from a Tukey g-and-h random field, a flexible trans-Gaussian random field, with the Matérn covariance function, where g controls skewness and h controls tail heaviness. We use maximum likelihood based on the multivariate Gaussian distribution to estimate the parameters of the Matérn covariance function. We illustrate the effects of non-Gaussianity of the data on the estimated covariance function by means of functional boxplots. Thanks to our tailored simulation design, a comparison of the maximum likelihood estimator under both the increasing and fixed domain asymptotics for spatial data is performed. We find that the maximum likelihood estimator based on Gaussian likelihood is overall satisfying and preferable than the non-distribution-based weighted least squares estimator for data from the Tukey g-and-h random field. We also present the result for Gaussian kriging based on Matérn covariance estimates with data from the Tukey g-and-h random field and observe an overall satisfactory performance.
Yan, Yuan; Genton, Marc G.
2017-01-01
Gaussian likelihood inference has been studied and used extensively in both statistical theory and applications due to its simplicity. However, in practice, the assumption of Gaussianity is rarely met in the analysis of spatial data. In this paper, we study the effect of non-Gaussianity on Gaussian likelihood inference for the parameters of the Matérn covariance model. By using Monte Carlo simulations, we generate spatial data from a Tukey g-and-h random field, a flexible trans-Gaussian random field, with the Matérn covariance function, where g controls skewness and h controls tail heaviness. We use maximum likelihood based on the multivariate Gaussian distribution to estimate the parameters of the Matérn covariance function. We illustrate the effects of non-Gaussianity of the data on the estimated covariance function by means of functional boxplots. Thanks to our tailored simulation design, a comparison of the maximum likelihood estimator under both the increasing and fixed domain asymptotics for spatial data is performed. We find that the maximum likelihood estimator based on Gaussian likelihood is overall satisfying and preferable than the non-distribution-based weighted least squares estimator for data from the Tukey g-and-h random field. We also present the result for Gaussian kriging based on Matérn covariance estimates with data from the Tukey g-and-h random field and observe an overall satisfactory performance.
International Nuclear Information System (INIS)
Boerzsoenyi, A.; Meroe, M.
2010-01-01
Complete text of publication follows. There is an increasing demand for active and precise dispersion control of ultrashort laser pulses. In chirped pulse amplification (CPA) laser systems, the dispersion of the optical elements of the laser has to be compensated at least to the fourth order to obtain high temporal contrast compressed pulses. Nowadays the most convenient device for active and programmable control of spectral phase and amplitude of broadband laser pulses is the acousto-optic programmable dispersive filter (AOPDF), claimed to be able to adjust the spectral phase up to the fourth order. Although it has been widely used, surprisingly enough there has been only a single, low resolution measurement reported on the accuracy of the induced spectral phase shift of the device. In our paper we report on the first systematic experiment aiming at the precise characterization of an AOPDF device. In the experiment the spectral phase shift of the AOPDF device was measured by spectrally and spatially resolved interferometry, which is especially powerful tool to determine small dispersion values with high accuracy. Besides the spectral phase dispersion, we measured both the propagation direction angular dispersion (PDAD) and the phase front angular dispersion (PhFAD). Although the two quantities are equal for plane waves, there may be noticeable difference for Gaussian pulses. PDAD was determined simply by focusing the beam on the slit of an imaging spectrograph, while PhFAD was measured by the use of an inverted Mach-Zehnder interferometer and an imaging spectrograph. In the measurements, the spectral phase shift and both types of angular dispersion have been recorded upon the systematic change of all the accessible functions of the acousto-optic programmable dispersive filter. The measured values of group delay dispersion (GDD) and third order dispersion (TOD) have been found to agree with the preset values within the error of the measurement (1 fs 2 and 10 fs 3
Optical vortex scanning inside the Gaussian beam
International Nuclear Information System (INIS)
Masajada, J; Leniec, M; Augustyniak, I
2011-01-01
We discussed a new scanning method for optical vortex-based scanning microscopy. The optical vortex is introduced into the incident Gaussian beam by a vortex lens. Then the beam with the optical vortex is focused by an objective and illuminates the sample. By changing the position of the vortex lens we can shift the optical vortex position at the sample plane. By adjusting system parameters we can get 30 times smaller shift at the sample plane compared to the vortex lens shift. Moreover, if the range of vortex shifts is smaller than 3% of the beam radius in the sample plane the amplitude and phase distribution around the phase dislocation remains practically unchanged. Thus we can scan the sample topography precisely with an optical vortex
White Gaussian Noise - Models for Engineers
Jondral, Friedrich K.
2018-04-01
This paper assembles some information about white Gaussian noise (WGN) and its applications. It starts from a description of thermal noise, i. e. the irregular motion of free charge carriers in electronic devices. In a second step, mathematical models of WGN processes and their most important parameters, especially autocorrelation functions and power spectrum densities, are introduced. In order to proceed from mathematical models to simulations, we discuss the generation of normally distributed random numbers. The signal-to-noise ratio as the most important quality measure used in communications, control or measurement technology is accurately introduced. As a practical application of WGN, the transmission of quadrature amplitude modulated (QAM) signals over additive WGN channels together with the optimum maximum likelihood (ML) detector is considered in a demonstrative and intuitive way.
Gaussian process regression for geometry optimization
Denzel, Alexander; Kästner, Johannes
2018-03-01
We implemented a geometry optimizer based on Gaussian process regression (GPR) to find minimum structures on potential energy surfaces. We tested both a two times differentiable form of the Matérn kernel and the squared exponential kernel. The Matérn kernel performs much better. We give a detailed description of the optimization procedures. These include overshooting the step resulting from GPR in order to obtain a higher degree of interpolation vs. extrapolation. In a benchmark against the Limited-memory Broyden-Fletcher-Goldfarb-Shanno optimizer of the DL-FIND library on 26 test systems, we found the new optimizer to generally reduce the number of required optimization steps.
Tunnelling through a Gaussian random barrier
International Nuclear Information System (INIS)
Bezak, Viktor
2008-01-01
A thorough analysis of the tunnelling of electrons through a laterally inhomogeneous rectangular barrier is presented. The barrier height is defined as a statistically homogeneous Gaussian random function. In order to simplify calculations, we assume that the electron energy is low enough in comparison with the mean value of the barrier height. The randomness of the barrier height is defined vertically by a constant variance and horizontally by a finite correlation length. We present detailed calculations of the angular probability density for the tunnelled electrons (i.e. for the scattering forwards). The tunnelling manifests a remarkably diffusive character if the wavelength of the electrons is comparable with the correlation length of the barrier
Clock synchronization and dispersion
International Nuclear Information System (INIS)
Giovannetti, Vittorio; Lloyd, Seth; Maccone, Lorenzo; Wong, Franco N C
2002-01-01
We present a method to defeat effects of dispersion of timing signals when synchronizing clocks. It is based on the recently proposed 'conveyor belt synchronization' scheme and on the quantum dispersion cancellation effect
Gaussian process regression for tool wear prediction
Kong, Dongdong; Chen, Yongjie; Li, Ning
2018-05-01
To realize and accelerate the pace of intelligent manufacturing, this paper presents a novel tool wear assessment technique based on the integrated radial basis function based kernel principal component analysis (KPCA_IRBF) and Gaussian process regression (GPR) for real-timely and accurately monitoring the in-process tool wear parameters (flank wear width). The KPCA_IRBF is a kind of new nonlinear dimension-increment technique and firstly proposed for feature fusion. The tool wear predictive value and the corresponding confidence interval are both provided by utilizing the GPR model. Besides, GPR performs better than artificial neural networks (ANN) and support vector machines (SVM) in prediction accuracy since the Gaussian noises can be modeled quantitatively in the GPR model. However, the existence of noises will affect the stability of the confidence interval seriously. In this work, the proposed KPCA_IRBF technique helps to remove the noises and weaken its negative effects so as to make the confidence interval compressed greatly and more smoothed, which is conducive for monitoring the tool wear accurately. Moreover, the selection of kernel parameter in KPCA_IRBF can be easily carried out in a much larger selectable region in comparison with the conventional KPCA_RBF technique, which helps to improve the efficiency of model construction. Ten sets of cutting tests are conducted to validate the effectiveness of the presented tool wear assessment technique. The experimental results show that the in-process flank wear width of tool inserts can be monitored accurately by utilizing the presented tool wear assessment technique which is robust under a variety of cutting conditions. This study lays the foundation for tool wear monitoring in real industrial settings.
Nelson, RD
1988-01-01
This book provides powder technologists with laboratory procedures for selecting dispersing agents and preparing stable dispersions that can then be used in particle size characterization instruments. Its broader goal is to introduce industrial chemists and engineers to the phenomena, terminology, physical principles, and chemical considerations involved in preparing and handling dispersions on a commercial scale. The book introduces novices to: - industrial problems due to improper degree of dispersion; - the nomenclature used in describing particles; - the basic physica
Directory of Open Access Journals (Sweden)
Xiong Gang
2009-01-01
Full Text Available This paper presents theoretical results on the convergence of the distributed consensus timing synchronization (DCTS algorithm for wireless sensor networks assuming general Gaussian delay between nodes. The asymptotic expectation and mean square of the global synchronization error are computed. The results lead to the definition of a time delay balanced network in which average timing consensus between nodes can be achieved despite random delays. Several structured network architectures are studied as examples, and their associated simulation results are used to validate analytical findings.
International Nuclear Information System (INIS)
Araujo, Marcos A. de; Silva, Rubens; Lima, Emerson de; Pereira, Daniel P.; Oliveira, Paulo C. de
2009-01-01
We revisited the well known Khosrofian and Garetz inversion algorithm [Appl. Opt.22, 3406-3410 (1983)APOPAI0003-6935] that was developed to analyze data obtained by the application of the traveling knife-edge technique. We have analyzed the approximated fitting function that was used for adjusting their experimental data and have found that it is not optimized to work with a full range of the experimentally-measured data. We have numerically calculated a new set of coefficients, which makes the approximated function suitable for a full experimental range, considerably improving the accuracy of the measurement of a radius of a focused Gaussian laser beam
Zhang, Hong; Hou, Rui; Yi, Lei; Meng, Juan; Pan, Zhisong; Zhou, Yuhuan
2016-07-01
The accurate identification of encrypted data stream helps to regulate illegal data, detect network attacks and protect users' information. In this paper, a novel encrypted data stream identification algorithm is introduced. The proposed method is based on randomness characteristics of encrypted data stream. We use a l1-norm regularized logistic regression to improve sparse representation of randomness features and Fuzzy Gaussian Mixture Model (FGMM) to improve identification accuracy. Experimental results demonstrate that the method can be adopted as an effective technique for encrypted data stream identification.
Directory of Open Access Journals (Sweden)
Abdenaceur Boudlal
2010-01-01
Full Text Available This article investigates a new method of motion estimation based on block matching criterion through the modeling of image blocks by a mixture of two and three Gaussian distributions. Mixture parameters (weights, means vectors, and covariance matrices are estimated by the Expectation Maximization algorithm (EM which maximizes the log-likelihood criterion. The similarity between a block in the current image and the more resembling one in a search window on the reference image is measured by the minimization of Extended Mahalanobis distance between the clusters of mixture. Performed experiments on sequences of real images have given good results, and PSNR reached 3 dB.
International Nuclear Information System (INIS)
Panitz, H.J.
1988-01-01
An essential aim of the improvements of the new program system UFOMOD for Accident Consequence Assessments (ACAs) was to substitute the straightline Gaussian plume model conventionally used in ACA models by more realistic atmospheric dispersion models. To identify improved models which can be applied in ACA codes and to quantify the implications of different concepts of dispersion modelling on the results of an ACA, probabilistic comparative calculations with different atmospheric dispersion models have been carried out. The study showed that there are trajectory models available which can be applied in ACAs and that these trajectory models provide more realistic results of ACAs than straight-line Gaussian models. This led to a completly novel concept of atmospheric dispersion modelling which distinguish between two different distance ranges of validity: the near range ( 50 km). The two ranges are assigned to respective trajectory models
Convergence of posteriors for discretized log Gaussian Cox processes
DEFF Research Database (Denmark)
Waagepetersen, Rasmus Plenge
2004-01-01
In Markov chain Monte Carlo posterior computation for log Gaussian Cox processes (LGCPs) a discretization of the continuously indexed Gaussian field is required. It is demonstrated that approximate posterior expectations computed from discretized LGCPs converge to the exact posterior expectations...... when the cell sizes of the discretization tends to zero. The effect of discretization is studied in a data example....
Comparing Fixed and Variable-Width Gaussian Networks
Czech Academy of Sciences Publication Activity Database
Kůrková, Věra; Kainen, P.C.
2014-01-01
Roč. 57, September (2014), s. 23-28 ISSN 0893-6080 R&D Projects: GA MŠk(CZ) LD13002 Institutional support: RVO:67985807 Keywords : Gaussian radial and kernel networks * Functionally equivalent networks * Universal approximators * Stabilizers defined by Gaussian kernels * Argminima of error functionals Subject RIV: IN - Informatics, Computer Science Impact factor: 2.708, year: 2014
Two-photon optics of Bessel-Gaussian modes
CSIR Research Space (South Africa)
McLaren, M
2013-09-01
Full Text Available In this paper we consider geometrical two-photon optics of Bessel-Gaussian modes generated in spontaneous parametric down-conversion of a Gaussian pump beam. We provide a general theoretical expression for the orbital angular momentum (OAM) spectrum...
Application Of Shared Gamma And Inverse-Gaussian Frailty Models ...
African Journals Online (AJOL)
Shared Gamma and Inverse-Gaussian Frailty models are used to analyze the survival times of patients who are clustered according to cancer/tumor types under Parametric Proportional Hazard framework. The result of the ... However, no evidence is strong enough for preference of either Gamma or Inverse Gaussian Frailty.
Optimality of Gaussian attacks in continuous-variable quantum cryptography.
Navascués, Miguel; Grosshans, Frédéric; Acín, Antonio
2006-11-10
We analyze the asymptotic security of the family of Gaussian modulated quantum key distribution protocols for continuous-variables systems. We prove that the Gaussian unitary attack is optimal for all the considered bounds on the key rate when the first and second momenta of the canonical variables involved are known by the honest parties.
Degeneracy of energy levels of pseudo-Gaussian oscillators
International Nuclear Information System (INIS)
Iacob, Theodor-Felix; Iacob, Felix; Lute, Marina
2015-01-01
We study the main features of the isotropic radial pseudo-Gaussian oscillators spectral properties. This study is made upon the energy levels degeneracy with respect to orbital angular momentum quantum number. In a previous work [6] we have shown that the pseudo-Gaussian oscillators belong to the class of quasi-exactly solvable models and an exact solution has been found
Nguyen, Ngoc Minh; Corff, Sylvain Le; Moulines, Éric
2017-12-01
This paper focuses on sequential Monte Carlo approximations of smoothing distributions in conditionally linear and Gaussian state spaces. To reduce Monte Carlo variance of smoothers, it is typical in these models to use Rao-Blackwellization: particle approximation is used to sample sequences of hidden regimes while the Gaussian states are explicitly integrated conditional on the sequence of regimes and observations, using variants of the Kalman filter/smoother. The first successful attempt to use Rao-Blackwellization for smoothing extends the Bryson-Frazier smoother for Gaussian linear state space models using the generalized two-filter formula together with Kalman filters/smoothers. More recently, a forward-backward decomposition of smoothing distributions mimicking the Rauch-Tung-Striebel smoother for the regimes combined with backward Kalman updates has been introduced. This paper investigates the benefit of introducing additional rejuvenation steps in all these algorithms to sample at each time instant new regimes conditional on the forward and backward particles. This defines particle-based approximations of the smoothing distributions whose support is not restricted to the set of particles sampled in the forward or backward filter. These procedures are applied to commodity markets which are described using a two-factor model based on the spot price and a convenience yield for crude oil data.
Maximum Correntropy Criterion Kalman Filter for α-Jerk Tracking Model with Non-Gaussian Noise
Directory of Open Access Journals (Sweden)
Bowen Hou
2017-11-01
Full Text Available As one of the most critical issues for target track, α -jerk model is an effective maneuver target track model. Non-Gaussian noises always exist in the track process, which usually lead to inconsistency and divergence of the track filter. A novel Kalman filter is derived and applied on α -jerk tracking model to handle non-Gaussian noise. The weighted least square solution is presented and the standard Kalman filter is deduced firstly. A novel Kalman filter with the weighted least square based on the maximum correntropy criterion is deduced. The robustness of the maximum correntropy criterion is also analyzed with the influence function and compared with the Huber-based filter, and, moreover, the kernel size of Gaussian kernel plays an important role in the filter algorithm. A new adaptive kernel method is proposed in this paper to adjust the parameter in real time. Finally, simulation results indicate the validity and the efficiency of the proposed filter. The comparison study shows that the proposed filter can significantly reduce the noise influence for α -jerk model.
Performance modeling and analysis of parallel Gaussian elimination on multi-core computers
Directory of Open Access Journals (Sweden)
Fadi N. Sibai
2014-01-01
Full Text Available Gaussian elimination is used in many applications and in particular in the solution of systems of linear equations. This paper presents mathematical performance models and analysis of four parallel Gaussian Elimination methods (precisely the Original method and the new Meet in the Middle –MiM– algorithms and their variants with SIMD vectorization on multi-core systems. Analytical performance models of the four methods are formulated and presented followed by evaluations of these models with modern multi-core systems’ operation latencies. Our results reveal that the four methods generally exhibit good performance scaling with increasing matrix size and number of cores. SIMD vectorization only makes a large difference in performance for low number of cores. For a large matrix size (n ⩾ 16 K, the performance difference between the MiM and Original methods falls from 16× with four cores to 4× with 16 K cores. The efficiencies of all four methods are low with 1 K cores or more stressing a major problem of multi-core systems where the network-on-chip and memory latencies are too high in relation to basic arithmetic operations. Thus Gaussian Elimination can greatly benefit from the resources of multi-core systems, but higher performance gains can be achieved if multi-core systems can be designed with lower memory operation, synchronization, and interconnect communication latencies, requirements of utmost importance and challenge in the exascale computing age.
Soft Sensor Modeling Based on Multiple Gaussian Process Regression and Fuzzy C-mean Clustering
Directory of Open Access Journals (Sweden)
Xianglin ZHU
2014-06-01
Full Text Available In order to overcome the difficulties of online measurement of some crucial biochemical variables in fermentation processes, a new soft sensor modeling method is presented based on the Gaussian process regression and fuzzy C-mean clustering. With the consideration that the typical fermentation process can be distributed into 4 phases including lag phase, exponential growth phase, stable phase and dead phase, the training samples are classified into 4 subcategories by using fuzzy C- mean clustering algorithm. For each sub-category, the samples are trained using the Gaussian process regression and the corresponding soft-sensing sub-model is established respectively. For a new sample, the membership between this sample and sub-models are computed based on the Euclidean distance, and then the prediction output of soft sensor is obtained using the weighting sum. Taking the Lysine fermentation as example, the simulation and experiment are carried out and the corresponding results show that the presented method achieves better fitting and generalization ability than radial basis function neutral network and single Gaussian process regression model.
Non-Gaussian Stochastic Radiation Transfer in Finite Planar Media with Quadratic Scattering
International Nuclear Information System (INIS)
Sallah, M.
2016-01-01
The stochastic radiation transfer is considered in a participating planar finite continuously fluctuating medium characterized by non-Gaussian variability. The problem is considered for diffuse-reflecting boundaries with quadratic Rayleigh scattering. Random variable transformation (RVT) technique is used to get the complete average for the solution functions that are represented by the probability-density function (PDF) of the solution process. RVT algorithm applies a simple integral transformation to the input stochastic process (the extinction function of the medium). This linear transformation enables us to rewrite the stochastic transport equations in terms of the optical random variable (x) and the optical random thickness (L). Then the radiation transfer equation is solved deterministically to get a closed form for the solution as a function of x and L. So, the solution is used to obtain the PDF of the solution functions applying the RVT technique among the input random variable (L) and the output process (the solution functions). The obtained averages of the solution functions are used to get the complete analytical averages for some interesting physical quantities, namely, reflectivity, transmissivity and partial heat fluxes at the medium boundaries. Numerical results are represented graphically for different non-Gaussian probability distribution functions that compared with the corresponding Gaussian PDF.
A brain MRI bias field correction method created in the Gaussian multi-scale space
Chen, Mingsheng; Qin, Mingxin
2017-07-01
A pre-processing step is needed to correct for the bias field signal before submitting corrupted MR images to such image-processing algorithms. This study presents a new bias field correction method. The method creates a Gaussian multi-scale space by the convolution of the inhomogeneous MR image with a two-dimensional Gaussian function. In the multi-Gaussian space, the method retrieves the image details from the differentiation of the original image and convolution image. Then, it obtains an image whose inhomogeneity is eliminated by the weighted sum of image details in each layer in the space. Next, the bias field-corrected MR image is retrieved after the Υ correction, which enhances the contrast and brightness of the inhomogeneity-eliminated MR image. We have tested the approach on T1 MRI and T2 MRI with varying bias field levels and have achieved satisfactory results. Comparison experiments with popular software have demonstrated superior performance of the proposed method in terms of quantitative indices, especially an improvement in subsequent image segmentation.
Ultrawide Bandwidth Receiver Based on a Multivariate Generalized Gaussian Distribution
Ahmed, Qasim Zeeshan
2015-04-01
Multivariate generalized Gaussian density (MGGD) is used to approximate the multiple access interference (MAI) and additive white Gaussian noise in pulse-based ultrawide bandwidth (UWB) system. The MGGD probability density function (pdf) is shown to be a better approximation of a UWB system as compared to multivariate Gaussian, multivariate Laplacian and multivariate Gaussian-Laplacian mixture (GLM). The similarity between the simulated and the approximated pdf is measured with the help of modified Kullback-Leibler distance (KLD). It is also shown that MGGD has the smallest KLD as compared to Gaussian, Laplacian and GLM densities. A receiver based on the principles of minimum bit error rate is designed for the MGGD pdf. As the requirement is stringent, the adaptive implementation of the receiver is also carried out in this paper. Training sequence of the desired user is the only requirement when implementing the detector adaptively. © 2002-2012 IEEE.
Gaussian cloning of coherent states with known phases
International Nuclear Information System (INIS)
Alexanian, Moorad
2006-01-01
The fidelity for cloning coherent states is improved over that provided by optimal Gaussian and non-Gaussian cloners for the subset of coherent states that are prepared with known phases. Gaussian quantum cloning duplicates all coherent states with an optimal fidelity of 2/3. Non-Gaussian cloners give optimal single-clone fidelity for a symmetric 1-to-2 cloner of 0.6826. Coherent states that have known phases can be cloned with a fidelity of 4/5. The latter is realized by a combination of two beam splitters and a four-wave mixer operated in the nonlinear regime, all of which are realized by interaction Hamiltonians that are quadratic in the photon operators. Therefore, the known Gaussian devices for cloning coherent states are extended when cloning coherent states with known phases by considering a nonbalanced beam splitter at the input side of the amplifier
Directory of Open Access Journals (Sweden)
Milad eLankarany
2013-09-01
Full Text Available Time-varying excitatory and inhibitory synaptic inputs govern activity of neurons and process information in the brain. The importance of trial-to-trial fluctuations of synaptic inputs has recently been investigated in neuroscience. Such fluctuations are ignored in the most conventional techniques because they are removed when trials are averaged during linear regression techniques. Here, we propose a novel recursive algorithm based on Gaussian mixture Kalman filtering for estimating time-varying excitatory and inhibitory synaptic inputs from single trials of noisy membrane potential in current clamp recordings. The Kalman filtering is followed by an expectation maximization algorithm to infer the statistical parameters (time-varying mean and variance of the synaptic inputs in a non-parametric manner. As our proposed algorithm is repeated recursively, the inferred parameters of the mixtures are used to initiate the next iteration. Unlike other recent algorithms, our algorithm does not assume an a priori distribution from which the synaptic inputs are generated. Instead, the algorithm recursively estimates such a distribution by fitting a Gaussian mixture model. The performance of the proposed algorithms is compared to a previously proposed PF-based algorithm (Paninski et al., 2012 with several illustrative examples, assuming that the distribution of synaptic input is unknown. If noise is small, the performance of our algorithms is similar to that of the previous one. However, if noise is large, they can significantly outperform the previous proposal. These promising results suggest that our algorithm is a robust and efficient technique for estimating time varying excitatory and inhibitory synaptic conductances from single trials of membrane potential recordings.
Theory of dispersive microlenses
Herman, B.; Gal, George
1993-01-01
A dispersive microlens is a miniature optical element which simultaneously focuses and disperses light. Arrays of dispersive mircolenses have potential applications in multicolor focal planes. They have a 100 percent optical fill factor and can focus light down to detectors of diffraction spot size, freeing up areas on the focal plane for on-chip analog signal processing. Use of dispersive microlenses allows inband color separation within a pixel and perfect scene registration. A dual-color separation has the potential for temperature discrimination. We discuss the design of dispersive microlenses and present sample results for efficient designs.
Chambolle's Projection Algorithm for Total Variation Denoising
Directory of Open Access Journals (Sweden)
Joan Duran
2013-12-01
Full Text Available Denoising is the problem of removing the inherent noise from an image. The standard noise model is additive white Gaussian noise, where the observed image f is related to the underlying true image u by the degradation model f=u+n, and n is supposed to be at each pixel independently and identically distributed as a zero-mean Gaussian random variable. Since this is an ill-posed problem, Rudin, Osher and Fatemi introduced the total variation as a regularizing term. It has proved to be quite efficient for regularizing images without smoothing the boundaries of the objects. This paper focuses on the simple description of the theory and on the implementation of Chambolle's projection algorithm for minimizing the total variation of a grayscale image. Furthermore, we adapt the algorithm to the vectorial total variation for color images. The implementation is described in detail and its parameters are analyzed and varied to come up with a reliable implementation.
Yang, Sejung; Lee, Byung-Uk
2015-01-01
In certain image acquisitions processes, like in fluorescence microscopy or astronomy, only a limited number of photons can be collected due to various physical constraints. The resulting images suffer from signal dependent noise, which can be modeled as a Poisson distribution, and a low signal-to-noise ratio. However, the majority of research on noise reduction algorithms focuses on signal independent Gaussian noise. In this paper, we model noise as a combination of Poisson and Gaussian probability distributions to construct a more accurate model and adopt the contourlet transform which provides a sparse representation of the directional components in images. We also apply hidden Markov models with a framework that neatly describes the spatial and interscale dependencies which are the properties of transformation coefficients of natural images. In this paper, an effective denoising algorithm for Poisson-Gaussian noise is proposed using the contourlet transform, hidden Markov models and noise estimation in the transform domain. We supplement the algorithm by cycle spinning and Wiener filtering for further improvements. We finally show experimental results with simulations and fluorescence microscopy images which demonstrate the improved performance of the proposed approach. PMID:26352138
Numerical methods of estimating the dispersion of radionuclides in atmosphere
International Nuclear Information System (INIS)
Vladu, Mihaela; Ghitulescu, Alina; Popescu, Gheorghe; Piciorea, Iuliana
2007-01-01
Full text: The paper presents the method of dispersion calculation, witch can be applied for the DLE calculation. This is necessary to ensure a secure performance of the Experimental Pilot Plant for Tritium and Deuterium Separation (using the technology for detritiation based upon isotope catalytic exchange between tritiated heavy water and deuterium followed by cryogenic distillation of the hydrogen isotopes). For the calculation of the dispersion of radioactivity effluents in the atmosphere, at a given distance between source and receiver, the Gaussian mathematical model was used. This model is currently applied for estimating the long-term results of dispersion in case of continuous or intermittent emissions as basic information for long-term radioprotection measures for areas of the order of kilometres from the source. We have considered intermittent or continuous emissions of intensity lower than 1% per day relative to the annual emission. It is supposed that the radioactive material released into environment presents a gaussian dispersion both in horizontal and vertical plan. The local dispersion parameters could be determined directly with turbulence measurements or indirectly by determination of atmospheric stability. Weather parameters for characterizing the atmospheric dispersion include: - direction of wind relative to the source; - the speed of the wind at the height of emission; - parameters of dispersion to different distances, depending on the atmospheric turbulence which characterizes the mixing of radioactive materials in the atmosphere; - atmospheric stability range; - the height of mixture stratum; - the type and intensity of precipitations. The choice of the most adequate version of Gaussian model depends on the relation among the height where effluent emission is in progress, H (m), and the height at which the buildings influence the air motion, HB (m). There were defined three zones of distinct dispersion. This zones can have variable lengths
International Nuclear Information System (INIS)
Tsuchida, Takahiro; Kimura, Koji
2015-01-01
Equivalent non-Gaussian excitation method is proposed to obtain the moments up to the fourth order of the response of systems under non-Gaussian random excitation. The excitation is prescribed by the probability density and power spectrum. Moment equations for the response can be derived from the stochastic differential equations for the excitation and the system. However, the moment equations are not closed due to the nonlinearity of the diffusion coefficient in the equation for the excitation. In the proposed method, the diffusion coefficient is replaced with the equivalent diffusion coefficient approximately to obtain a closed set of the moment equations. The square of the equivalent diffusion coefficient is expressed by the second-order polynomial. In order to demonstrate the validity of the method, a linear system to non-Gaussian excitation with generalized Gaussian distribution is analyzed. The results show the method is applicable to non-Gaussian excitation with the widely different kurtosis and bandwidth. (author)
Kalman filtration of radiation monitoring data from atmospheric dispersion of radioactive materials
DEFF Research Database (Denmark)
Drews, M.; Lauritzen, B.; Madsen, H.
2004-01-01
A Kalman filter method using off-site radiation monitoring data is proposed as a tool for on-line estimation of the source term for short-range atmospheric dispersion of radioactive materials. The method is based on the Gaussian plume model, in which the plume parameters including the source term...
Random noise suppression of seismic data using non-local Bayes algorithm
Chang, De-Kuan; Yang, Wu-Yang; Wang, Yi-Hui; Yang, Qing; Wei, Xin-Jian; Feng, Xiao-Ying
2018-02-01
For random noise suppression of seismic data, we present a non-local Bayes (NL-Bayes) filtering algorithm. The NL-Bayes algorithm uses the Gaussian model instead of the weighted average of all similar patches in the NL-means algorithm to reduce the fuzzy of structural details, thereby improving the denoising performance. In the denoising process of seismic data, the size and the number of patches in the Gaussian model are adaptively calculated according to the standard deviation of noise. The NL-Bayes algorithm requires two iterations to complete seismic data denoising, but the second iteration makes use of denoised seismic data from the first iteration to calculate the better mean and covariance of the patch Gaussian model for improving the similarity of patches and achieving the purpose of denoising. Tests with synthetic and real data sets demonstrate that the NL-Bayes algorithm can effectively improve the SNR and preserve the fidelity of seismic data.
Semiparametric score level fusion: Gaussian copula approach
Susyanyo, N.; Klaassen, C.A.J.; Veldhuis, Raymond N.J.; Spreeuwers, Lieuwe Jan
2015-01-01
Score level fusion is an appealing method for combining multi-algorithms, multi- representations, and multi-modality biometrics due to its simplicity. Often, scores are assumed to be independent, but even for dependent scores, accord- ing to the Neyman-Pearson lemma, the likelihood ratio is the
Primordial non-Gaussianity from LAMOST surveys
International Nuclear Information System (INIS)
Gong Yan; Wang Xin; Chen Xuelei; Zheng Zheng
2010-01-01
The primordial non-Gaussianity (PNG) in the matter density perturbation is a very powerful probe of the physics of the very early Universe. The local PNG can induce a distinct scale-dependent bias on the large scale structure distribution of galaxies and quasars, which could be used for constraining it. We study the detection limits of PNG from the surveys of the LAMOST telescope. The cases of the main galaxy survey, the luminous red galaxy (LRG) survey, and the quasar survey of different magnitude limits are considered. We find that the Main1 sample (i.e. the main galaxy survey which is one magnitude deeper than the SDSS main galaxy survey, or r NL are |f NL | NL | NL | is between 50 and 103, depending on the magnitude limit of the survey. With Planck-like priors on cosmological parameters, the quasar survey with g NL | < 43 (2σ). We also discuss the possibility of further tightening the constraint by using the relative bias method proposed by Seljak.
A Decentralized Receiver in Gaussian Interference
Directory of Open Access Journals (Sweden)
Christian D. Chapman
2018-04-01
Full Text Available Bounds are developed on the maximum communications rate between a transmitter and a fusion node aided by a cluster of distributed receivers with limited resources for cooperation, all in the presence of an additive Gaussian interferer. The receivers cannot communicate with one another and can only convey processed versions of their observations to the fusion center through a Local Array Network (LAN with limited total throughput. The effectiveness of each bound’s approach for mitigating a strong interferer is assessed over a wide range of channels. It is seen that, if resources are shared effectively, even a simple quantize-and-forward strategy can mitigate an interferer 20 dB stronger than the signal in a diverse range of spatially Ricean channels. Monte-Carlo experiments for the bounds reveal that, while achievable rates are stable when varying the receiver’s observed scattered-path to line-of-sight signal power, the receivers must adapt how they share resources in response to this change. The bounds analyzed are proven to be achievable and are seen to be tight with capacity when LAN resources are either ample or limited.
Bayesian nonparametric adaptive control using Gaussian processes.
Chowdhary, Girish; Kingravi, Hassan A; How, Jonathan P; Vela, Patricio A
2015-03-01
Most current model reference adaptive control (MRAC) methods rely on parametric adaptive elements, in which the number of parameters of the adaptive element are fixed a priori, often through expert judgment. An example of such an adaptive element is radial basis function networks (RBFNs), with RBF centers preallocated based on the expected operating domain. If the system operates outside of the expected operating domain, this adaptive element can become noneffective in capturing and canceling the uncertainty, thus rendering the adaptive controller only semiglobal in nature. This paper investigates a Gaussian process-based Bayesian MRAC architecture (GP-MRAC), which leverages the power and flexibility of GP Bayesian nonparametric models of uncertainty. The GP-MRAC does not require the centers to be preallocated, can inherently handle measurement noise, and enables MRAC to handle a broader set of uncertainties, including those that are defined as distributions over functions. We use stochastic stability arguments to show that GP-MRAC guarantees good closed-loop performance with no prior domain knowledge of the uncertainty. Online implementable GP inference methods are compared in numerical simulations against RBFN-MRAC with preallocated centers and are shown to provide better tracking and improved long-term learning.
High-efficiency Gaussian key reconciliation in continuous variable quantum key distribution
Bai, ZengLiang; Wang, XuYang; Yang, ShenShen; Li, YongMin
2016-01-01
Efficient reconciliation is a crucial step in continuous variable quantum key distribution. The progressive-edge-growth (PEG) algorithm is an efficient method to construct relatively short block length low-density parity-check (LDPC) codes. The qua-sicyclic construction method can extend short block length codes and further eliminate the shortest cycle. In this paper, by combining the PEG algorithm and qua-si-cyclic construction method, we design long block length irregular LDPC codes with high error-correcting capacity. Based on these LDPC codes, we achieve high-efficiency Gaussian key reconciliation with slice recon-ciliation based on multilevel coding/multistage decoding with an efficiency of 93.7%.
Smolin, John A; Gambetta, Jay M; Smith, Graeme
2012-02-17
We provide an efficient method for computing the maximum-likelihood mixed quantum state (with density matrix ρ) given a set of measurement outcomes in a complete orthonormal operator basis subject to Gaussian noise. Our method works by first changing basis yielding a candidate density matrix μ which may have nonphysical (negative) eigenvalues, and then finding the nearest physical state under the 2-norm. Our algorithm takes at worst O(d(4)) for the basis change plus O(d(3)) for finding ρ where d is the dimension of the quantum state. In the special case where the measurement basis is strings of Pauli operators, the basis change takes only O(d(3)) as well. The workhorse of the algorithm is a new linear-time method for finding the closest probability distribution (in Euclidean distance) to a set of real numbers summing to one.
Gaussian interaction profile kernels for predicting drug-target interaction.
van Laarhoven, Twan; Nabuurs, Sander B; Marchiori, Elena
2011-11-01
The in silico prediction of potential interactions between drugs and target proteins is of core importance for the identification of new drugs or novel targets for existing drugs. However, only a tiny portion of all drug-target pairs in current datasets are experimentally validated interactions. This motivates the need for developing computational methods that predict true interaction pairs with high accuracy. We show that a simple machine learning method that uses the drug-target network as the only source of information is capable of predicting true interaction pairs with high accuracy. Specifically, we introduce interaction profiles of drugs (and of targets) in a network, which are binary vectors specifying the presence or absence of interaction with every target (drug) in that network. We define a kernel on these profiles, called the Gaussian Interaction Profile (GIP) kernel, and use a simple classifier, (kernel) Regularized Least Squares (RLS), for prediction drug-target interactions. We test comparatively the effectiveness of RLS with the GIP kernel on four drug-target interaction networks used in previous studies. The proposed algorithm achieves area under the precision-recall curve (AUPR) up to 92.7, significantly improving over results of state-of-the-art methods. Moreover, we show that using also kernels based on chemical and genomic information further increases accuracy, with a neat improvement on small datasets. These results substantiate the relevance of the network topology (in the form of interaction profiles) as source of information for predicting drug-target interactions. Software and Supplementary Material are available at http://cs.ru.nl/~tvanlaarhoven/drugtarget2011/. tvanlaarhoven@cs.ru.nl; elenam@cs.ru.nl. Supplementary data are available at Bioinformatics online.
International Nuclear Information System (INIS)
Kou, Peng; Liang, Deliang; Gao, Lin; Lou, Jianyong
2015-01-01
Highlights: • A novel active learning model for the probabilistic electricity price forecasting. • Heteroscedastic Gaussian process that captures the local volatility of the electricity price. • Variational Bayesian learning that avoids over-fitting. • Active learning algorithm that reduces the computational efforts. - Abstract: Electricity price forecasting is essential for the market participants in their decision making. Nevertheless, the accuracy of such forecasting cannot be guaranteed due to the high variability of the price data. For this reason, in many cases, rather than merely point forecasting results, market participants are more interested in the probabilistic price forecasting results, i.e., the prediction intervals of the electricity price. Focusing on this issue, this paper proposes a new model for the probabilistic electricity price forecasting. This model is based on the active learning technique and the variational heteroscedastic Gaussian process (VHGP). It provides the heteroscedastic Gaussian prediction intervals, which effectively quantify the heteroscedastic uncertainties associated with the price data. Because the high computational effort of VHGP hinders its application to the large-scale electricity price forecasting tasks, we design an active learning algorithm to select a most informative training subset from the whole available training set. By constructing the forecasting model on this smaller subset, the computational efforts can be significantly reduced. In this way, the practical applicability of the proposed model is enhanced. The forecasting performance and the computational time of the proposed model are evaluated using the real-world electricity price data, which is obtained from the ANEM, PJM, and New England ISO
Wavefront-ray grid FDTD algorithm
ÇİYDEM, MEHMET
2016-01-01
A finite difference time domain algorithm on a wavefront-ray grid (WRG-FDTD) is proposed in this study to reduce numerical dispersion of conventional FDTD methods. A FDTD algorithm conforming to a wavefront-ray grid can be useful to take into account anisotropy effects of numerical grids since it features directional energy flow along the rays. An explicit and second-order accurate WRG-FDTD algorithm is provided in generalized curvilinear coordinates for an inhomogeneous isotropic medium. Num...
Uranium Dispersion and Dosimetry (UDAD) Code
International Nuclear Information System (INIS)
Momeni, M.H.; Yuan, Y.; Zielen, A.J.
1979-05-01
The Uranium Dispersion and Dosimetry (UDAD) Code provides estimates of potential radiation exposure to individuals and to the general population in the vicinity of a uranium processing facility. The UDAD Code incorporates the radiation dose from the airborne release of radioactive materials, and includes dosimetry of inhalation, ingestion, and external exposures. The removal of raioactive particles from a contaminated area by wind action is estimated, atmospheric concentrations of radioactivity from specific sources are calculated, and source depletion as a result of deposition, fallout, and ingrowth of radon daughters are included in a sector-averaged Gaussian plume dispersion model. The average air concentration at any given receptor location is assumed to be constant during each annual release period, but to increase from year to year because of resuspension. Surface contamination and deposition velocity are estimated. Calculation of the inhalation dose and dose rate to an individual is based on the ICRP Task Group Lung Model. Estimates of the dose to the bronchial epithelium of the lung from inhalation of radon and its short-lived daughters are calculated based on a dose conversion factor from the BEIR report. External radiation exposure includes radiation from airborne radionuclides and exposure to radiation from contaminated ground. Terrestrial food pathways include vegetation, meat, milk, poultry, and eggs. Internal dosimetry is based on ICRP recommendations. In addition, individual dose commitments, population dose commitments, and environmental dose commitments are computed. This code also may be applied to dispersion of any other pollutant
Gaussian process tomography for soft x-ray spectroscopy at WEST without equilibrium information
Wang, T.; Mazon, D.; Svensson, J.; Li, D.; Jardin, A.; Verdoolaege, G.
2018-06-01
Gaussian process tomography (GPT) is a recently developed tomography method based on the Bayesian probability theory [J. Svensson, JET Internal Report EFDA-JET-PR(11)24, 2011 and Li et al., Rev. Sci. Instrum. 84, 083506 (2013)]. By modeling the soft X-ray (SXR) emissivity field in a poloidal cross section as a Gaussian process, the Bayesian SXR tomography can be carried out in a robust and extremely fast way. Owing to the short execution time of the algorithm, GPT is an important candidate for providing real-time reconstructions with a view to impurity transport and fast magnetohydrodynamic control. In addition, the Bayesian formalism allows quantifying uncertainty on the inferred parameters. In this paper, the GPT technique is validated using a synthetic data set expected from the WEST tokamak, and the results are shown of its application to the reconstruction of SXR emissivity profiles measured on Tore Supra. The method is compared with the standard algorithm based on minimization of the Fisher information.
Wang, Shunfang; Nie, Bing; Yue, Kun; Fei, Yu; Li, Wenjia; Xu, Dongshu
2017-12-15
Kernel discriminant analysis (KDA) is a dimension reduction and classification algorithm based on nonlinear kernel trick, which can be novelly used to treat high-dimensional and complex biological data before undergoing classification processes such as protein subcellular localization. Kernel parameters make a great impact on the performance of the KDA model. Specifically, for KDA with the popular Gaussian kernel, to select the scale parameter is still a challenging problem. Thus, this paper introduces the KDA method and proposes a new method for Gaussian kernel parameter selection depending on the fact that the differences between reconstruction errors of edge normal samples and those of interior normal samples should be maximized for certain suitable kernel parameters. Experiments with various standard data sets of protein subcellular localization show that the overall accuracy of protein classification prediction with KDA is much higher than that without KDA. Meanwhile, the kernel parameter of KDA has a great impact on the efficiency, and the proposed method can produce an optimum parameter, which makes the new algorithm not only perform as effectively as the traditional ones, but also reduce the computational time and thus improve efficiency.
Fast Kalman-like filtering for large-dimensional linear and Gaussian state-space models
Ait-El-Fquih, Boujemaa; Hoteit, Ibrahim
2015-01-01
This paper considers the filtering problem for linear and Gaussian state-space models with large dimensions, a setup in which the optimal Kalman Filter (KF) might not be applicable owing to the excessive cost of manipulating huge covariance matrices. Among the most popular alternatives that enable cheaper and reasonable computation is the Ensemble KF (EnKF), a Monte Carlo-based approximation. In this paper, we consider a class of a posteriori distributions with diagonal covariance matrices and propose fast approximate deterministic-based algorithms based on the Variational Bayesian (VB) approach. More specifically, we derive two iterative KF-like algorithms that differ in the way they operate between two successive filtering estimates; one involves a smoothing estimate and the other involves a prediction estimate. Despite its iterative nature, the prediction-based algorithm provides a computational cost that is, on the one hand, independent of the number of iterations in the limit of very large state dimensions, and on the other hand, always much smaller than the cost of the EnKF. The cost of the smoothing-based algorithm depends on the number of iterations that may, in some situations, make this algorithm slower than the EnKF. The performances of the proposed filters are studied and compared to those of the KF and EnKF through a numerical example.
Fast Kalman-like filtering for large-dimensional linear and Gaussian state-space models
Ait-El-Fquih, Boujemaa
2015-08-13
This paper considers the filtering problem for linear and Gaussian state-space models with large dimensions, a setup in which the optimal Kalman Filter (KF) might not be applicable owing to the excessive cost of manipulating huge covariance matrices. Among the most popular alternatives that enable cheaper and reasonable computation is the Ensemble KF (EnKF), a Monte Carlo-based approximation. In this paper, we consider a class of a posteriori distributions with diagonal covariance matrices and propose fast approximate deterministic-based algorithms based on the Variational Bayesian (VB) approach. More specifically, we derive two iterative KF-like algorithms that differ in the way they operate between two successive filtering estimates; one involves a smoothing estimate and the other involves a prediction estimate. Despite its iterative nature, the prediction-based algorithm provides a computational cost that is, on the one hand, independent of the number of iterations in the limit of very large state dimensions, and on the other hand, always much smaller than the cost of the EnKF. The cost of the smoothing-based algorithm depends on the number of iterations that may, in some situations, make this algorithm slower than the EnKF. The performances of the proposed filters are studied and compared to those of the KF and EnKF through a numerical example.
Energy Technology Data Exchange (ETDEWEB)
Cho, I; Nakanishi, I [Kyoto University, Kyoto (Japan); Ling, S [Nihon Nessui Corp., Tokyo (Japan); Okada, H [Hokkaido University, Sapporo (Japan)
1997-10-22
Discussions were given on a genetic algorithm as a means to solve simultaneously the problems related to stability of solution and dependence on an initial model in estimating subsurface structures using the microtremor exploration method. In the study, a forking genetic algorithm (fGA) to explore solid substance groups was applied to the optimizing simulations for a velocity structure model to discuss whether the algorithm can be used practically. The simulation No. 1 was performed by making the number of layers four for both of the given velocity structure and the optimizing model. On the other hand, the simulation No. 2 was executed by making the number of layers for the given velocity structure greater than that for the optimizing model. As a result, it was verified that wide range exploration may be possible for the velocity structure model, and that a large number of candidates for the velocity structure model may be proposed. In either case, the exploration capability of the fGA exceeded that of the standard simple genetic algorithm. 8 refs., 4 figs., 2 tabs.
Boltzmann-Gaussian transition under specific noise effect
International Nuclear Information System (INIS)
Anh, Chu Thuy; Lan, Nguyen Tri; Viet, Nguyen Ai
2014-01-01
It is observed that a short time data set of market returns presents almost symmetric Boltzmann distribution whereas a long time data set tends to show a Gaussian distribution. To understand this universal phenomenon, many hypotheses which are spreading in a wide range of interdisciplinary research were proposed. In current work, the effects of background fluctuations on symmetric Boltzmann distribution is investigated. The numerical calculation is performed to show that the Gaussian noise may cause the transition from initial Boltzmann distribution to Gaussian one. The obtained results would reflect non-dynamic nature of the transition under consideration.
Legendre Duality of Spherical and Gaussian Spin Glasses
International Nuclear Information System (INIS)
Genovese, Giuseppe; Tantari, Daniele
2015-01-01
The classical result of concentration of the Gaussian measure on the sphere in the limit of large dimension induces a natural duality between Gaussian and spherical models of spin glass. We analyse the Legendre variational structure linking the free energies of these two systems, in the spirit of the equivalence of ensembles of statistical mechanics. Our analysis, combined with the previous work (Barra et al., J. Phys. A: Math. Theor. 47, 155002, 2014), shows that such models are replica symmetric. Lastly, we briefly discuss an application of our result to the study of the Gaussian Hopfield model
Controllable gaussian-qubit interface for extremal quantum state engineering.
Adesso, Gerardo; Campbell, Steve; Illuminati, Fabrizio; Paternostro, Mauro
2010-06-18
We study state engineering through bilinear interactions between two remote qubits and two-mode gaussian light fields. The attainable two-qubit states span the entire physically allowed region in the entanglement-versus-global-purity plane. Two-mode gaussian states with maximal entanglement at fixed global and marginal entropies produce maximally entangled two-qubit states in the corresponding entropic diagram. We show that a small set of parameters characterizing extremally entangled two-mode gaussian states is sufficient to control the engineering of extremally entangled two-qubit states, which can be realized in realistic matter-light scenarios.
Legendre Duality of Spherical and Gaussian Spin Glasses
Energy Technology Data Exchange (ETDEWEB)
Genovese, Giuseppe, E-mail: giuseppe.genovese@math.uzh.ch [Universität Zürich, Institut für Mathematik (Switzerland); Tantari, Daniele, E-mail: daniele.tantari@sns.it [Scuola Normale Superiore di Pisa, Centro Ennio de Giorgi (Italy)
2015-12-15
The classical result of concentration of the Gaussian measure on the sphere in the limit of large dimension induces a natural duality between Gaussian and spherical models of spin glass. We analyse the Legendre variational structure linking the free energies of these two systems, in the spirit of the equivalence of ensembles of statistical mechanics. Our analysis, combined with the previous work (Barra et al., J. Phys. A: Math. Theor. 47, 155002, 2014), shows that such models are replica symmetric. Lastly, we briefly discuss an application of our result to the study of the Gaussian Hopfield model.
Methods to characterize non-Gaussian noise in TAMA
International Nuclear Information System (INIS)
Ando, Masaki; Arai, K; Takahashi, R; Tatsumi, D; Beyersdorf, P; Kawamura, S; Miyoki, S; Mio, N; Moriwaki, S; Numata, K; Kanda, N; Aso, Y; Fujimoto, M-K; Tsubono, K; Kuroda, K
2003-01-01
We present a data characterization method for the main output signal of the interferometric gravitational-wave detector, in particular targeting at effective detection of burst gravitational waves from stellar core collapse. The time scale of non-Gaussian events is evaluated in this method, and events with longer time scale than real signals are rejected as non-Gaussian noises. As a result of data analysis using 1000 h of real data with the interferometric gravitational-wave detector TAMA300, the false-alarm rate was improved 10 3 times with this non-Gaussian noise evaluation and rejection method
Coincidence Imaging and interference with coherent Gaussian beams
Institute of Scientific and Technical Information of China (English)
CAI Yang-jian; ZHU Shi-yao
2006-01-01
we present a theoretical study of coincidence imaging and interference with coherent Gaussian beams The equations for the coincidence image formation and interference fringes are derived,from which it is clear that the imaging is due to the corresponding focusing in the two paths .The quality and visibility of the images and fringes can be high simultaneously.The nature of the coincidence imaging and interference between quantum entangled photon pairs and coherent Gaussian beams are different .The coincidence image with coherent Gaussian beams is due to intensity-intensity correspondence,a classical nature,while that with entangled photon pairs is due to the amplitude correlation a quantum nature.
Quantum Teamwork for Unconditional Multiparty Communication with Gaussian States
Zhang, Jing; Adesso, Gerardo; Xie, Changde; Peng, Kunchi
2009-08-01
We demonstrate the capability of continuous variable Gaussian states to communicate multipartite quantum information. A quantum teamwork protocol is presented according to which an arbitrary possibly entangled multimode state can be faithfully teleported between two teams each comprising many cooperative users. We prove that N-mode Gaussian weighted graph states exist for arbitrary N that enable unconditional quantum teamwork implementations for any arrangement of the teams. These perfect continuous variable maximally multipartite entangled resources are typical among pure Gaussian states and are unaffected by the entanglement frustration occurring in multiqubit states.
A note on moving average models for Gaussian random fields
DEFF Research Database (Denmark)
Hansen, Linda Vadgård; Thorarinsdottir, Thordis L.
The class of moving average models offers a flexible modeling framework for Gaussian random fields with many well known models such as the Matérn covariance family and the Gaussian covariance falling under this framework. Moving average models may also be viewed as a kernel smoothing of a Lévy...... basis, a general modeling framework which includes several types of non-Gaussian models. We propose a new one-parameter spatial correlation model which arises from a power kernel and show that the associated Hausdorff dimension of the sample paths can take any value between 2 and 3. As a result...
Detection Performance of Signals in Dependent Noise From a Gaussian Mixture Uncertainty Class
National Research Council Canada - National Science Library
Gerlach, K
1998-01-01
... (correlated) multivariate noise from a Gaussian mixture uncertainty class. This uncertainty class is defined using upper and lower bounding functions on the univariate Gaussian mixing distribution function...
Xu, Lu; Yu, Lianghong; Liang, Xiaoyan
2016-04-01
We present for the first time a scheme to amplify a Laguerre-Gaussian vortex beam based on non-collinear optical parametric chirped pulse amplification (OPCPA). In addition, a three-dimensional numerical model of non-collinear optical parametric amplification was deduced in the frequency domain, in which the effects of non-collinear configuration, temporal and spatial walk-off, group-velocity dispersion and diffraction were also taken into account, to trace the dynamics of the Laguerre-Gaussian vortex beam and investigate its critical parameters in the non-collinear OPCPA process. Based on the numerical simulation results, the scheme shows promise for implementation in a relativistic twisted laser pulse system, which will diversify the light-matter interaction field.
International Nuclear Information System (INIS)
Li Zhe; Liu Min; Shi Rui; Wu Xuemei; Tuo Xianguo
2012-01-01
Background: Non-standard analysis (NSA) technique is one of the most important development directions of energy dispersive X-ray fluorescence (EDXRF). Purpose: This NSA technique is mainly based on Monte Carlo (MC) simulation and full energy peak broadening, which were studied preliminarily in this paper. Methods: A kind of MC model was established for Si-PIN based EDXRF setup, and the flux spectra were obtained for iron ore sample. Finally, the flux spectra were broadened by Gaussian broaden parameters calculated by a new method proposed in this paper, and the broadened spectra were compared with measured energy spectra. Results: MC method can be used to simulate EDXRF measurement, and can correct the matrix effects among elements automatically. Peak intensities can be obtained accurately by using the proposed Gaussian broaden technique. Conclusions: This study provided a key technique for EDXRF to achieve advanced NSA technology. (authors)
Directory of Open Access Journals (Sweden)
Yimeng Zhang
2013-05-01
Full Text Available A method of blind recognition of the coding parameters for binary Bose-Chaudhuri-Hocquenghem (BCH codes is proposed in this paper. We consider an intelligent communication receiver which can blindly recognize the coding parameters of the received data stream. The only knowledge is that the stream is encoded using binary BCH codes, while the coding parameters are unknown. The problem can be addressed on the context of the non-cooperative communications or adaptive coding and modulations (ACM for cognitive radio networks. The recognition processing includes two major procedures: code length estimation and generator polynomial reconstruction. A hard decision method has been proposed in a previous literature. In this paper we propose the recognition approach in soft decision situations with Binary-Phase-Shift-Key modulations and Additive-White-Gaussian-Noise (AWGN channels. The code length is estimated by maximizing the root information dispersion entropy function. And then we search for the code roots to reconstruct the primitive and generator polynomials. By utilizing the soft output of the channel, the recognition performance is improved and the simulations show the efficiency of the proposed algorithm.
International Nuclear Information System (INIS)
Tsuchida, Takahiro; Kimura, Koji
2016-01-01
Equivalent non-Gaussian excitation method is proposed to obtain the response moments up to the 4th order of dynamic systems under non-Gaussian random excitation. The non-Gaussian excitation is prescribed by the probability density and the power spectrum, and is described by an Ito stochastic differential equation. Generally, moment equations for the response, which are derived from the governing equations for the excitation and the system, are not closed due to the nonlinearity of the diffusion coefficient in the equation for the excitation even though the system is linear. In the equivalent non-Gaussian excitation method, the diffusion coefficient is replaced with the equivalent diffusion coefficient approximately to obtain a closed set of the moment equations. The square of the equivalent diffusion coefficient is expressed by a quadratic polynomial. In numerical examples, a linear system subjected to nonGaussian excitations with bimodal and Rayleigh distributions is analyzed by using the present method. The results show that the method yields the variance, skewness and kurtosis of the response with high accuracy for non-Gaussian excitation with the widely different probability densities and bandwidth. The statistical moments of the equivalent non-Gaussian excitation are also investigated to describe the feature of the method. (paper)
International Nuclear Information System (INIS)
Strandlie, A.; Wroldsen, J.
2006-01-01
If any of the probability densities involved in track fitting deviate from the Gaussian assumption, it is plausible that a non-linear estimator which better takes the actual shape of the distribution into account can do better. One such non-linear estimator is the Gaussian-sum filter, which is adequate if the distributions under consideration can be approximated by Gaussian mixtures. The main purpose of this paper is to present a Gaussian-sum filter for track fitting, based on a two-component approximation of the distribution of angular deflections due to multiple scattering. In a simulation study within a linear track model the Gaussian-sum filter is shown to be a competitive alternative to the Kalman filter. Scenarios at various momenta and with various maximum number of components in the Gaussian-sum filter are considered. Particularly at low momenta the Gaussian-sum filter yields a better estimate of the uncertainties than the Kalman filter, and it is also slightly more precise than the latter
International Nuclear Information System (INIS)
Yu, Jie; Chen, Kuilin; Mori, Junichi; Rashid, Mudassir M.
2013-01-01
Optimizing wind power generation and controlling the operation of wind turbines to efficiently harness the renewable wind energy is a challenging task due to the intermittency and unpredictable nature of wind speed, which has significant influence on wind power production. A new approach for long-term wind speed forecasting is developed in this study by integrating GMCM (Gaussian mixture copula model) and localized GPR (Gaussian process regression). The time series of wind speed is first classified into multiple non-Gaussian components through the Gaussian mixture copula model and then Bayesian inference strategy is employed to incorporate the various non-Gaussian components using the posterior probabilities. Further, the localized Gaussian process regression models corresponding to different non-Gaussian components are built to characterize the stochastic uncertainty and non-stationary seasonality of the wind speed data. The various localized GPR models are integrated through the posterior probabilities as the weightings so that a global predictive model is developed for the prediction of wind speed. The proposed GMCM–GPR approach is demonstrated using wind speed data from various wind farm locations and compared against the GMCM-based ARIMA (auto-regressive integrated moving average) and SVR (support vector regression) methods. In contrast to GMCM–ARIMA and GMCM–SVR methods, the proposed GMCM–GPR model is able to well characterize the multi-seasonality and uncertainty of wind speed series for accurate long-term prediction. - Highlights: • A novel predictive modeling method is proposed for long-term wind speed forecasting. • Gaussian mixture copula model is estimated to characterize the multi-seasonality. • Localized Gaussian process regression models can deal with the random uncertainty. • Multiple GPR models are integrated through Bayesian inference strategy. • The proposed approach shows higher prediction accuracy and reliability
Huang, Xingguo; Sun, Hui
2018-05-01
Gaussian beam is an important complex geometrical optical technology for modeling seismic wave propagation and diffraction in the subsurface with complex geological structure. Current methods for Gaussian beam modeling rely on the dynamic ray tracing and the evanescent wave tracking. However, the dynamic ray tracing method is based on the paraxial ray approximation and the evanescent wave tracking method cannot describe strongly evanescent fields. This leads to inaccuracy of the computed wave fields in the region with a strong inhomogeneous medium. To address this problem, we compute Gaussian beam wave fields using the complex phase by directly solving the complex eikonal equation. In this method, the fast marching method, which is widely used for phase calculation, is combined with Gauss-Newton optimization algorithm to obtain the complex phase at the regular grid points. The main theoretical challenge in combination of this method with Gaussian beam modeling is to address the irregular boundary near the curved central ray. To cope with this challenge, we present the non-uniform finite difference operator and a modified fast marching method. The numerical results confirm the proposed approach.
Atmospheric dispersion of radionuclides released by a nuclear plant
International Nuclear Information System (INIS)
Barboza, A.A.
1989-01-01
A numerical model has been developed to simulate the atmospheric dispersion of radionuclides released by a nuclear plant operating under normal conditions. The model, based on gaussian plume representation, accouts for and evaluates several factors which affect the concentraction of effluents in the atmosphere, such as: ressuspension, deposition, radioactive decay, energy and type of the radiation emitted, among others. The concentraction of effluents in the atmosphere is calculated for a uniform mesh of points around the plant, allowing the equivalent doses to be then evaluated. Simulations of the atmosphere dispersion of radioactive plumes of Cs 137 and Ar 41 have been performed assuming a constant rate of release, as expected from the normal operation of a nuclear plant. Finally, this work analyzes the equivalent doses at ground level due to the dispersion of Cs 137 and Ar 41 , accumulated over one year and determines the isodose curves for a hypothetical site. (author) [pt
Dispersion of acoustic surface waves by velocity gradients
Kwon, S. D.; Kim, H. C.
1987-10-01
The perturbation theory of Auld [Acoustic Fields and Waves in Solids (Wiley, New York, 1973), Vol. II, p. 294], which describes the effect of a subsurface gradient on the velocity dispersion of surface waves, has been modified to a simpler form by an approximation using a newly defined velocity gradient for the case of isotropic materials. The modified theory is applied to nitrogen implantation in AISI 4140 steel with a velocity gradient of Gaussian profile, and compared with dispersion data obtained by the ultrasonic right-angle technique in the frequency range from 2.4 to 14.8 MHz. The good agreement between experiments and our theory suggests that the compound layer in the subsurface region plays a dominant role in causing the dispersion of acoustic surface waves.
Comparative calculations and validation studies with atmospheric dispersion models
International Nuclear Information System (INIS)
Paesler-Sauer, J.
1986-11-01
This report presents the results of an intercomparison of different mesoscale dispersion models and measured data of tracer experiments. The types of models taking part in the intercomparison are Gaussian-type, numerical Eulerian, and Lagrangian dispersion models. They are suited for the calculation of the atmospherical transport of radionuclides released from a nuclear installation. For the model intercomparison artificial meteorological situations were defined and corresponding arithmetical problems were formulated. For the purpose of model validation real dispersion situations of tracer experiments were used as input data for model calculations; in these cases calculated and measured time-integrated concentrations close to the ground are compared. Finally a valuation of the models concerning their efficiency in solving the problems is carried out by the aid of objective methods. (orig./HP) [de
Enhancements to AERMOD's building downwash algorithms based on wind-tunnel and Embedded-LES modeling
Monbureau, E. M.; Heist, D. K.; Perry, S. G.; Brouwer, L. H.; Foroutan, H.; Tang, W.
2018-04-01
Knowing the fate of effluent from an industrial stack is important for assessing its impact on human health. AERMOD is one of several Gaussian plume models containing algorithms to evaluate the effect of buildings on the movement of the effluent from a stack. The goal of this study is to improve AERMOD's ability to accurately model important and complex building downwash scenarios by incorporating knowledge gained from a recently completed series of wind tunnel studies and complementary large eddy simulations of flow and dispersion around simple structures for a variety of building dimensions, stack locations, stack heights, and wind angles. This study presents three modifications to the building downwash algorithm in AERMOD that improve the physical basis and internal consistency of the model, and one modification to AERMOD's building pre-processor to better represent elongated buildings in oblique winds. These modifications are demonstrated to improve the ability of AERMOD to model observed ground-level concentrations in the vicinity of a building for the variety of conditions examined in the wind tunnel and numerical studies.
Bianchi, Davide; Chiesa, Matteo; Guzzo, Luigi
2015-01-01
As a step towards a more accurate modelling of redshift-space distortions (RSD) in galaxy surveys, we develop a general description of the probability distribution function of galaxy pairwise velocities within the framework of the so-called streaming model. For a given galaxy separation r, such function can be described as a superposition of virtually infinite local distributions. We characterize these in terms of their moments and then consider the specific case in which they are Gaussian functions, each with its own mean μ and dispersion σ. Based on physical considerations, we make the further crucial assumption that these two parameters are in turn distributed according to a bivariate Gaussian, with its own mean and covariance matrix. Tests using numerical simulations explicitly show that with this compact description one can correctly model redshift-space distortions on all scales, fully capturing the overall linear and non-linear dynamics of the galaxy flow at different separations. In particular, we naturally obtain Gaussian/exponential, skewed/unskewed distribution functions, depending on separation as observed in simulations and data. Also, the recently proposed single-Gaussian description of RSD is included in this model as a limiting case, when the bivariate Gaussian is collapsed to a two-dimensional Dirac delta function. We also show how this description naturally allows for the Taylor expansion of 1 + ξS(s) around 1 + ξR(r), which leads to the Kaiser linear formula when truncated to second order, explicating its connection with the moments of the velocity distribution functions. More work is needed, but these results indicate a very promising path to make definitive progress in our programme to improve RSD estimators.
Adaptive antenna array algorithms and their impact on code division ...
African Journals Online (AJOL)
In this paper four each blind adaptive array algorithms are developed, and their performance under different test situations (e.g. A WGN (Additive White Gaussian Noise) channel, and multipath environment) is studied A MATLAB test bed is created to show their performance on these two test situations and an optimum one ...
Optimal multicopy asymmetric Gaussian cloning of coherent states
Fiurášek, Jaromír; Cerf, Nicolas J.
2007-05-01
We investigate the asymmetric Gaussian cloning of coherent states which produces M copies from N input replicas in such a way that the fidelity of each copy may be different. We show that the optimal asymmetric Gaussian cloning can be performed with a single phase-insensitive amplifier and an array of beam splitters. We obtain a simple analytical expression characterizing the set of optimal asymmetric Gaussian cloning machines and prove the optimality of these cloners using the formalism of Gaussian completely positive maps and semidefinite programming techniques. We also present an alternative implementation of the asymmetric cloning machine where the phase-insensitive amplifier is replaced with a beam splitter, heterodyne detector, and feedforward.
Simple form for the Gaussian equations in curved space
International Nuclear Information System (INIS)
Mazzitelli, F.D.; Paz, J.P.
1988-01-01
We show that the variational Gaussian equations for λphi 4 theory in an arbitrary background gravitational field admit a simple form, which allows the use of a Schwinger-DeWitt-type expansion in order to renormalize them
Optimal multicopy asymmetric Gaussian cloning of coherent states
International Nuclear Information System (INIS)
Fiurasek, Jaromir; Cerf, Nicolas J.
2007-01-01
We investigate the asymmetric Gaussian cloning of coherent states which produces M copies from N input replicas in such a way that the fidelity of each copy may be different. We show that the optimal asymmetric Gaussian cloning can be performed with a single phase-insensitive amplifier and an array of beam splitters. We obtain a simple analytical expression characterizing the set of optimal asymmetric Gaussian cloning machines and prove the optimality of these cloners using the formalism of Gaussian completely positive maps and semidefinite programming techniques. We also present an alternative implementation of the asymmetric cloning machine where the phase-insensitive amplifier is replaced with a beam splitter, heterodyne detector, and feedforward
Quantifying entanglement in two-mode Gaussian states
Tserkis, Spyros; Ralph, Timothy C.
2017-12-01
Entangled two-mode Gaussian states are a key resource for quantum information technologies such as teleportation, quantum cryptography, and quantum computation, so quantification of Gaussian entanglement is an important problem. Entanglement of formation is unanimously considered a proper measure of quantum correlations, but for arbitrary two-mode Gaussian states no analytical form is currently known. In contrast, logarithmic negativity is a measure that is straightforward to calculate and so has been adopted by most researchers, even though it is a less faithful quantifier. In this work, we derive an analytical lower bound for entanglement of formation of generic two-mode Gaussian states, which becomes tight for symmetric states and for states with balanced correlations. We define simple expressions for entanglement of formation in physically relevant situations and use these to illustrate the problematic behavior of logarithmic negativity, which can lead to spurious conclusions.
Super-resolving random-Gaussian apodized photon sieve.
Sabatyan, Arash; Roshaninejad, Parisa
2012-09-10
A novel apodized photon sieve is presented in which random dense Gaussian distribution is implemented to modulate the pinhole density in each zone. The random distribution in dense Gaussian distribution causes intrazone discontinuities. Also, the dense Gaussian distribution generates a substantial number of pinholes in order to form a large degree of overlap between the holes in a few innermost zones of the photon sieve; thereby, clear zones are formed. The role of the discontinuities on the focusing properties of the photon sieve is examined as well. Analysis shows that secondary maxima have evidently been suppressed, transmission has increased enormously, and the central maxima width is approximately unchanged in comparison to the dense Gaussian distribution. Theoretical results have been completely verified by experiment.
Mimicking an amplitude damping channel for Laguerre Gaussian Modes
CSIR Research Space (South Africa)
Dudley, Angela L
2010-10-01
Full Text Available An amplitude damping channel for Laguerre-Gaussian (LG) modes is presented. Experimentally the action of the channel on LG modes is in good agreement with that predicted theoretically....
Scalable Gaussian Processes and the search for exoplanets
CERN. Geneva
2015-01-01
Gaussian Processes are a class of non-parametric models that are often used to model stochastic behavior in time series or spatial data. A major limitation for the application of these models to large datasets is the computational cost. The cost of a single evaluation of the model likelihood scales as the third power of the number of data points. In the search for transiting exoplanets, the datasets of interest have tens of thousands to millions of measurements with uneven sampling, rendering naive application of a Gaussian Process model impractical. To attack this problem, we have developed robust approximate methods for Gaussian Process regression that can be applied at this scale. I will describe the general problem of Gaussian Process regression and offer several applicable use cases. Finally, I will present our work on scaling this model to the exciting field of exoplanet discovery and introduce a well-tested open source implementation of these new methods.
Mescia, L.; Bia, P.; Caratelli, D.
2017-01-01
A novel two-dimensional (2-D) finite-difference timedomain algorithm for modeling ultrawideband pulse propagation in arbitrary dispersive soils is presented. The soil dispersion is modeled by general power law series representation, accounting for multiple higher order dispersive relaxation
Schweiner, Frank; Laturner, Jeanine; Main, Jörg; Wunner, Günter
2017-11-01
Until now only for specific crossovers between Poissonian statistics (P), the statistics of a Gaussian orthogonal ensemble (GOE), or the statistics of a Gaussian unitary ensemble (GUE) have analytical formulas for the level spacing distribution function been derived within random matrix theory. We investigate arbitrary crossovers in the triangle between all three statistics. To this aim we propose an according formula for the level spacing distribution function depending on two parameters. Comparing the behavior of our formula for the special cases of P→GUE, P→GOE, and GOE→GUE with the results from random matrix theory, we prove that these crossovers are described reasonably. Recent investigations by F. Schweiner et al. [Phys. Rev. E 95, 062205 (2017)2470-004510.1103/PhysRevE.95.062205] have shown that the Hamiltonian of magnetoexcitons in cubic semiconductors can exhibit all three statistics in dependence on the system parameters. Evaluating the numerical results for magnetoexcitons in dependence on the excitation energy and on a parameter connected with the cubic valence band structure and comparing the results with the formula proposed allows us to distinguish between regular and chaotic behavior as well as between existent or broken antiunitary symmetries. Increasing one of the two parameters, transitions between different crossovers, e.g., from the P→GOE to the P→GUE crossover, are observed and discussed.
Energy Technology Data Exchange (ETDEWEB)
Kenfack, Lionel Tenemeza, E-mail: kenfacklionel300@gmail.com [Mesoscopic and Multilayer Structure Laboratory, Department of Physics, Faculty of Science, University of Dschang, PO Box: 67 Dschang (Cameroon); Tchoffo, Martin; Fai, Lukong Cornelius [Mesoscopic and Multilayer Structure Laboratory, Department of Physics, Faculty of Science, University of Dschang, PO Box: 67 Dschang (Cameroon); Fouokeng, Georges Collince [Mesoscopic and Multilayer Structure Laboratory, Department of Physics, Faculty of Science, University of Dschang, PO Box: 67 Dschang (Cameroon); Laboratoire de Génie des Matériaux, Pôle Recherche-Innovation-Entrepreneuriat (PRIE), Institut Universitaire de la Côte, BP 3001 Douala (Cameroon)
2017-04-15
We address the entanglement dynamics of a three-qubit system interacting with a classical fluctuating environment described either by a Gaussian or non-Gaussian noise in three different configurations namely: common, independent and mixed environments. Specifically, we focus on the Ornstein-Uhlenbeck (OU) noise and the random telegraph noise (RTN). The qubits are prepared in a state composed of a Greenberger-Horne-Zeilinger (GHZ) and a W state. With the help of the tripartite negativity, we show that the entanglement evolution is not only affected by the type of system-environment coupling but also by the kind and the memory properties of the considered noise. We also compared the dynamics induced by the two kinds of noise and we find that even if both noises have a Lorentzian spectrum, the effects of the OU noise cannot be in a simple way deduced from those of the RTN and vice-versa. In addition, we show that the entanglement can be indefinitely preserved when the qubits are coupled to the environmental noise in a common environment (CE). Finally, the presence or absence of peculiar phenomena such as entanglement revivals (ER) and entanglement sudden death (ESD) is observed.
International Nuclear Information System (INIS)
Kenfack, Lionel Tenemeza; Tchoffo, Martin; Fai, Lukong Cornelius; Fouokeng, Georges Collince
2017-01-01
We address the entanglement dynamics of a three-qubit system interacting with a classical fluctuating environment described either by a Gaussian or non-Gaussian noise in three different configurations namely: common, independent and mixed environments. Specifically, we focus on the Ornstein-Uhlenbeck (OU) noise and the random telegraph noise (RTN). The qubits are prepared in a state composed of a Greenberger-Horne-Zeilinger (GHZ) and a W state. With the help of the tripartite negativity, we show that the entanglement evolution is not only affected by the type of system-environment coupling but also by the kind and the memory properties of the considered noise. We also compared the dynamics induced by the two kinds of noise and we find that even if both noises have a Lorentzian spectrum, the effects of the OU noise cannot be in a simple way deduced from those of the RTN and vice-versa. In addition, we show that the entanglement can be indefinitely preserved when the qubits are coupled to the environmental noise in a common environment (CE). Finally, the presence or absence of peculiar phenomena such as entanglement revivals (ER) and entanglement sudden death (ESD) is observed.
Non-gaussianity versus nonlinearity of cosmological perturbations.
Verde, L
2001-06-01
Following the discovery of the cosmic microwave background, the hot big-bang model has become the standard cosmological model. In this theory, small primordial fluctuations are subsequently amplified by gravity to form the large-scale structure seen today. Different theories for unified models of particle physics, lead to different predictions for the statistical properties of the primordial fluctuations, that can be divided in two classes: gaussian and non-gaussian. Convincing evidence against or for gaussian initial conditions would rule out many scenarios and point us toward a physical theory for the origin of structures. The statistical distribution of cosmological perturbations, as we observe them, can deviate from the gaussian distribution in several different ways. Even if perturbations start off gaussian, nonlinear gravitational evolution can introduce non-gaussian features. Additionally, our knowledge of the Universe comes principally from the study of luminous material such as galaxies, but galaxies might not be faithful tracers of the underlying mass distribution. The relationship between fluctuations in the mass and in the galaxies distribution (bias), is often assumed to be local, but could well be nonlinear. Moreover, galaxy catalogues use the redshift as third spatial coordinate: the resulting redshift-space map of the galaxy distribution is nonlinearly distorted by peculiar velocities. Nonlinear gravitational evolution, biasing, and redshift-space distortion introduce non-gaussianity, even in an initially gaussian fluctuation field. I investigate the statistical tools that allow us, in principle, to disentangle the above different effects, and the observational datasets we require to do so in practice.
GAUSSIAN 76: an ab initio molecular orbital program
International Nuclear Information System (INIS)
Binkley, J.S.; Whiteside, R.; Hariharan, P.C.; Seeger, R.; Hehre, W.J.; Lathan, W.A.; Newton, M.D.; Ditchfield, R.; Pople, J.A.
Gaussian 76 is a general-purpose computer program for ab initio Hartree-Fock molecular orbital calculations. It can handle basis sets involving s, p and d-type gaussian functions. Certain standard sets (STO-3G, 4-31G, 6-31G*, etc.) are stored internally for easy use. Closed shell (RHF) or unrestricted open shell (UHF) wave functions can be obtained. Facilities are provided for geometry optimization to potential minima and for limited potential surface scans
Gaussian Process Regression for WDM System Performance Prediction
DEFF Research Database (Denmark)
Wass, Jesper; Thrane, Jakob; Piels, Molly
2017-01-01
Gaussian process regression is numerically and experimentally investigated to predict the bit error rate of a 24 x 28 CiBd QPSK WDM system. The proposed method produces accurate predictions from multi-dimensional and sparse measurement data.......Gaussian process regression is numerically and experimentally investigated to predict the bit error rate of a 24 x 28 CiBd QPSK WDM system. The proposed method produces accurate predictions from multi-dimensional and sparse measurement data....
Revisiting non-Gaussianity from non-attractor inflation models
Cai, Yi-Fu; Chen, Xingang; Namjoo, Mohammad Hossein; Sasaki, Misao; Wang, Dong-Gang; Wang, Ziwei
2018-05-01
Non-attractor inflation is known as the only single field inflationary scenario that can violate non-Gaussianity consistency relation with the Bunch-Davies vacuum state and generate large local non-Gaussianity. However, it is also known that the non-attractor inflation by itself is incomplete and should be followed by a phase of slow-roll attractor. Moreover, there is a transition process between these two phases. In the past literature, this transition was approximated as instant and the evolution of non-Gaussianity in this phase was not fully studied. In this paper, we follow the detailed evolution of the non-Gaussianity through the transition phase into the slow-roll attractor phase, considering different types of transition. We find that the transition process has important effect on the size of the local non-Gaussianity. We first compute the net contribution of the non-Gaussianities at the end of inflation in canonical non-attractor models. If the curvature perturbations keep evolving during the transition—such as in the case of smooth transition or some sharp transition scenarios—the Script O(1) local non-Gaussianity generated in the non-attractor phase can be completely erased by the subsequent evolution, although the consistency relation remains violated. In extremal cases of sharp transition where the super-horizon modes freeze immediately right after the end of the non-attractor phase, the original non-attractor result can be recovered. We also study models with non-canonical kinetic terms, and find that the transition can typically contribute a suppression factor in the squeezed bispectrum, but the final local non-Gaussianity can still be made parametrically large.
Identification and estimation of non-Gaussian structural vector autoregressions
DEFF Research Database (Denmark)
Lanne, Markku; Meitz, Mika; Saikkonen, Pentti
-Gaussian components is, without any additional restrictions, identified and leads to (essentially) unique impulse responses. We also introduce an identification scheme under which the maximum likelihood estimator of the non-Gaussian SVAR model is consistent and asymptotically normally distributed. As a consequence......, additional economic identifying restrictions can be tested. In an empirical application, we find a negative impact of a contractionary monetary policy shock on financial markets, and clearly reject the commonly employed recursive identifying restrictions....
Mcmanus, M. L.
1979-01-01
Dispersal flights of selected species of forest insects which are associated with periodic outbreaks of pests that occur over large contiguous forested areas are discussed. Gypsy moths, spruce budworms, and forest tent caterpillars were studied for their massive migrations in forested areas. Results indicate that large dispersals into forested areas are due to the females, except in the case of the gypsy moth.
Improved new generation dispersants
International Nuclear Information System (INIS)
Anon.
1993-01-01
The use of dispersants to combat oil spills has attracted controversy over the years, and there has been a number of accusations of the chemicals involved doing more harm than good. A new study by the International Petroleum Industry Environmental Conservation Association discusses the positive and the negatives of dispersant use to be considered when drawing up spill contingency plans. (author)
Middleton, B.; Van Diggelen, R.; Jensen, K.
2006-01-01
Question: How does seed dispersal reduce fen isolation and contribute to biodiversity? Location: European and North American fens. Methods: This paper reviews the literature on seed dispersal to fens. Results: Landscape fragmentation may reduce dispersal opportunities thereby isolating fens and reducing genetic exchange. Species in fragmented wetlands may have lower reproductive success, which can lead to biodiversity loss. While fens may have always been relatively isolated from each other, they have become increasingly fragmented in modern times within agricultural and urban landscapes in both Europe and North America. Dispersal by water, animals and wind has been hampered by changes related to development in landscapes surrounding fens. Because the seeds of certain species are long-lived in the seed bank, frequent episodes of dispersal are not always necessary to maintain the biodiversity of fens. However, of particular concern to restoration is that some dominant species, such as the tussock sedge Carex stricta, may not disperse readily between fens. Conclusions: Knowledge of seed dispersal can be used to maintain and restore the biodiversity of fens in fragmented landscapes. Given that development has fragmented landscapes and that this situation is not likely to change, the dispersal of seeds might be enhanced by moving hay or cattle from fens to damaged sites, or by reestablishing lost hydrological connections. ?? IAVS; Opulus Press.
International Nuclear Information System (INIS)
Havens, J.; Spicer, T.
1990-09-01
The topical report is one of a series on the development of methods for LNG vapor dispersion prediction for regulatory application. The results indicate that the DEGADIS model is superior both phenomenologically and in performance to the Gaussian line source model promulgated in 49 CFR 193 for LNG vapor dispersion simulation. Availability of the DEGADIS model for VAX and IBM-PC formats provides for wider use of the model and greater potential for industry and regulatory acceptance. The acceptance is seen as an important interim objective while research continues on vapor dispersion estimation methods which provide for effects of vapor detention systems, turbulence induced by plant structure, and plant/area topographical features
Inverse modelling of atmospheric tracers: non-Gaussian methods and second-order sensitivity analysis
Directory of Open Access Journals (Sweden)
M. Bocquet
2008-02-01
Full Text Available For a start, recent techniques devoted to the reconstruction of sources of an atmospheric tracer at continental scale are introduced. A first method is based on the principle of maximum entropy on the mean and is briefly reviewed here. A second approach, which has not been applied in this field yet, is based on an exact Bayesian approach, through a maximum a posteriori estimator. The methods share common grounds, and both perform equally well in practice. When specific prior hypotheses on the sources are taken into account such as positivity, or boundedness, both methods lead to purposefully devised cost-functions. These cost-functions are not necessarily quadratic because the underlying assumptions are not Gaussian. As a consequence, several mathematical tools developed in data assimilation on the basis of quadratic cost-functions in order to establish a posteriori analysis, need to be extended to this non-Gaussian framework. Concomitantly, the second-order sensitivity analysis needs to be adapted, as well as the computations of the averaging kernels of the source and the errors obtained in the reconstruction. All of these developments are applied to a real case of tracer dispersion: the European Tracer Experiment [ETEX]. Comparisons are made between a least squares cost function (similar to the so-called 4D-Var approach and a cost-function which is not based on Gaussian hypotheses. Besides, the information content of the observations which is used in the reconstruction is computed and studied on the application case. A connection with the degrees of freedom for signal is also established. As a by-product of these methodological developments, conclusions are drawn on the information content of the ETEX dataset as seen from the inverse modelling point of view.
Current inversion induced by colored non-Gaussian noise
International Nuclear Information System (INIS)
Bag, Bidhan Chandra; Hu, Chin-Kung
2009-01-01
We study a stochastic process driven by colored non-Gaussian noises. For the flashing ratchet model we find that there is a current inversion in the variation of the current with the half-cycle period which accounts for the potential on–off operation. The current inversion almost disappears if one switches from non-Gaussian (NG) to Gaussian (G) noise. We also find that at low value of the asymmetry parameter of the potential the mobility controlled current is more negative for NG noise as compared to G noise. But at large magnitude of the parameter the diffusion controlled positive current is higher for the former than for the latter. On increasing the noise correlation time (τ), keeping the noise strength fixed, the mean velocity of a particle first increases and then decreases after passing through a maximum if the noise is non-Gaussian. For Gaussian noise, the current monotonically decreases. The current increases with the noise parameter p, 0< p<5/3, which is 1 for Gaussian noise
Passivity and practical work extraction using Gaussian operations
International Nuclear Information System (INIS)
Brown, Eric G; Huber, Marcus; Friis, Nicolai
2016-01-01
Quantum states that can yield work in a cyclical Hamiltonian process form one of the primary resources in the context of quantum thermodynamics. Conversely, states whose average energy cannot be lowered by unitary transformations are called passive. However, while work may be extracted from non-passive states using arbitrary unitaries, the latter may be hard to realize in practice. It is therefore pertinent to consider the passivity of states under restricted classes of operations that can be feasibly implemented. Here, we ask how restrictive the class of Gaussian unitaries is for the task of work extraction. We investigate the notion of Gaussian passivity, that is, we present necessary and sufficient criteria identifying all states whose energy cannot be lowered by Gaussian unitaries. For all other states we give a prescription for the Gaussian operations that extract the maximal amount of energy. Finally, we show that the gap between passivity and Gaussian passivity is maximal, i.e., Gaussian-passive states may still have a maximal amount of energy that is extractable by arbitrary unitaries, even under entropy constraints. (paper)
Superstatistical generalised Langevin equation: non-Gaussian viscoelastic anomalous diffusion
Ślęzak, Jakub; Metzler, Ralf; Magdziarz, Marcin
2018-02-01
Recent advances in single particle tracking and supercomputing techniques demonstrate the emergence of normal or anomalous, viscoelastic diffusion in conjunction with non-Gaussian distributions in soft, biological, and active matter systems. We here formulate a stochastic model based on a generalised Langevin equation in which non-Gaussian shapes of the probability density function and normal or anomalous diffusion have a common origin, namely a random parametrisation of the stochastic force. We perform a detailed analysis demonstrating how various types of parameter distributions for the memory kernel result in exponential, power law, or power-log law tails of the memory functions. The studied system is also shown to exhibit a further unusual property: the velocity has a Gaussian one point probability density but non-Gaussian joint distributions. This behaviour is reflected in the relaxation from a Gaussian to a non-Gaussian distribution observed for the position variable. We show that our theoretical results are in excellent agreement with stochastic simulations.
Non-Gaussianity from inflation: theory and observations
Bartolo, N.; Komatsu, E.; Matarrese, S.; Riotto, A.
2004-11-01
This is a review of models of inflation and of their predictions for the primordial non-Gaussianity in the density perturbations which are thought to be at the origin of structures in the Universe. Non-Gaussianity emerges as a key observable to discriminate among competing scenarios for the generation of cosmological perturbations and is one of the primary targets of present and future Cosmic Microwave Background satellite missions. We give a detailed presentation of the state-of-the-art of the subject of non-Gaussianity, both from the theoretical and the observational point of view, and provide all the tools necessary to compute at second order in perturbation theory the level of non-Gaussianity in any model of cosmological perturbations. We discuss the new wave of models of inflation, which are firmly rooted in modern particle physics theory and predict a significant amount of non-Gaussianity. The review is addressed to both astrophysicists and particle physicists and contains useful tables which summarize the theoretical and observational results regarding non-Gaussianity.
Back to Normal! Gaussianizing posterior distributions for cosmological probes
Schuhmann, Robert L.; Joachimi, Benjamin; Peiris, Hiranya V.
2014-05-01
We present a method to map multivariate non-Gaussian posterior probability densities into Gaussian ones via nonlinear Box-Cox transformations, and generalizations thereof. This is analogous to the search for normal parameters in the CMB, but can in principle be applied to any probability density that is continuous and unimodal. The search for the optimally Gaussianizing transformation amongst the Box-Cox family is performed via a maximum likelihood formalism. We can judge the quality of the found transformation a posteriori: qualitatively via statistical tests of Gaussianity, and more illustratively by how well it reproduces the credible regions. The method permits an analytical reconstruction of the posterior from a sample, e.g. a Markov chain, and simplifies the subsequent joint analysis with other experiments. Furthermore, it permits the characterization of a non-Gaussian posterior in a compact and efficient way. The expression for the non-Gaussian posterior can be employed to find analytic formulae for the Bayesian evidence, and consequently be used for model comparison.
UFOMOD - atmospheric dispersion and deposition
International Nuclear Information System (INIS)
Panitz, H.J.; Matzerath, C.; Paesler-Sauer, J.
1989-10-01
The report gives an introduction into the modelling of atmospheric dispersion and deposition which has been implemented in the new program system UFOMOD for assessing the consequences after nuclear accidents. According to the new structure of UFOMOD, different trajectory models with ranges of validity near to the site and at far distances are applied. Emphasis is laid on the description of the segmented plume model MUSEMET and its affilated submodels, being the removal of activity from the cloud by dry and wet deposition, and special effects like plume rise and the behaviour of plumes released into building wakes. In addition, the evaluation of γ-dose correction factors to take account of the finite extent of the radioactive plume in the near range (up to about 20 km) are described. Only brief introductions are given into the principles of the other models available: the puff model RIMPUFF, the long-range puff model MESOS, and the special straight-line Gaussian model ISOLA which are used if low-level long-duration releases are considered. To define starting times of weather sequences and the probabilities of occurrence of these sequences, it is convenient to perform stratified sampling. Therefore, the preprocessing program package METSAM has been developed to perform for generic ACAs a random sampling of weather sequences out off a population of classified weather conditions. The sampling procedure and a detailed input/output (I/O) description is presented and an additional appendix, respectively. A general overview on the I/O structure of MUSEMET as well as a brief user guide to run the KfK version of the MESOS code are also given in the appendix. (orig.) [de
Dispersion of some fission radionuclides during routine releases from ETRR-2 reactor
International Nuclear Information System (INIS)
Essa, S.M.K.; Mayhoub, A.B.; Mubarak, F.; Abedel Fattah, A.T.; Atia, S.
2005-01-01
One of the most important parameters in plume dispersion modeling is the plume growth (dispersion coefficients δ). Different models for estimating dispersion parameters are discussed to establish the relative importance of one over the others. Comparisons were made between power-law function, standard, split sigma, and split sigma theta methods. We use the double Gaussian expression for calculating concentration in this comparison. The results show that, with low wind speed (<2 m/s) split sigma and split sigma theta methods give much better results than other methods. While with wind speed greater than 2 m/s the power-law functions methods give more plausible results
Channel Capacity Calculation at Large SNR and Small Dispersion within Path-Integral Approach
Reznichenko, A. V.; Terekhov, I. S.
2018-04-01
We consider the optical fiber channel modelled by the nonlinear Shrödinger equation with additive white Gaussian noise. Using Feynman path-integral approach for the model with small dispersion we find the first nonzero corrections to the conditional probability density function and the channel capacity estimations at large signal-to-noise ratio. We demonstrate that the correction to the channel capacity in small dimensionless dispersion parameter is quadratic and positive therefore increasing the earlier calculated capacity for a nondispersive nonlinear optical fiber channel in the intermediate power region. Also for small dispersion case we find the analytical expressions for simple correlators of the output signals in our noisy channel.
Rabbani, Hossein; Sonka, Milan; Abramoff, Michael D
2013-01-01
In this paper, MMSE estimator is employed for noise-free 3D OCT data recovery in 3D complex wavelet domain. Since the proposed distribution for noise-free data plays a key role in the performance of MMSE estimator, a priori distribution for the pdf of noise-free 3D complex wavelet coefficients is proposed which is able to model the main statistical properties of wavelets. We model the coefficients with a mixture of two bivariate Gaussian pdfs with local parameters which are able to capture the heavy-tailed property and inter- and intrascale dependencies of coefficients. In addition, based on the special structure of OCT images, we use an anisotropic windowing procedure for local parameters estimation that results in visual quality improvement. On this base, several OCT despeckling algorithms are obtained based on using Gaussian/two-sided Rayleigh noise distribution and homomorphic/nonhomomorphic model. In order to evaluate the performance of the proposed algorithm, we use 156 selected ROIs from 650 × 512 × 128 OCT dataset in the presence of wet AMD pathology. Our simulations show that the best MMSE estimator using local bivariate mixture prior is for the nonhomomorphic model in the presence of Gaussian noise which results in an improvement of 7.8 ± 1.7 in CNR.
Directory of Open Access Journals (Sweden)
Hossein Rabbani
2013-01-01
Full Text Available In this paper, MMSE estimator is employed for noise-free 3D OCT data recovery in 3D complex wavelet domain. Since the proposed distribution for noise-free data plays a key role in the performance of MMSE estimator, a priori distribution for the pdf of noise-free 3D complex wavelet coefficients is proposed which is able to model the main statistical properties of wavelets. We model the coefficients with a mixture of two bivariate Gaussian pdfs with local parameters which are able to capture the heavy-tailed property and inter- and intrascale dependencies of coefficients. In addition, based on the special structure of OCT images, we use an anisotropic windowing procedure for local parameters estimation that results in visual quality improvement. On this base, several OCT despeckling algorithms are obtained based on using Gaussian/two-sided Rayleigh noise distribution and homomorphic/nonhomomorphic model. In order to evaluate the performance of the proposed algorithm, we use 156 selected ROIs from 650 × 512 × 128 OCT dataset in the presence of wet AMD pathology. Our simulations show that the best MMSE estimator using local bivariate mixture prior is for the nonhomomorphic model in the presence of Gaussian noise which results in an improvement of 7.8 ± 1.7 in CNR.
Gu, Wenjun; Zhang, Weizhi; Wang, Jin; Amini Kashani, M. R.; Kavehrad, Mohsen
2015-01-01
Over the past decade, location based services (LBS) have found their wide applications in indoor environments, such as large shopping malls, hospitals, warehouses, airports, etc. Current technologies provide wide choices of available solutions, which include Radio-frequency identification (RFID), Ultra wideband (UWB), wireless local area network (WLAN) and Bluetooth. With the rapid development of light-emitting-diodes (LED) technology, visible light communications (VLC) also bring a practical approach to LBS. As visible light has a better immunity against multipath effect than radio waves, higher positioning accuracy is achieved. LEDs are utilized both for illumination and positioning purpose to realize relatively lower infrastructure cost. In this paper, an indoor positioning system using VLC is proposed, with LEDs as transmitters and photo diodes as receivers. The algorithm for estimation is based on received-signalstrength (RSS) information collected from photo diodes and trilateration technique. By appropriately making use of the characteristics of receiver movements and the property of trilateration, estimation on three-dimensional (3-D) coordinates is attained. Filtering technique is applied to enable tracking capability of the algorithm, and a higher accuracy is reached compare to raw estimates. Gaussian mixture Sigma-point particle filter (GM-SPPF) is proposed for this 3-D system, which introduces the notion of Gaussian Mixture Model (GMM). The number of particles in the filter is reduced by approximating the probability distribution with Gaussian components.
Fractal scattering of Gaussian solitons in directional couplers with logarithmic nonlinearities
Energy Technology Data Exchange (ETDEWEB)
Teixeira, Rafael M.P.; Cardoso, Wesley B., E-mail: wesleybcardoso@gmail.com
2016-08-12
In this paper we study the interaction of Gaussian solitons in a dispersive and nonlinear media with log-law nonlinearity. The model is described by the coupled logarithmic nonlinear Schrödinger equations, which is a nonintegrable system that allows the observation of a very rich scenario in the collision patterns. By employing a variational approach and direct numerical simulations, we observe a fractal-scattering phenomenon from the exit velocities of each soliton as a function of the input velocities. Furthermore, we introduce a linearization model to identify the position of the reflection/transmission window that emerges within the chaotic region. This enables us the possibility of controlling the scattering of solitons as well as the lifetime of bound states. - Highlights: • We study the interaction of Gaussian solitons in a system with log-law nonlinearity. • The model is described by the coupled logarithmic nonlinear Schrödinger equations. • We observe a fractal-scattering phenomenon of the solitons.
Directory of Open Access Journals (Sweden)
Koc Ut-Va
2005-01-01
Full Text Available The widely-used LMS algorithm for coefficient updates in adaptive (feedforward/decision-feedback equalizers is found to be suboptimal for ASE-dominant systems but various coefficient-dithering approaches suffer from slow adaptation rate without guarantee of convergence. In view of the non-Gaussian nature of optical noise after the square-law optoelectronic conversion, we propose to apply the higher-order least-mean 2 th-order (LMN algorithms resulting in OSNR penalty which is 1.5–2 dB less than that of LMS. Furthermore, combined with adjustable slicer threshold control, the proposed equalizer structures are demonstrated through extensive Monte Carlo simulations to achieve better performance.
Measurement of the broadening and depolarization of a Gaussian beam to transmit in fog water
International Nuclear Information System (INIS)
Serrano, G.; Reynoso, E; Davila, J. A.
2012-01-01
In this paper, we develop a controlled experimental environment in the laboratory, a waterproof camera where it will introduce artificial fog and become a beam of linearly polarized Gaussian laser light, creating sprawl, broadening and depolarization is studied with a system capable of measuring such phenomena. Most studies on dispersion have focused on the lobes of backscattering, however the correct conditions with the incident light beam a substantial fraction of light is scattered in the forward direction forming a widened light lobe. With this light lobe formed could be studied with extreme precision many factors such as the existence of single or multiple scattering and the amount by which this is carried out. This is of great importance in the estimation of lidar returns because these foundations can learn important information such as extinction and backscatter coefficients, particulate pollutants in the atmosphere and thus understand the operation model of nature. (Author)
Theory of stochastic space-dependent neutron kinetics with a Gaussian parametric excitation
International Nuclear Information System (INIS)
Saito, K.
1980-01-01
Neutron kinetics and statics in a multiplying medium with a statistically fluctuating reactivity are unified and systematically studied by applying the Novikov-Furutsu formula. The parametric or multiplicative noise is spatially distributed and of Gaussian nature with an arbitrary spectral profile. It is found that the noise introduces a new definite production term into the conventional balance equation for the mean neutron number. The term is characterized by the magnitude and the correlation function of the random excitation. Its relaxation phenomena bring forth a non-Markoffian or a memory effect, which is conceptualised by introducing 'pseudo-precursors' or 'pseudo-delayed neutrons'. By using the concept, some typical reactor physical problems are solved; they are (1) reactivity and flux perturbation originating from the random dispersal of core materials and (2) analysis of neutron decay mode and it relaxation constant, and derivation of the corresponding new inhour equation. (author)
Sensitivity analysis of an operational advanced Gaussian model to different turbulent regimes
International Nuclear Information System (INIS)
Mangia, C.; Rizza, U.; Tirabassi, T.
1998-01-01
A non-reactive air pollution model evaluating ground level concentration is presented. It relies on a new Gaussian formulation (Lupini, R. and Tirabassi, T., J. Appl. Meteor., 20 (1981) 565-570; Tirabassi, T. and Rizza, U., Atmos. Environ., 28 (1994) 611-615) for transport and vertical diffusion in the Atmospheric Boundary Layer (ABL). In this formulation, the source height is replaced by a virtual height expressed by simple functions of meteorological variables. The model accepts a general profile of wind u(z) and eddy diffusivity coefficient K z . The lateral dispersion coefficient is based on Taylor's theory (Taylor, G. I., Proc. London Math. Soc., 20 (1921) 196-204). The turbulence in the ABL is subdivided into various regimes, each characterized by different parameters for length and velocity scales. The model performances under unstable conditions have been tested utilizing two different data sets
Accurate Recovery of H i Velocity Dispersion from Radio Interferometers
Energy Technology Data Exchange (ETDEWEB)
Ianjamasimanana, R. [Max-Planck Institut für Astronomie, Königstuhl 17, D-69117, Heidelberg (Germany); Blok, W. J. G. de [Netherlands Institute for Radio Astronomy (ASTRON), Postbus 2, 7990 AA Dwingeloo (Netherlands); Heald, George H., E-mail: roger@mpia.de, E-mail: blok@astron.nl, E-mail: George.Heald@csiro.au [Kapteyn Astronomical Institute, University of Groningen, P.O. Box 800, 9700 AV, Groningen (Netherlands)
2017-05-01
Gas velocity dispersion measures the amount of disordered motion of a rotating disk. Accurate estimates of this parameter are of the utmost importance because the parameter is directly linked to disk stability and star formation. A global measure of the gas velocity dispersion can be inferred from the width of the atomic hydrogen (H i) 21 cm line. We explore how several systematic effects involved in the production of H i cubes affect the estimate of H i velocity dispersion. We do so by comparing the H i velocity dispersion derived from different types of data cubes provided by The H i Nearby Galaxy Survey. We find that residual-scaled cubes best recover the H i velocity dispersion, independent of the weighting scheme used and for a large range of signal-to-noise ratio. For H i observations, where the dirty beam is substantially different from a Gaussian, the velocity dispersion values are overestimated unless the cubes are cleaned close to (e.g., ∼1.5 times) the noise level.
Dispersal and metapopulation stability
Directory of Open Access Journals (Sweden)
Shaopeng Wang
2015-10-01
Full Text Available Metapopulation dynamics are jointly regulated by local and spatial factors. These factors may affect the dynamics of local populations and of the entire metapopulation differently. Previous studies have shown that dispersal can stabilize local populations; however, as dispersal also tends to increase spatial synchrony, its net effect on metapopulation stability has been controversial. Here we present a simple metapopulation model to study how dispersal, in interaction with other spatial and local processes, affects the temporal variability of metapopulations in a stochastic environment. Our results show that in homogeneous metapopulations, the local stabilizing and spatial synchronizing effects of dispersal cancel each other out, such that dispersal has no effect on metapopulation variability. This result is robust to moderate heterogeneities in local and spatial parameters. When local and spatial dynamics exhibit high heterogeneities, however, dispersal can either stabilize or destabilize metapopulation dynamics through various mechanisms. Our findings have important theoretical and practical implications. We show that dispersal functions as a form of spatial intraspecific mutualism in metapopulation dynamics and that its effect on metapopulation stability is opposite to that of interspecific competition on local community stability. Our results also suggest that conservation corridors should be designed with appreciation of spatial heterogeneities in population dynamics in order to maximize metapopulation stability.
Fast pencil beam dose calculation for proton therapy using a double-Gaussian beam model
Directory of Open Access Journals (Sweden)
Joakim eda Silva
2015-12-01
Full Text Available The highly conformal dose distributions produced by scanned proton pencil beams are more sensitive to motion and anatomical changes than those produced by conventional radiotherapy. The ability to calculate the dose in real time as it is being delivered would enable, for example, online dose monitoring, and is therefore highly desirable. We have previously described an implementation of a pencil beam algorithm running on graphics processing units (GPUs intended specifically for online dose calculation. Here we present an extension to the dose calculation engine employing a double-Gaussian beam model to better account for the low-dose halo. To the best of our knowledge, it is the first such pencil beam algorithm for proton therapy running on a GPU. We employ two different parametrizations for the halo dose, one describing the distribution of secondary particles from nuclear interactions found in the literature and one relying on directly fitting the model to Monte Carlo simulations of pencil beams in water. Despite the large width of the halo contribution, we show how in either case the second Gaussian can be included whilst prolonging the calculation of the investigated plans by no more than 16%, or the calculation of the most time-consuming energy layers by about 25%. Further, the calculation time is relatively unaffected by the parametrization used, which suggests that these results should hold also for different systems. Finally, since the implementation is based on an algorithm employed by a commercial treatment planning system, it is expected that with adequate tuning, it should be able to reproduce the halo dose from a general beam line with sufficient accuracy.
Wen, Xian-Huan; Gómez-Hernández, J. Jaime
1998-03-01
The macrodispersion of an inert solute in a 2-D heterogeneous porous media is estimated numerically in a series of fields of varying heterogeneity. Four different random function (RF) models are used to model log-transmissivity (ln T) spatial variability, and for each of these models, ln T variance is varied from 0.1 to 2.0. The four RF models share the same univariate Gaussian histogram and the same isotropic covariance, but differ from one another in terms of the spatial connectivity patterns at extreme transmissivity values. More specifically, model A is a multivariate Gaussian model for which, by definition, extreme values (both high and low) are spatially uncorrelated. The other three models are non-multi-Gaussian: model B with high connectivity of high extreme values, model C with high connectivity of low extreme values, and model D with high connectivities of both high and low extreme values. Residence time distributions (RTDs) and macrodispersivities (longitudinal and transverse) are computed on ln T fields corresponding to the different RF models, for two different flow directions and at several scales. They are compared with each other, as well as with predicted values based on first-order analytical results. Numerically derived RTDs and macrodispersivities for the multi-Gaussian model are in good agreement with analytically derived values using first-order theories for log-transmissivity variance up to 2.0. The results from the non-multi-Gaussian models differ from each other and deviate largely from the multi-Gaussian results even when ln T variance is small. RTDs in non-multi-Gaussian realizations with high connectivity at high extreme values display earlier breakthrough than in multi-Gaussian realizations, whereas later breakthrough and longer tails are observed for RTDs from non-multi-Gaussian realizations with high connectivity at low extreme values. Longitudinal macrodispersivities in the non-multi-Gaussian realizations are, in general, larger than
MixSim : An R Package for Simulating Data to Study Performance of Clustering Algorithms
Directory of Open Access Journals (Sweden)
Volodymyr Melnykov
2012-11-01
Full Text Available The R package MixSim is a new tool that allows simulating mixtures of Gaussian distributions with different levels of overlap between mixture components. Pairwise overlap, defined as a sum of two misclassification probabilities, measures the degree of interaction between components and can be readily employed to control the clustering complexity of datasets simulated from mixtures. These datasets can then be used for systematic performance investigation of clustering and finite mixture modeling algorithms. Among other capabilities of MixSim, there are computing the exact overlap for Gaussian mixtures, simulating Gaussian and non-Gaussian data, simulating outliers and noise variables, calculating various measures of agreement between two partitionings, and constructing parallel distribution plots for the graphical display of finite mixture models. All features of the package are illustrated in great detail. The utility of the package is highlighted through a small comparison study of several popular clustering algorithms.
Kittisuwan, Pichid
2015-03-01
The application of image processing in industry has shown remarkable success over the last decade, for example, in security and telecommunication systems. The denoising of natural image corrupted by Gaussian noise is a classical problem in image processing. So, image denoising is an indispensable step during image processing. This paper is concerned with dual-tree complex wavelet-based image denoising using Bayesian techniques. One of the cruxes of the Bayesian image denoising algorithms is to estimate the statistical parameter of the image. Here, we employ maximum a posteriori (MAP) estimation to calculate local observed variance with generalized Gamma density prior for local observed variance and Laplacian or Gaussian distribution for noisy wavelet coefficients. Evidently, our selection of prior distribution is motivated by efficient and flexible properties of generalized Gamma density. The experimental results show that the proposed method yields good denoising results.