A generalized form of the Bernoulli Trial collision scheme in DSMC: Derivation and evaluation

Science.gov (United States)

Roohi, Ehsan; Stefanov, Stefan; Shoja-Sani, Ahmad; Ejraei, Hossein

2018-02-01

The impetus of this research is to present a generalized Bernoulli Trial collision scheme in the context of the direct simulation Monte Carlo (DSMC) method. Previously, a subsequent of several collision schemes have been put forward, which were mathematically based on the Kac stochastic model. These include Bernoulli Trial (BT), Ballot Box (BB), Simplified Bernoulli Trial (SBT) and Intelligent Simplified Bernoulli Trial (ISBT) schemes. The number of considered pairs for a possible collision in the above-mentioned schemes varies between N (l) (N (l) - 1) / 2 in BT, 1 in BB, and (N (l) - 1) in SBT or ISBT, where N (l) is the instantaneous number of particles in the lth cell. Here, we derive a generalized form of the Bernoulli Trial collision scheme (GBT) where the number of selected pairs is any desired value smaller than (N (l) - 1), i.e., Nsel < (N (l) - 1), keeping the same the collision frequency and accuracy of the solution as the original SBT and BT models. We derive two distinct formulas for the GBT scheme, where both formula recover BB and SBT limits if Nsel is set as 1 and N (l) - 1, respectively, and provide accurate solutions for a wide set of test cases. The present generalization further improves the computational efficiency of the BT-based collision models compared to the standard no time counter (NTC) and nearest neighbor (NN) collision models.

Fixed-Point Algorithms for the Blind Separation of Arbitrary Complex-Valued Non-Gaussian Signal Mixtures

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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.

Bernoulli-Carlitz and Cauchy-Carlitz numbers with Stirling-Carlitz numbers

OpenAIRE

Kaneko, Hajime; Komatsu, Takao

2017-01-01

Recently, the Cauchy-Carlitz number was defined as the counterpart of the Bernoulli-Carlitz number. Both numbers can be expressed explicitly in terms of so-called Stirling-Carlitz numbers. In this paper, we study the second analogue of Stirling-Carlitz numbers and give some general formulae, including Bernoulli and Cauchy numbers in formal power series with complex coefficients, and Bernoulli-Carlitz and Cauchy-Carlitz numbers in function fields. We also give some applications of Hasse-Teichm...

Theoretical and Computational Analyses of Bernoulli Levitation Flows

International Nuclear Information System (INIS)

Nam, Jong Soon; Kim, Gyu Wan; Kim, Jin Hyeon; Kim, Heuy Dong

2013-01-01

Pneumatic levitation is based upon Bernoulli's principle. However, this method is known to require a large gas flow rate that can lead to an increase in the cost of products. In this case, the gas flow rate should be increased, and the compressible effects of the gas may be of practical importance. In the present study, a computational fluid dynamics method has been used to obtain insights into Bernoulli levitation flows. Three-dimensional compressible Navier-Stokes equations in combination with the SST k-ω turbulence model were solved using a fully implicit finite volume scheme. The gas flow rate, work piece diameter,and clearance gap between the work piece and the circular cylinder were varied to investigate the flow characteristics inside. It is known that there is an optimal clearance gap for the lifting force and that increasing the supply gas flow rate results in a larger lifting force

Theoretical and Computational Analyses of Bernoulli Levitation Flows

Energy Technology Data Exchange (ETDEWEB)

Nam, Jong Soon; Kim, Gyu Wan; Kim, Jin Hyeon; Kim, Heuy Dong [Andong Nat' l Univ., Andong (Korea, Republic of)

2013-07-15

Pneumatic levitation is based upon Bernoulli's principle. However, this method is known to require a large gas flow rate that can lead to an increase in the cost of products. In this case, the gas flow rate should be increased, and the compressible effects of the gas may be of practical importance. In the present study, a computational fluid dynamics method has been used to obtain insights into Bernoulli levitation flows. Three-dimensional compressible Navier-Stokes equations in combination with the SST k-{omega} turbulence model were solved using a fully implicit finite volume scheme. The gas flow rate, work piece diameter,and clearance gap between the work piece and the circular cylinder were varied to investigate the flow characteristics inside. It is known that there is an optimal clearance gap for the lifting force and that increasing the supply gas flow rate results in a larger lifting force.

Whole analogy between Daniel Bernoulli solution and direct kinematics solution

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Filipović Mirjana

2010-01-01

Full Text Available In this paper, the relationship between the original Euler-Bernoulli's rod equation and contemporary knowledge is established. The solution which Daniel Bernoulli defined for the simplest conditions is essentially the solution of 'direct kinematics'. For this reason, special attention is devoted to dynamics and kinematics of elastic mechanisms configuration. The Euler-Bernoulli equation and its solution (used in literature for a long time should be expanded according to the requirements of the mechanisms motion complexity. The elastic deformation is a dynamic value that depends on the total mechanism movements dynamics. Mathematical model of the actuators comprises also elasticity forces.

Dynamic behaviour of non-uniform Bernoulli-Euler beams subjected ...

African Journals Online (AJOL)

This paper investigates the dynamics behaviour of non-uniform Bernoulli-Euler beams subjected to concentrated loads ravelling at variable velocities. The solution technique is based on the Generalized Galerkin Method and the use of the generating function of the Bessel function type. The results show that, for all the ...

A new class of generalized polynomials associated with Hermite and Bernoulli polynomials

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M. A. Pathan

2015-05-01

Full Text Available In this paper, we introduce a new class of generalized  polynomials associated with  the modified Milne-Thomson's polynomials Φ_{n}^{(α}(x,ν of degree n and order α introduced by  Derre and Simsek.The concepts of Bernoulli numbers B_n, Bernoulli polynomials  B_n(x, generalized Bernoulli numbers B_n(a,b, generalized Bernoulli polynomials  B_n(x;a,b,c of Luo et al, Hermite-Bernoulli polynomials  {_HB}_n(x,y of Dattoli et al and {_HB}_n^{(α} (x,y of Pathan  are generalized to the one   {_HB}_n^{(α}(x,y,a,b,c which is called  the generalized  polynomial depending on three positive real parameters. Numerous properties of these polynomials and some relationships between B_n, B_n(x, B_n(a,b, B_n(x;a,b,c and {}_HB_n^{(α}(x,y;a,b,c  are established. Some implicit summation formulae and general symmetry identities are derived by using different analytical means and applying generating functions. These results extend some known summations and identities of generalized Bernoulli numbers and polynomials

A HOS-based blind deconvolution algorithm for the improvement of time resolution of mixed phase low SNR seismic data

International Nuclear Information System (INIS)

Hani, Ahmad Fadzil M; Younis, M Shahzad; Halim, M Firdaus M

2009-01-01

A blind deconvolution technique using a modified higher order statistics (HOS)-based eigenvector algorithm (EVA) is presented in this paper. The main purpose of the technique is to enable the processing of low SNR short length seismograms. In our study, the seismogram is assumed to be the output of a mixed phase source wavelet (system) driven by a non-Gaussian input signal (due to earth) with additive Gaussian noise. Techniques based on second-order statistics are shown to fail when processing non-minimum phase seismic signals because they only rely on the autocorrelation function of the observed signal. In contrast, existing HOS-based blind deconvolution techniques are suitable in the processing of a non-minimum (mixed) phase system; however, most of them are unable to converge and show poor performance whenever noise dominates the actual signal, especially in the cases where the observed data are limited (few samples). The developed blind equalization technique is primarily based on the EVA for blind equalization, initially to deal with mixed phase non-Gaussian seismic signals. In order to deal with the dominant noise issue and small number of available samples, certain modifications are incorporated into the EVA. For determining the deconvolution filter, one of the modifications is to use more than one higher order cumulant slice in the EVA. This overcomes the possibility of non-convergence due to a low signal-to-noise ratio (SNR) of the observed signal. The other modification conditions the cumulant slice by increasing the power of eigenvalues of the cumulant slice, related to actual signal, and rejects the eigenvalues below the threshold representing the noise. This modification reduces the effect of the availability of a small number of samples and strong additive noise on the cumulant slices. These modifications are found to improve the overall deconvolution performance, with approximately a five-fold reduction in a mean square error (MSE) and a six

Stabilization analysis of Euler-Bernoulli beam equation with locally distributed disturbance

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Pengcheng HAN

2017-12-01

Full Text Available In order to enrich the system stability theory of the control theories, taking Euler-Bernoulli beam equation as the research subject, the stability of Euler-Bernoulli beam equation with locally distributed disturbance is studied. A feedback controller based on output is designed to reduce the effects of the disturbances. The well-posedness of the nonlinear closed-loop system is investigated by the theory of maximal monotone operator, namely the existence and uniqueness of solutions for the closed-loop system. An appropriate state space is established, an appropriate inner product is defined, and a non-linear operator satisfying this state space is defined. Then, the system is transformed into the form of evolution equation. Based on this, the existence and uniqueness of solutions for the closed-loop system are proved. The asymptotic stability of the system is studied by constructing an appropriate Lyapunov function, which proves the asymptotic stability of the closed-loop system. The result shows that designing proper anti-interference controller is the foundation of investigating the system stability, and the research of the stability of Euler-bernoulli beam equation with locally distributed disturbance can prove the asymptotic stability of the system. This method can be extended to study the other equations such as wave equation, Timoshenko beam equation, Schrodinger equation, etc.

Spacings and pair correlations for finite Bernoulli convolutions

International Nuclear Information System (INIS)

Benjamini, Itai; Solomyak, Boris

2009-01-01

We consider finite Bernoulli convolutions with a parameter 1/2 N . These sequences are uniformly distributed with respect to the infinite Bernoulli convolution measure ν λ , as N → ∞. Numerical evidence suggests that for a generic λ, the distribution of spacings between appropriately rescaled points is Poissonian. We obtain some partial results in this direction; for instance, we show that, on average, the pair correlations do not exhibit attraction or repulsion in the limit. On the other hand, for certain algebraic λ the behaviour is totally different

Generalization of the Bernoulli ODE

Science.gov (United States)

Azevedo, Douglas; Valentino, Michele C.

2017-01-01

In this note, we propose a generalization of the famous Bernoulli differential equation by introducing a class of nonlinear first-order ordinary differential equations (ODEs). We provide a family of solutions for this introduced class of ODEs and also we present some examples in order to illustrate the applications of our result.

Testing Bernoulli's Law

Science.gov (United States)

Ivanov, Dragia; Nikolov, Stefan; Petrova, Hristina

2014-01-01

In this paper we present three different methods for testing Bernoulli's law that are different from the standard "tube with varying cross-section." They are all applicable to high-school level physics education, with varying levels of theoretical and experimental complexity, depending on students' skills, and may even be…

Bernoulli and Euler Numbers

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Dae San Kim

2012-01-01

Full Text Available We derive some interesting identities and arithmetic properties of Bernoulli and Euler polynomials from the orthogonality of Hermite polynomials. Let Pn={p(x∈ℚ[x]∣deg p(x≤n} be the (n+1-dimensional vector space over ℚ. Then we show that {H0(x,H1(x,…,Hn(x} is a good basis for the space Pn for our purpose of arithmetical and combinatorial applications.

Blind signal processing algorithms under DC biased Gaussian noise

Science.gov (United States)

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.

Quantum Walk in Terms of Quantum Bernoulli Noise and Quantum Central Limit Theorem for Quantum Bernoulli Noise

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Caishi Wang

2018-01-01

Full Text Available As a unitary quantum walk with infinitely many internal degrees of freedom, the quantum walk in terms of quantum Bernoulli noise (recently introduced by Wang and Ye shows a rather classical asymptotic behavior, which is quite different from the case of the usual quantum walks with a finite number of internal degrees of freedom. In this paper, we further examine the structure of the walk. By using the Fourier transform on the state space of the walk, we obtain a formula that links the moments of the walk’s probability distributions directly with annihilation and creation operators on Bernoulli functionals. We also prove some other results on the structure of the walk. Finally, as an application of these results, we establish a quantum central limit theorem for the annihilation and creation operators themselves.

Analysis of piezoelectric energy harvester under modulated and filtered white Gaussian noise

Science.gov (United States)

Quaranta, Giuseppe; Trentadue, Francesco; Maruccio, Claudio; Marano, Giuseppe C.

2018-05-01

This paper proposes a comprehensive method for the electromechanical probabilistic analysis of piezoelectric energy harvesters subjected to modulated and filtered white Gaussian noise (WGN) at the base. Specifically, the dynamic excitation is simulated by means of an amplitude-modulated WGN, which is filtered through the Clough-Penzien filter. The considered piezoelectric harvester is a cantilever bimorph modeled as Euler-Bernoulli beam with a concentrated mass at the free-end, and its global behavior is approximated by the fundamental vibration mode (which is tuned with the dominant frequency of the dynamic input). A resistive electrical load is considered in the circuit. Once the Lyapunov equation of the coupled electromechanical problem has been formulated, an original and efficient semi-analytical procedure is proposed to estimate mean and standard deviation of the electrical energy extracted from the piezoelectric layers.

Geometrical study of phyllotactic patterns by Bernoulli spiral lattices.

Science.gov (United States)

Sushida, Takamichi; Yamagishi, Yoshikazu

2017-06-01

Geometrical studies of phyllotactic patterns deal with the centric or cylindrical models produced by ideal lattices. van Iterson (Mathematische und mikroskopisch - anatomische Studien über Blattstellungen nebst Betrachtungen über den Schalenbau der Miliolinen, Verlag von Gustav Fischer, Jena, 1907) suggested a centric model representing ideal phyllotactic patterns as disk packings of Bernoulli spiral lattices and presented a phase diagram now called Van Iterson's diagram explaining the bifurcation processes of their combinatorial structures. Geometrical properties on disk packings were shown by Rothen & Koch (J. Phys France, 50(13), 1603-1621, 1989). In contrast, as another centric model, we organized a mathematical framework of Voronoi tilings of Bernoulli spiral lattices and showed mathematically that the phase diagram of a Voronoi tiling is graph-theoretically dual to Van Iterson's diagram. This paper gives a review of two centric models for disk packings and Voronoi tilings of Bernoulli spiral lattices. © 2017 Japanese Society of Developmental Biologists.

A family of isospectral Euler–Bernoulli beams

International Nuclear Information System (INIS)

Gladwell, Graham M L; Morassi, Antonino

2010-01-01

In this paper we consider the class of Euler–Bernoulli beams such that the product between the bending stiffness and the linear mass density is constant. Under the assumption that the end conditions are any combination of pinned and sliding, we obtain closed-form expressions for beams isospectral to a given one. The analysis is based on the fact that this special class of beams is, in a certain sense, equivalent to a string, and uses a Darboux lemma after reduction of the string equation to Sturm–Liouville canonical form

Generation of Quasi-Gaussian Pulses Based on Correlation Techniques

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POHOATA, S.

2012-02-01

Full Text Available The Gaussian pulses have been mostly used within communications, where some applications can be emphasized: mobile telephony (GSM, where GMSK signals are used, as well as the UWB communications, where short-period pulses based on Gaussian waveform are generated. Since the Gaussian function signifies a theoretical concept, which cannot be accomplished from the physical point of view, this should be expressed by using various functions, able to determine physical implementations. New techniques of generating the Gaussian pulse responses of good precision are approached, proposed and researched in this paper. The second and third order derivatives with regard to the Gaussian pulse response are accurately generated. The third order derivates is composed of four individual rectangular pulses of fixed amplitudes, being easily to be generated by standard techniques. In order to generate pulses able to satisfy the spectral mask requirements, an adequate filter is necessary to be applied. This paper emphasizes a comparative analysis based on the relative error and the energy spectra of the proposed pulses.

Evaluation of aerodynamic characteristics of a coupled fluid-structure system using generalized Bernoulli's principle: An application to vocal folds vibration.

Science.gov (United States)

Zhang, Lucy T; Yang, Jubiao

2016-12-01

In this work we explore the aerodynamics flow characteristics of a coupled fluid-structure interaction system using a generalized Bernoulli equation derived directly from the Cauchy momentum equations. Unlike the conventional Bernoulli equation where incompressible, inviscid, and steady flow conditions are assumed, this generalized Bernoulli equation includes the contributions from compressibility, viscous, and unsteadiness, which could be essential in defining aerodynamic characteristics. The application of the derived Bernoulli's principle is on a fully-coupled fluid-structure interaction simulation of the vocal folds vibration. The coupled system is simulated using the immersed finite element method where compressible Navier-Stokes equations are used to describe the air and an elastic pliable structure to describe the vocal fold. The vibration of the vocal fold works to open and close the glottal flow. The aerodynamics flow characteristics are evaluated using the derived Bernoulli's principles for a vibration cycle in a carefully partitioned control volume based on the moving structure. The results agree very well to experimental observations, which validate the strategy and its use in other types of flow characteristics that involve coupled fluid-structure interactions.

Bernoulli Suction Effect on Soap Bubble Blowing?

Science.gov (United States)

Davidson, John; Ryu, Sangjin

2015-11-01

As a model system for thin-film bubble with two gas-liquid interfaces, we experimentally investigated the pinch-off of soap bubble blowing. Using the lab-built bubble blower and high-speed videography, we have found that the scaling law exponent of soap bubble pinch-off is 2/3, which is similar to that of soap film bridge. Because air flowed through the decreasing neck of soap film tube, we studied possible Bernoulli suction effect on soap bubble pinch-off by evaluating the Reynolds number of airflow. Image processing was utilized to calculate approximate volume of growing soap film tube and the volume flow rate of the airflow, and the Reynolds number was estimated to be 800-3200. This result suggests that soap bubbling may involve the Bernoulli suction effect.

Hydraulic jump and Bernoulli equation in nonlinear shallow water model

Science.gov (United States)

Sun, Wen-Yih

2018-06-01

A shallow water model was applied to study the hydraulic jump and Bernoulli equation across the jump. On a flat terrain, when a supercritical flow plunges into a subcritical flow, discontinuity develops on velocity and Bernoulli function across the jump. The shock generated by the obstacle may propagate downstream and upstream. The latter reflected from the inflow boundary, moves downstream and leaves the domain. Before the reflected wave reaching the obstacle, the short-term integration (i.e., quasi-steady) simulations agree with Houghton and Kasahara's results, which may have unphysical complex solutions. The quasi-steady flow is quickly disturbed by the reflected wave, finally, flow reaches steady and becomes critical without complex solutions. The results also indicate that Bernoulli function is discontinuous but the potential of mass flux remains constant across the jump. The latter can be used to predict velocity/height in a steady flow.

Ultrawide Bandwidth Receiver Based on a Multivariate Generalized Gaussian Distribution

KAUST Repository

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.

A wavelet-based Gaussian method for energy dispersive X-ray fluorescence spectrum

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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.

Who Solved the Bernoulli Differential Equation and How Did They Do It?

Science.gov (United States)

Parker, Adam E.

2013-01-01

The Bernoulli brothers, Jacob and Johann, and Leibniz: Any of these might have been first to solve what is called the Bernoulli differential equation. We explore their ideas and the chronology of their work, finding out, among other things, that variation of parameters was used in 1697, 78 years before 1775, when Lagrange introduced it in general.

L’Hôpital's Analyse des infiniments petits an annotated translation with source material by Johann Bernoulli

CERN Document Server

Bradley, Robert E; Sandifer, C Edward

2015-01-01

This monograph is an annotated translation of what is considered to be the world’s first calculus textbook, originally published in French in 1696. That anonymously published textbook on differential calculus was based on lectures given to the Marquis de l’Hôpital in 1691-2 by the great Swiss mathematician, Johann Bernoulli. In the 1920s, a copy of Bernoulli’s lecture notes was discovered in a library in Basel, which presented the opportunity to compare Bernoulli’s notes, in Latin, to l’Hôpital’s text in French. The similarities are remarkable, but there is also much in l’Hôpital’s book that is original and innovative. This book offers the first English translation of Bernoulli's notes, along with the first faithful English translation of l’Hôpital’s text, complete with annotations and commentary. Additionally, a significant portion of the correspondence between l’Hôpital and Bernoulli has been included, also for the fi rst time in English translation. This translation will provide ...

Variational Bayesian labeled multi-Bernoulli filter with unknown sensor noise statistics

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Qiu Hao

2016-10-01

Full Text Available It is difficult to build accurate model for measurement noise covariance in complex backgrounds. For the scenarios of unknown sensor noise variances, an adaptive multi-target tracking algorithm based on labeled random finite set and variational Bayesian (VB approximation is proposed. The variational approximation technique is introduced to the labeled multi-Bernoulli (LMB filter to jointly estimate the states of targets and sensor noise variances. Simulation results show that the proposed method can give unbiased estimation of cardinality and has better performance than the VB probability hypothesis density (VB-PHD filter and the VB cardinality balanced multi-target multi-Bernoulli (VB-CBMeMBer filter in harsh situations. The simulations also confirm the robustness of the proposed method against the time-varying noise variances. The computational complexity of proposed method is higher than the VB-PHD and VB-CBMeMBer in extreme cases, while the mean execution times of the three methods are close when targets are well separated.

Flawed Applications of Bernoulli's Principle

Science.gov (United States)

Koumaras, Panagiotis; Primerakis, Georgios

2018-04-01

One of the most popular demonstration experiments pertaining to Bernoulli's principle is the production of a water spray by using a vertical plastic straw immersed in a glass of water and a horizontal straw to blow air towards the top edge of the vertical one. A more general version of this phenomenon, appearing also in school physics problems, is the determination of the rise of the water level h in the straw (see Fig. 1).

A Gaussian mixture copula model based localized Gaussian process regression approach for long-term wind speed prediction

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

Bernoulli Polynomials, Fourier Series and Zeta Numbers

DEFF Research Database (Denmark)

Scheufens, Ernst E

2013-01-01

Fourier series for Bernoulli polynomials are used to obtain information about values of the Riemann zeta function for integer arguments greater than one. If the argument is even we recover the well-known exact values, if the argument is odd we find integral representations and rapidly convergent...

Heuristic analogy in Ars Conjectandi: From Archimedes' De Circuli Dimensione to Bernoulli's theorem.

Science.gov (United States)

Campos, Daniel G

2018-02-01

This article investigates the way in which Jacob Bernoulli proved the main mathematical theorem that undergirds his art of conjecturing-the theorem that founded, historically, the field of mathematical probability. It aims to contribute a perspective into the question of problem-solving methods in mathematics while also contributing to the comprehension of the historical development of mathematical probability. It argues that Bernoulli proved his theorem by a process of mathematical experimentation in which the central heuristic strategy was analogy. In this context, the analogy functioned as an experimental hypothesis. The article expounds, first, Bernoulli's reasoning for proving his theorem, describing it as a process of experimentation in which hypothesis-making is crucial. Next, it investigates the analogy between his reasoning and Archimedes' approximation of the value of π, by clarifying both Archimedes' own experimental approach to the said approximation and its heuristic influence on Bernoulli's problem-solving strategy. The discussion includes some general considerations about analogy as a heuristic technique to make experimental hypotheses in mathematics. Copyright © 2018 Elsevier Ltd. All rights reserved.

Vibrations of an Euler-Bernoulli beam with hysteretic damping arising from dispersed frictional microcracks

Science.gov (United States)

Maiti, Soumyabrata; Bandyopadhyay, Ritwik; Chatterjee, Anindya

2018-01-01

We study free and harmonically forced vibrations of an Euler-Bernoulli beam with rate-independent hysteretic dissipation. The dissipation follows a model proposed elsewhere for materials with randomly dispersed frictional microcracks. The virtual work of distributed dissipative moments is approximated using Gaussian quadrature, yielding a few discrete internal hysteretic states. Lagrange's equations are obtained for the modal coordinates. Differential equations for the modal coordinates and internal states are integrated together. Free vibrations decay exponentially when a single mode dominates. With multiple modes active, higher modes initially decay rapidly while lower modes decay relatively slowly. Subsequently, lower modes show their own characteristic modal damping, while small amplitude higher modes show more erratic decay. Large dissipation, for the adopted model, leads mathematically to fast and damped oscillations in the limit, unlike viscously overdamped systems. Next, harmonically forced, lightly damped responses of the beam are studied using both a slow frequency sweep and a shooting-method based search for periodic solutions along with numerical continuation. Shooting method and frequency sweep results match for large ranges of frequency. The shooting method struggles near resonances, where internal states collapse into lower dimensional behavior and Newton-Raphson iterations fail. Near the primary resonances, simple numerically-aided harmonic balance gives excellent results. Insights are also obtained into the harmonic content of secondary resonances.

A Ritz approach for the static analysis of planar pantographic structures modeled with nonlinear Euler-Bernoulli beams

Science.gov (United States)

Andreaus, Ugo; Spagnuolo, Mario; Lekszycki, Tomasz; Eugster, Simon R.

2018-04-01

We present a finite element discrete model for pantographic lattices, based on a continuous Euler-Bernoulli beam for modeling the fibers composing the pantographic sheet. This model takes into account large displacements, rotations and deformations; the Euler-Bernoulli beam is described by using nonlinear interpolation functions, a Green-Lagrange strain for elongation and a curvature depending on elongation. On the basis of the introduced discrete model of a pantographic lattice, we perform some numerical simulations. We then compare the obtained results to an experimental BIAS extension test on a pantograph printed with polyamide PA2200. The pantographic structures involved in the numerical as well as in the experimental investigations are not proper fabrics: They are composed by just a few fibers for theoretically allowing the use of the Euler-Bernoulli beam theory in the description of the fibers. We compare the experiments to numerical simulations in which we allow the fibers to elastically slide one with respect to the other in correspondence of the interconnecting pivot. We present as result a very good agreement between the numerical simulation, based on the introduced model, and the experimental measures.

Natural frequencies of Euler-Bernoulli beam with open cracks on elastic foundations

International Nuclear Information System (INIS)

Shin, Young Jae; Yun, Jong Hak; Seong, Kyeong Youn; Kim, Jae Ho; Kang, Sung Hwang

2006-01-01

A study of the natural vibrations of beam resting on elastic foundation with finite number of transverse open cracks is presented. Frequency equations are derived for beams with different end restraints. Euler-Bernoulli beam on Winkler foundation and Euler-Bernoulli beam on Paster nak foundation are investigated. The cracks are modeled by massless substitute spring. The effects of the crack location, size and its number and the foundation constants, on the natural frequencies of the beam, are investigated

A Novel Bearing Fault Diagnosis Method Based on Gaussian Restricted Boltzmann Machine

Directory of Open Access Journals (Sweden)

Xiao-hui He

2016-01-01

Full Text Available To realize the fault diagnosis of bearing effectively, this paper presents a novel bearing fault diagnosis method based on Gaussian restricted Boltzmann machine (Gaussian RBM. Vibration signals are firstly resampled to the same equivalent speed. Subsequently, the envelope spectrums of the resampled data are used directly as the feature vectors to represent the fault types of bearing. Finally, in order to deal with the high-dimensional feature vectors based on envelope spectrum, a classifier model based on Gaussian RBM is applied. Gaussian RBM has the ability to provide a closed-form representation of the distribution underlying the training data, and it is very convenient for modeling high-dimensional real-valued data. Experiments on 10 different data sets verify the performance of the proposed method. The superiority of Gaussian RBM classifier is also confirmed by comparing with other classifiers, such as extreme learning machine, support vector machine, and deep belief network. The robustness of the proposed method is also studied in this paper. It can be concluded that the proposed method can realize the bearing fault diagnosis accurately and effectively.

Flawed Applications of Bernoulli's Principle

Science.gov (United States)

Koumaras, Panagiotis; Primerakis, Georgios

2018-01-01

One of the most popular demonstration experiments pertaining to Bernoulli's principle is the production of a water spray by using a vertical plastic straw immersed in a glass of water and a horizontal straw to blow air towards the top edge of the vertical one. A more general version of this phenomenon, appearing also in school physics problems, is…

Bernoulli numbers and polynomials from a more general point of view

Energy Technology Data Exchange (ETDEWEB)

Dattoli, G. [ENEA, Centro Ricerche Frascati, Frascati, RM(Italy). Div. Fisica Applicata; Cesarano, C. [Ulm Univ., Ulm (Germany). Dept. of Mathematics; Lonzellutta, S. [ENEA, Centro Ricerche E. Clementel, Bologna (Italy). Div. Fisica Applicata

2000-07-01

In this work it is applied the method of generating function, to introduce new forms of Bernoulli numbers and polynomials, which are exploited to derive further classes of partial sums involving generalized many index many variable polynomials. Analogous considerations are developed for the Euler numbers and polynomials. [Italian] Si applica il metodo della funzione generatrice per introdurre nuove forme di numeri e polinomi di Bernoulli che vengono utilizzati per sviluppare e per calcolare somme parziali che coinvolgono polinomi a piu' indici ed a piu' variabili. Si sviluppano considerazioni analoghe per i polinomi ed i numeri di Eulero.

Epileptic Seizure Detection with Log-Euclidean Gaussian Kernel-Based Sparse Representation.

Science.gov (United States)

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.

A biclustering algorithm for binary matrices based on penalized Bernoulli likelihood

KAUST Repository

Lee, Seokho

2013-01-31

We propose a new biclustering method for binary data matrices using the maximum penalized Bernoulli likelihood estimation. Our method applies a multi-layer model defined on the logits of the success probabilities, where each layer represents a simple bicluster structure and the combination of multiple layers is able to reveal complicated, multiple biclusters. The method allows for non-pure biclusters, and can simultaneously identify the 1-prevalent blocks and 0-prevalent blocks. A computationally efficient algorithm is developed and guidelines are provided for specifying the tuning parameters, including initial values of model parameters, the number of layers, and the penalty parameters. Missing-data imputation can be handled in the EM framework. The method is tested using synthetic and real datasets and shows good performance. © 2013 Springer Science+Business Media New York.

A nonlinear free boundary problem with a self-driven Bernoulli condition

OpenAIRE

Dipierro, Serena; Karakhanyan, Aram; Valdinoci, Enrico

2017-01-01

We study a Bernoulli type free boundary problem with two phases J[u]=∫Ω|∇u(x)|2dx+Φ(M−(u),M+(u)),u−u¯∈W1,20(Ω), where u¯∈W1,2(Ω) is a given boundary datum. Here, M1 and M2 are weighted volumes of {u≤0}∩Ω and {u>0}∩Ω, respectively, and Φ is a nonnegative function of two real variables. We show that, for this problem, the Bernoulli constant, which determines the gradient jump condition across the free boundary, is of global type and it is indeed determined by the weighted volumes of the phas...

James Bernoulli与《推测术》%James Bernoulli and Stochastics

Institute of Scientific and Technical Information of China (English)

于忠义

2003-01-01

Two hundred and eighty-nine years ago, "Arts of conjecturing", the posthumous work of the great mathematician James Bernoulli, was finally produced in the eager wait of mathematicians, Ian Hacking, a famous historian of statistics, commented that the publication of the book signaled the beginning of the mathematical theory of probability and terminated man's long process in the forming of the probability concept By introducing James Bernoulli and his "Arts of conjecturing", this paper aims at providing the reader with the 18th century story of Bemoulli's work, and at making the reader know that even today Bemoulli's idea is very helpful and instructive in the research of modern statistics.

Receiver design for SPAD-based VLC systems under Poisson-Gaussian mixed noise model.

Science.gov (United States)

Mao, Tianqi; Wang, Zhaocheng; Wang, Qi

2017-01-23

Single-photon avalanche diode (SPAD) is a promising photosensor because of its high sensitivity to optical signals in weak illuminance environment. Recently, it has drawn much attention from researchers in visible light communications (VLC). However, existing literature only deals with the simplified channel model, which only considers the effects of Poisson noise introduced by SPAD, but neglects other noise sources. Specifically, when an analog SPAD detector is applied, there exists Gaussian thermal noise generated by the transimpedance amplifier (TIA) and the digital-to-analog converter (D/A). Therefore, in this paper, we propose an SPAD-based VLC system with pulse-amplitude-modulation (PAM) under Poisson-Gaussian mixed noise model, where Gaussian-distributed thermal noise at the receiver is also investigated. The closed-form conditional likelihood of received signals is derived using the Laplace transform and the saddle-point approximation method, and the corresponding quasi-maximum-likelihood (quasi-ML) detector is proposed. Furthermore, the Poisson-Gaussian-distributed signals are converted to Gaussian variables with the aid of the generalized Anscombe transform (GAT), leading to an equivalent additive white Gaussian noise (AWGN) channel, and a hard-decision-based detector is invoked. Simulation results demonstrate that, the proposed GAT-based detector can reduce the computational complexity with marginal performance loss compared with the proposed quasi-ML detector, and both detectors are capable of accurately demodulating the SPAD-based PAM signals.

Group colorings and Bernoulli subflows

CERN Document Server

Gao, Su; Seward, Brandon

2016-01-01

In this paper the authors study the dynamics of Bernoulli flows and their subflows over general countable groups. One of the main themes of this paper is to establish the correspondence between the topological and the symbolic perspectives. From the topological perspective, the authors are particularly interested in free subflows (subflows in which every point has trivial stabilizer), minimal subflows, disjointness of subflows, and the problem of classifying subflows up to topological conjugacy. Their main tool to study free subflows will be the notion of hyper aperiodic points; a point is hyper aperiodic if the closure of its orbit is a free subflow.

Discrete-time retrial queue with Bernoulli vacation, preemptive resume and feedback customers

Directory of Open Access Journals (Sweden)

Peishu Chen

2015-09-01

Full Text Available Purpose: We consider a discrete-time Geo/G/1 retrial queue where the retrial time follows a general distribution, the server subject to Bernoulli vacation policy and the customer has preemptive resume priority, Bernoulli feedback strategy. The main purpose of this paper is to derive the generating functions of the stationary distribution of the system state, the orbit size and some important performance measures. Design/methodology: Using probability generating function technique, some valuable and interesting performance measures of the system are obtained. We also investigate two stochastic decomposition laws and present some numerical results. Findings: We obtain the probability generating functions of the system state distribution as well as those of the orbit size and the system size distributions. We also obtain some analytical expressions for various performance measures such as idle and busy probabilities, mean orbit and system sizes. Originality/value: The analysis of discrete-time retrial queues with Bernoulli vacation, preemptive resume and feedback customers is interesting and to the best of our knowledge, no other scientific journal paper has dealt with this question. This fact gives the reason why efforts should be taken to plug this gap.

A study on the use of Gumbel approximation with the Bernoulli spatial scan statistic.

Science.gov (United States)

Read, S; Bath, P A; Willett, P; Maheswaran, R

2013-08-30

The Bernoulli version of the spatial scan statistic is a well established method of detecting localised spatial clusters in binary labelled point data, a typical application being the epidemiological case-control study. A recent study suggests the inferential accuracy of several versions of the spatial scan statistic (principally the Poisson version) can be improved, at little computational cost, by using the Gumbel distribution, a method now available in SaTScan(TM) (www.satscan.org). We study in detail the effect of this technique when applied to the Bernoulli version and demonstrate that it is highly effective, albeit with some increase in false alarm rates at certain significance thresholds. We explain how this increase is due to the discrete nature of the Bernoulli spatial scan statistic and demonstrate that it can affect even small p-values. Despite this, we argue that the Gumbel method is actually preferable for very small p-values. Furthermore, we extend previous research by running benchmark trials on 12 000 synthetic datasets, thus demonstrating that the overall detection capability of the Bernoulli version (i.e. ratio of power to false alarm rate) is not noticeably affected by the use of the Gumbel method. We also provide an example application of the Gumbel method using data on hospital admissions for chronic obstructive pulmonary disease. Copyright © 2013 John Wiley & Sons, Ltd.

Improved implementation of the risk-adjusted Bernoulli CUSUM chart to monitor surgical outcome quality.

Science.gov (United States)

Keefe, Matthew J; Loda, Justin B; Elhabashy, Ahmad E; Woodall, William H

2017-06-01

The traditional implementation of the risk-adjusted Bernoulli cumulative sum (CUSUM) chart for monitoring surgical outcome quality requires waiting a pre-specified period of time after surgery before incorporating patient outcome information. We propose a simple but powerful implementation of the risk-adjusted Bernoulli CUSUM chart that incorporates outcome information as soon as it is available, rather than waiting a pre-specified period of time after surgery. A simulation study is presented that compares the performance of the traditional implementation of the risk-adjusted Bernoulli CUSUM chart to our improved implementation. We show that incorporating patient outcome information as soon as it is available leads to quicker detection of process deterioration. Deterioration of surgical performance could be detected much sooner using our proposed implementation, which could lead to the earlier identification of problems. © The Author 2017. Published by Oxford University Press in association with the International Society for Quality in Health Care. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

Gaussian likelihood inference on data from trans-Gaussian random fields with Matérn covariance function

KAUST Repository

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.

Gaussian likelihood inference on data from trans-Gaussian random fields with Matérn covariance function

KAUST Repository

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.

Kinect Posture Reconstruction Based on a Local Mixture of Gaussian Process Models.

Science.gov (United States)

Liu, Zhiguang; Zhou, Liuyang; Leung, Howard; Shum, Hubert P H

2016-11-01

Depth sensor based 3D human motion estimation hardware such as Kinect has made interactive applications more popular recently. However, it is still challenging to accurately recognize postures from a single depth camera due to the inherently noisy data derived from depth images and self-occluding action performed by the user. In this paper, we propose a new real-time probabilistic framework to enhance the accuracy of live captured postures that belong to one of the action classes in the database. We adopt the Gaussian Process model as a prior to leverage the position data obtained from Kinect and marker-based motion capture system. We also incorporate a temporal consistency term into the optimization framework to constrain the velocity variations between successive frames. To ensure that the reconstructed posture resembles the accurate parts of the observed posture, we embed a set of joint reliability measurements into the optimization framework. A major drawback of Gaussian Process is its cubic learning complexity when dealing with a large database due to the inverse of a covariance matrix. To solve the problem, we propose a new method based on a local mixture of Gaussian Processes, in which Gaussian Processes are defined in local regions of the state space. Due to the significantly decreased sample size in each local Gaussian Process, the learning time is greatly reduced. At the same time, the prediction speed is enhanced as the weighted mean prediction for a given sample is determined by the nearby local models only. Our system also allows incrementally updating a specific local Gaussian Process in real time, which enhances the likelihood of adapting to run-time postures that are different from those in the database. Experimental results demonstrate that our system can generate high quality postures even under severe self-occlusion situations, which is beneficial for real-time applications such as motion-based gaming and sport training.

The location-, word-, and arrow-based Simon effects: An ex-Gaussian analysis.

Science.gov (United States)

Luo, Chunming; Proctor, Robert W

2018-04-01

Task-irrelevant spatial information, conveyed by stimulus location, location word, or arrow direction, can influence the response to task-relevant attributes, generating the location-, word-, and arrow-based Simon effects. We examined whether different mechanisms are involved in the generation of these Simon effects by fitting a mathematical ex-Gaussian function to empirical response time (RT) distributions. Specifically, we tested whether which ex-Gaussian parameters (μ, σ, and τ) show Simon effects and whether the location-, word, and arrow-based effects are on different parameters. Results show that the location-based Simon effect occurred on mean RT and μ but not on τ, and a reverse Simon effect occurred on σ. In contrast, a positive word-based Simon effect was obtained on all these measures (including σ), and a positive arrow-based Simon effect was evident on mean RT, σ, and τ but not μ. The arrow-based Simon effect was not different from the word-based Simon effect on τ or σ but was on μ and mean RT. These distinct results on mean RT and ex-Gaussian parameters provide evidence that spatial information conveyed by the various location modes are different in the time-course of activation.

Management of colon stents based on Bernoulli's principle.

Science.gov (United States)

Uno, Yoshiharu

2017-03-01

The colonic self-expanding metal stent (SEMS) has been widely used for "bridge to surgery" and palliative therapy. However, if the spread of SEMS is insufficient, not only can a decompression effect not be obtained but also perforation and obstructive colitis can occur. The mechanism of occurrence of obstructive colitis and perforation was investigated by flow dynamics. Bernoulli's principle was applied, assuming that the cause of inflammation and perforation represented the pressure difference in the proximal lumen and stent. The variables considered were proximal lumen diameter, stent lumen diameter, flow rate into the proximal lumen, and fluid density. To model the right colon, the proximal lumen diameter was set at 50 mm. To model the left-side colon, the proximal lumen diameter was set at 30 mm. For both the right colon model and the left-side colon model, the difference in pressure between the proximal lumen and the stent was less than 20 mmHg, when the diameter of the stent lumen was 14 mm or more. Both the right colon model and the left-side colon model were 30 mmHg or more at 200 mL s -1 when the stent lumen was 10 mm or less. Even with an inflow rate of 90-110 mL s -1 , the pressure was 140 mmHg when the stent lumen diameter was 5 mm. In theory, in order to maintain the effectiveness of SEMS, it is necessary to keep the diameter of the stent lumen at 14 mm or more.

Semiparametric Gaussian copula models : Geometry and efficient rank-based estimation

NARCIS (Netherlands)

Segers, J.; van den Akker, R.; Werker, B.J.M.

2014-01-01

We propose, for multivariate Gaussian copula models with unknown margins and structured correlation matrices, a rank-based, semiparametrically efficient estimator for the Euclidean copula parameter. This estimator is defined as a one-step update of a rank-based pilot estimator in the direction of

Gaussian entanglement revisited

Science.gov (United States)

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.

Bernoulli's Principle Applied to Brain Fluids: Intracranial Pressure Does Not Drive Cerebral Perfusion or CSF Flow.

Science.gov (United States)

Schmidt, Eric; Ros, Maxime; Moyse, Emmanuel; Lorthois, Sylvie; Swider, Pascal

2016-01-01

In line with the first law of thermodynamics, Bernoulli's principle states that the total energy in a fluid is the same at all points. We applied Bernoulli's principle to understand the relationship between intracranial pressure (ICP) and intracranial fluids. We analyzed simple fluid physics along a tube to describe the interplay between pressure and velocity. Bernoulli's equation demonstrates that a fluid does not flow along a gradient of pressure or velocity; a fluid flows along a gradient of energy from a high-energy region to a low-energy region. A fluid can even flow against a pressure gradient or a velocity gradient. Pressure and velocity represent part of the total energy. Cerebral blood perfusion is not driven by pressure but by energy: the blood flows from high-energy to lower-energy regions. Hydrocephalus is related to increased cerebrospinal fluid (CSF) resistance (i.e., energy transfer) at various points. Identification of the energy transfer within the CSF circuit is important in understanding and treating CSF-related disorders. Bernoulli's principle is not an abstract concept far from clinical practice. We should be aware that pressure is easy to measure, but it does not induce resumption of fluid flow. Even at the bedside, energy is the key to understanding ICP and fluid dynamics.

Chaotic dynamics of flexible Euler-Bernoulli beams

Energy Technology Data Exchange (ETDEWEB)

Awrejcewicz, J., E-mail: awrejcew@p.lodz.pl [Department of Automation, Biomechanics and Mechatronics, Lodz University of Technology, 1/15 Stefanowski St., 90-924 Lodz, Poland and Department of Vehicles, Warsaw University of Technology, 84 Narbutta St., 02-524 Warsaw (Poland); Krysko, A. V., E-mail: anton.krysko@gmail.com [Department of Applied Mathematics and Systems Analysis, Saratov State Technical University, Politehnicheskaya 77, 410054 Saratov (Russian Federation); Kutepov, I. E., E-mail: iekutepov@gmail.com; Zagniboroda, N. A., E-mail: tssrat@mail.ru; Dobriyan, V., E-mail: Dobriy88@yandex.ru; Krysko, V. A., E-mail: tak@san.ru [Department of Mathematics and Modeling, Saratov State Technical University, Politehnicheskaya 77, 410054 Saratov (Russian Federation)

2013-12-15

Mathematical modeling and analysis of spatio-temporal chaotic dynamics of flexible simple and curved Euler-Bernoulli beams are carried out. The Kármán-type geometric non-linearity is considered. Algorithms reducing partial differential equations which govern the dynamics of studied objects and associated boundary value problems are reduced to the Cauchy problem through both Finite Difference Method with the approximation of O(c{sup 2}) and Finite Element Method. The obtained Cauchy problem is solved via the fourth and sixth-order Runge-Kutta methods. Validity and reliability of the results are rigorously discussed. Analysis of the chaotic dynamics of flexible Euler-Bernoulli beams for a series of boundary conditions is carried out with the help of the qualitative theory of differential equations. We analyze time histories, phase and modal portraits, autocorrelation functions, the Poincaré and pseudo-Poincaré maps, signs of the first four Lyapunov exponents, as well as the compression factor of the phase volume of an attractor. A novel scenario of transition from periodicity to chaos is obtained, and a transition from chaos to hyper-chaos is illustrated. In particular, we study and explain the phenomenon of transition from symmetric to asymmetric vibrations. Vibration-type charts are given regarding two control parameters: amplitude q{sub 0} and frequency ω{sub p} of the uniformly distributed periodic excitation. Furthermore, we detected and illustrated how the so called temporal-space chaos is developed following the transition from regular to chaotic system dynamics.

Resource theory of non-Gaussian operations

Science.gov (United States)

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.

[Work, momentum and fatigue in the work of Daniel Bernoulli: toward the optimization of biological fact].

Science.gov (United States)

Fonteneau, Yannick; Viard, Jérôme

The concept of mechanical work is inherited from the concepts of potentia absoluta and men's work, both implemented in the section IX of Daniel Bernoulli's Hydrodynamica in 1738. Nonetheless, Bernoulli did not confuse these two entities: he defined a link from gender to species between the former, which is general, and the latter, which is organic. In addition, Bernoulli clearly distinguished between vis viva and potentia absoluta (or work). Their reciprocal conversions are rarely mentioned explicitly in this book, except once, in the section X of his work, from vis viva to work, and subordinated to the mediation of a machine, in a driving forces substitution problem. His attitude evolved significantly in a text in 1753, in which work and vis viva were unambiguously connected, while the concept of potentia absoluta was reduced to that of human work, and the expression itself was abandoned. It was then accepted that work can be converted into vis viva, but the opposite is true in only one case, the intra-organic one. It is the concept of fatigue, seen as an expenditure of animal spirits themselves conceived of as little tensed springs releasing vis viva, that allowed the conversion, never quantified and listed simply as a model, from vis viva to work. Thus, work may have ultimately appeared as a transitional state between two kinds of vis viva, of which the first is non-quantifiable. At the same time, the natural elements were discredited from any hint of profitable production. Only men and animals were able to work in the strict sense of the word. Nature, left to itself, does not work, according to Bernoulli. In spite of his wish to bring together rational mechanics and practical mechanics, one perceived in the work of Bernoulli the subsistence of a rarely crossed disjunction between practical and theoretical fields.

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

Finite-key analysis for quantum key distribution with weak coherent pulses based on Bernoulli sampling

Science.gov (United States)

Kawakami, Shun; Sasaki, Toshihiko; Koashi, Masato

2017-07-01

An essential step in quantum key distribution is the estimation of parameters related to the leaked amount of information, which is usually done by sampling of the communication data. When the data size is finite, the final key rate depends on how the estimation process handles statistical fluctuations. Many of the present security analyses are based on the method with simple random sampling, where hypergeometric distribution or its known bounds are used for the estimation. Here we propose a concise method based on Bernoulli sampling, which is related to binomial distribution. Our method is suitable for the Bennett-Brassard 1984 (BB84) protocol with weak coherent pulses [C. H. Bennett and G. Brassard, Proceedings of the IEEE Conference on Computers, Systems and Signal Processing (IEEE, New York, 1984), Vol. 175], reducing the number of estimated parameters to achieve a higher key generation rate compared to the method with simple random sampling. We also apply the method to prove the security of the differential-quadrature-phase-shift (DQPS) protocol in the finite-key regime. The result indicates that the advantage of the DQPS protocol over the phase-encoding BB84 protocol in terms of the key rate, which was previously confirmed in the asymptotic regime, persists in the finite-key regime.

Probabilistic blind deconvolution of non-stationary sources

DEFF Research Database (Denmark)

Olsson, Rasmus Kongsgaard; Hansen, Lars Kai

2004-01-01

We solve a class of blind signal separation problems using a constrained linear Gaussian model. The observed signal is modelled by a convolutive mixture of colored noise signals with additive white noise. We derive a time-domain EM algorithm `KaBSS' which estimates the source signals...

Analysis of A Uniform Bernoulli – Euler Beam on Winkler Foundation ...

African Journals Online (AJOL)

ADOWIE PERE

2018-03-09

Mar 9, 2018 ... method to analyze Winkler foundation subjected to a harmonic moving load on a uniform Bernoulli – Euler Beam. MATLAB software was used to implement the Newmark time integration method to ... A lot of engineering structures under moving loads .... Because numerical procedure produce stability issue,.

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.

Comparison of Gaussian and non-Gaussian Atmospheric Profile Retrievals from Satellite Microwave Data

Science.gov (United States)

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.

Photonic generation of FCC-compliant UWB pulses based on modified Gaussian quadruplet and incoherent wavelength-to-time conversion

Science.gov (United States)

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.

Geometry of the free-sliding Bernoulli beam

Directory of Open Access Journals (Sweden)

Moreno Giovanni

2016-12-01

Full Text Available If a variational problem comes with no boundary conditions prescribed beforehand, and yet these arise as a consequence of the variation process itself, we speak of the free boundary values variational problem. Such is, for instance, the problem of finding the shortest curve whose endpoints can slide along two prescribed curves. There exists a rigorous geometric way to formulate this sort of problems on smooth manifolds with boundary, which we review here in a friendly self-contained way. As an application, we study the particular free boundary values variational problem of the free-sliding Bernoulli beam.

Gaussian process based intelligent sampling for measuring nano-structure surfaces

Science.gov (United States)

Sun, L. J.; Ren, M. J.; Yin, Y. H.

2016-09-01

Nanotechnology is the science and engineering that manipulate matters at nano scale, which can be used to create many new materials and devices with a vast range of applications. As the nanotech product increasingly enters the commercial marketplace, nanometrology becomes a stringent and enabling technology for the manipulation and the quality control of the nanotechnology. However, many measuring instruments, for instance scanning probe microscopy, are limited to relatively small area of hundreds of micrometers with very low efficiency. Therefore some intelligent sampling strategies should be required to improve the scanning efficiency for measuring large area. This paper presents a Gaussian process based intelligent sampling method to address this problem. The method makes use of Gaussian process based Bayesian regression as a mathematical foundation to represent the surface geometry, and the posterior estimation of Gaussian process is computed by combining the prior probability distribution with the maximum likelihood function. Then each sampling point is adaptively selected by determining the position which is the most likely outside of the required tolerance zone among the candidates and then inserted to update the model iteratively. Both simulationson the nominal surface and manufactured surface have been conducted on nano-structure surfaces to verify the validity of the proposed method. The results imply that the proposed method significantly improves the measurement efficiency in measuring large area structured surfaces.

Bernoulli numbers and polynomials from a more general point of view

International Nuclear Information System (INIS)

Dattoli, G.; Cesarano, C.; Lorenzutta, S.

2000-01-01

In this work it is applied the method of generating function, to introduce new forms of Bernoulli numbers and polynomials, which are exploited to derive further classes of partial sums involving generalized many index many variable polynomials. Analogous considerations are developed for the Euler numbers and polynomials [it

Gaussian process regression analysis for functional data

CERN Document Server

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

A Lattice-Based Identity-Based Proxy Blind Signature Scheme in the Standard Model

Directory of Open Access Journals (Sweden)

Lili Zhang

2014-01-01

Full Text Available A proxy blind signature scheme is a special form of blind signature which allowed a designated person called proxy signer to sign on behalf of original signers without knowing the content of the message. It combines the advantages of proxy signature and blind signature. Up to date, most proxy blind signature schemes rely on hard number theory problems, discrete logarithm, and bilinear pairings. Unfortunately, the above underlying number theory problems will be solvable in the postquantum era. Lattice-based cryptography is enjoying great interest these days, due to implementation simplicity and provable security reductions. Moreover, lattice-based cryptography is believed to be hard even for quantum computers. In this paper, we present a new identity-based proxy blind signature scheme from lattices without random oracles. The new scheme is proven to be strongly unforgeable under the standard hardness assumption of the short integer solution problem (SIS and the inhomogeneous small integer solution problem (ISIS. Furthermore, the secret key size and the signature length of our scheme are invariant and much shorter than those of the previous lattice-based proxy blind signature schemes. To the best of our knowledge, our construction is the first short lattice-based identity-based proxy blind signature scheme in the standard model.

Fourier transform and mean quadratic variation of Bernoulli convolution on homogeneous Cantor set

Energy Technology Data Exchange (ETDEWEB)

Yu Zuguo E-mail: yuzg@hotmail.comz.yu

2004-07-01

For the Bernoulli convolution on homogeneous Cantor set, under some condition, it is proved that the mean quadratic variation and the average of Fourier transform of this measure are bounded above and below.

Multiple zeta values and application to the Lacunary recurrence formulas of Bernoulli numbers

International Nuclear Information System (INIS)

Chen, Y-H

2008-01-01

This paper obtains a recurrence related to multiple zeta function, which generalizes the Newton recurrence for multiple zeta values for period 1. Moreover, we obtain some new Lacunary recurrence formulas of Bernoulli numbers

Gradient-based adaptation of general gaussian kernels.

Science.gov (United States)

Glasmachers, Tobias; Igel, Christian

2005-10-01

Gradient-based optimizing of gaussian kernel functions is considered. The gradient for the adaptation of scaling and rotation of the input space is computed to achieve invariance against linear transformations. This is done by using the exponential map as a parameterization of the kernel parameter manifold. By restricting the optimization to a constant trace subspace, the kernel size can be controlled. This is, for example, useful to prevent overfitting when minimizing radius-margin generalization performance measures. The concepts are demonstrated by training hard margin support vector machines on toy data.

20 CFR 404.1582 - A period of disability based on blindness.

Science.gov (United States)

2010-04-01

... 20 Employees' Benefits 2 2010-04-01 2010-04-01 false A period of disability based on blindness..., SURVIVORS AND DISABILITY INSURANCE (1950- ) Determining Disability and Blindness Blindness § 404.1582 A period of disability based on blindness. If we find that you are blind and you meet the insured status...

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)

Fuzzy-based simulation of real color blindness.

Science.gov (United States)

Lee, Jinmi; dos Santos, Wellington P

2010-01-01

About 8% of men are affected by color blindness. That population is at a disadvantage since they cannot perceive a substantial amount of the visual information. This work presents two computational tools developed to assist color blind people. The first one tests color blindness and assess its severity. The second tool is based on Fuzzy Logic, and implements a method proposed to simulate real red and green color blindness in order to generate synthetic cases of color vision disturbance in a statistically significant amount. Our purpose is to develop correction tools and obtain a deeper understanding of the accessibility problems faced by people with chromatic visual impairment.

Box-Particle Cardinality Balanced Multi-Target Multi-Bernoulli Filter

OpenAIRE

L. Song; X. Zhao

2014-01-01

As a generalized particle filtering, the box-particle filter (Box-PF) has a potential to process the measurements affected by bounded error of unknown distributions and biases. Inspired by the Box-PF, a novel implementation for multi-target tracking, called box-particle cardinality balanced multi-target multi-Bernoulli (Box-CBMeMBer) filter is presented in this paper. More important, to eliminate the negative effect of clutters in the estimation of the numbers of targets, an improved generali...

Measurement of carotid bifurcation pressure gradients using the Bernoulli principle.

Science.gov (United States)

Illig, K A; Ouriel, K; DeWeese, J A; Holen, J; Green, R M

1996-04-01

Current randomized prospective studies suggest that the degree of carotid stenosis is a critical element in deciding whether surgical or medical treatment is appropriate. Of potential interest is the actual pressure drop caused by the blockage, but no direct non-invasive means of quantifying the hemodynamic consequences of carotid artery stenoses currently exists. The present prospective study examined whether preoperative pulsed-Doppler duplex ultrasonographic velocity (v) measurements could be used to predict pressure gradients (delta P) caused by carotid artery stenoses, and whether such measurements could be used to predict angiographic percent diameter reduction. Preoperative Doppler velocity and intraoperative direct pressure measurements were obtained, and per cent diameter angiographic stenosis measured in 76 consecutive patients who underwent 77 elective carotid endarterectomies. Using the Bernoulli principle (delta P = 4v(2), pressure gradients across the stenoses were calculated. The predicted delta P, as well as absolute velocities and internal carotid artery/common carotid velocity ratios were compared with the actual delta P measured intraoperatively and with preoperative angiography and oculopneumoplethysmography (OPG) results. An end-diastolic velocity of > or = 1 m/s and an end-diastolic internal carotid artery/common carotid artery velocity ratio of > or = 10 predicted a 50% diameter angiographic stenosis with 100% specificity. Although statistical significance was reached, preoperative pressure gradients derived from the Bernoulli equation could not predict actual individual intraoperative pressure gradients with enough accuracy to allow decision making on an individual basis. Velocity measurements were as specific and more sensitive than OPG results. Delta P as predicted by the Bernoulli equation is not sufficiently accurate at the carotid bifurcation to be useful for clinical decision making on an individual basis. However, end

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)

Efficient Kernel-Based Ensemble Gaussian Mixture Filtering

KAUST Repository

Liu, Bo

2015-11-11

We consider the Bayesian filtering problem for data assimilation following the kernel-based ensemble Gaussian-mixture filtering (EnGMF) approach introduced by Anderson and Anderson (1999). In this approach, the posterior distribution of the system state is propagated with the model using the ensemble Monte Carlo method, providing a forecast ensemble that is then used to construct a prior Gaussian-mixture (GM) based on the kernel density estimator. This results in two update steps: a Kalman filter (KF)-like update of the ensemble members and a particle filter (PF)-like update of the weights, followed by a resampling step to start a new forecast cycle. After formulating EnGMF for any observational operator, we analyze the influence of the bandwidth parameter of the kernel function on the covariance of the posterior distribution. We then focus on two aspects: i) the efficient implementation of EnGMF with (relatively) small ensembles, where we propose a new deterministic resampling strategy preserving the first two moments of the posterior GM to limit the sampling error; and ii) the analysis of the effect of the bandwidth parameter on contributions of KF and PF updates and on the weights variance. Numerical results using the Lorenz-96 model are presented to assess the behavior of EnGMF with deterministic resampling, study its sensitivity to different parameters and settings, and evaluate its performance against ensemble KFs. The proposed EnGMF approach with deterministic resampling suggests improved estimates in all tested scenarios, and is shown to require less localization and to be less sensitive to the choice of filtering parameters.

Modulation Transfer Function of a Gaussian Beam Based on the Generalized Modified Atmospheric Spectrum

Directory of Open Access Journals (Sweden)

Chao Gao

2016-01-01

Full Text Available This paper investigates the modulation transfer function of a Gaussian beam propagating through a horizontal path in weak-fluctuation non-Kolmogorov turbulence. Mathematical expressions are obtained based on the generalized modified atmospheric spectrum, which includes the spectral power law value of non-Kolmogorov turbulence, the finite inner and outer scales of turbulence, and other optical parameters of the Gaussian beam. The numerical results indicate that the atmospheric turbulence would produce less negative effects on the wireless optical communication system with an increase in the inner scale of turbulence. Additionally, the increased outer scale of turbulence makes a Gaussian beam influenced more seriously by the atmospheric turbulence.

Reproducing kernel Hilbert spaces of Gaussian priors

NARCIS (Netherlands)

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

Improved system blind identification based on second-order ...

Indian Academy of Sciences (India)

An improved system blind identification method based on second- order cyclostationary statistics and the properties of group delay, has been ... In the last decade, there has been considerable research on achieving blind identification.

Inequality for the infinite-cluster density in Bernoulli percolation

International Nuclear Information System (INIS)

Chayes, J.T.; Chayes, L.

1986-01-01

Under a certain assumption (which is satisfied whenever there is a dense infinite cluster in the half-space), we prove a differential inequality for the infinite-cluster density, P/sub infinity/(p), in Bernoulli percolation. The principal implication of this result is that if P/sub infinity/(p) vanishes with critical exponent β, then β obeys the mean-field bound β< or =1. As a corollary, we also derive an inequality relating the backbone density, the truncated susceptibility, and the infinite-cluster density

Bernoulli Variational Problem and Beyond

KAUST Repository

Lorz, Alexander

2013-12-17

The question of \\'cutting the tail\\' of the solution of an elliptic equation arises naturally in several contexts and leads to a singular perturbation problem under the form of a strong cut-off. We consider both the PDE with a drift and the symmetric case where a variational problem can be stated. It is known that, in both cases, the same critical scale arises for the size of the singular perturbation. More interesting is that in both cases another critical parameter (of order one) arises that decides when the limiting behaviour is non-degenerate. We study both theoretically and numerically the values of this critical parameter and, in the symmetric case, ask if the variational solution leads to the same value as for the maximal solution of the PDE. Finally we propose a weak formulation of the limiting Bernoulli problem which incorporates both Dirichlet and Neumann boundary condition. © 2013 Springer-Verlag Berlin Heidelberg.

Bernoulli's Principle: The Effects of Instruction on Young Children's Understanding of Flight.

Science.gov (United States)

Fleege, Pamela O.; And Others

This study examined the effects of hands-on instruction on young children's understanding of an aspect of flight, specifically Bernoulli's principle. First, 137 public school children, ages 5 through 8 years, were interviewed about their understanding of how an airplane flies. Two weeks later, the subjects participated in two hands-on…

Interconversion of pure Gaussian states requiring non-Gaussian operations

Science.gov (United States)

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.

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

Treatment of non-Gaussian tails of multiple Coulomb scattering in track fitting with a Gaussian-sum filter

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

Uniform approximations of Bernoulli and Euler polynomials in terms of hyperbolic functions

NARCIS (Netherlands)

J.L. López; N.M. Temme (Nico)

1998-01-01

textabstractBernoulli and Euler polynomials are considered for large values of the order. Convergent expansions are obtained for $B_n(nz+1/2)$ and $E_n(nz+1/2)$ in powers of $n^{-1$, with coefficients being rational functions of $z$ and hyperbolic functions of argument $1/2z$. These expansions are

Childhood blindness in India: a population based perspective

Science.gov (United States)

Dandona, R; Dandona, L

2003-01-01

Aim: To estimate the prevalence and causes of blindness in children in the southern Indian state of Andhra Pradesh. Methods: These data were obtained as part of two population based studies in which 6935 children ≤15 years of age participated. Blindness was defined as presenting distance visual acuity <6/60 in the better eye. Results: The prevalence of childhood blindness was 0.17% (95% confidence interval 0.09 to 0.30). Treatable refractive error caused 33.3% of the blindness, followed by 16.6% due to preventable causes (8.3% each due to vitamin A deficiency and amblyopia after cataract surgery). The major causes of the remaining blindness included congenital eye anomalies (16.7%) and retinal degeneration (16.7%). Conclusion: In the context of Vision 2020, the priorities for action to reduce childhood blindness in India are refractive error, cataract related amblyopia, and corneal diseases. PMID:12598433

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)

Non-gaussian turbulence

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)

Lean buffering in serial production lines with Bernoulli machines

Directory of Open Access Journals (Sweden)

A. B. Hu

2006-01-01

Full Text Available Lean buffering is the smallest buffer capacity necessary to ensure the desired production rate of a manufacturing system. In this paper, analytical methods for selecting lean buffering in serial production lines are developed under the assumption that the machines obey the Bernoulli reliability model. Both closed-form expressions and recursive approaches are investigated. The cases of identical and nonidentical machines are analyzed. Results obtained can be useful for production line designers and production managers to maintain the required production rate with the smallest possible inventories.

THE CPA QUALIFICATION METHOD BASED ON THE GAUSSIAN CURVE FITTING

Directory of Open Access Journals (Sweden)

M.T. Adithia

2015-01-01

Full Text Available The Correlation Power Analysis (CPA attack is an attack on cryptographic devices, especially smart cards. The results of the attack are correlation traces. Based on the correlation traces, an evaluation is done to observe whether significant peaks appear in the traces or not. The evaluation is done manually, by experts. If significant peaks appear then the smart card is not considered secure since it is assumed that the secret key is revealed. We develop a method that objectively detects peaks and decides which peak is significant. We conclude that using the Gaussian curve fitting method, the subjective qualification of the peak significance can be objectified. Thus, better decisions can be taken by security experts. We also conclude that the Gaussian curve fitting method is able to show the influence of peak sizes, especially the width and height, to a significance of a particular peak.

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...

Inverse sampled Bernoulli (ISB) procedure for estimating a population proportion, with nuclear material applications

International Nuclear Information System (INIS)

Wright, T.

1982-01-01

A new sampling procedure is introduced for estimating a population proportion. The procedure combines the ideas of inverse binomial sampling and Bernoulli sampling. An unbiased estimator is given with its variance. The procedure can be viewed as a generalization of inverse binomial sampling

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.

Subspace Based Blind Sparse Channel Estimation

DEFF Research Database (Denmark)

Hayashi, Kazunori; Matsushima, Hiroki; Sakai, Hideaki

2012-01-01

The paper proposes a subspace based blind sparse channel estimation method using 1–2 optimization by replacing the 2–norm minimization in the conventional subspace based method by the 1–norm minimization problem. Numerical results confirm that the proposed method can significantly improve...

Numerical modeling of macrodispersion in heterogeneous media: a comparison of multi-Gaussian and non-multi-Gaussian models

Science.gov (United States)

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

The backward phase flow and FBI-transform-based Eulerian Gaussian beams for the Schroedinger equation

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.

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

Exact Partial Information Decompositions for Gaussian Systems Based on Dependency Constraints

Directory of Open Access Journals (Sweden)

Jim W. Kay

2018-03-01

Full Text Available The Partial Information Decomposition, introduced by Williams P. L. et al. (2010, provides a theoretical framework to characterize and quantify the structure of multivariate information sharing. A new method ( I dep has recently been proposed by James R. G. et al. (2017 for computing a two-predictor partial information decomposition over discrete spaces. A lattice of maximum entropy probability models is constructed based on marginal dependency constraints, and the unique information that a particular predictor has about the target is defined as the minimum increase in joint predictor-target mutual information when that particular predictor-target marginal dependency is constrained. Here, we apply the I dep approach to Gaussian systems, for which the marginally constrained maximum entropy models are Gaussian graphical models. Closed form solutions for the I dep PID are derived for both univariate and multivariate Gaussian systems. Numerical and graphical illustrations are provided, together with practical and theoretical comparisons of the I dep PID with the minimum mutual information partial information decomposition ( I mmi , which was discussed by Barrett A. B. (2015. The results obtained using I dep appear to be more intuitive than those given with other methods, such as I mmi , in which the redundant and unique information components are constrained to depend only on the predictor-target marginal distributions. In particular, it is proved that the I mmi method generally produces larger estimates of redundancy and synergy than does the I dep method. In discussion of the practical examples, the PIDs are complemented by the use of tests of deviance for the comparison of Gaussian graphical models.

An optimized Nash nonlinear grey Bernoulli model based on particle swarm optimization and its application in prediction for the incidence of Hepatitis B in Xinjiang, China.

Science.gov (United States)

Zhang, Liping; Zheng, Yanling; Wang, Kai; Zhang, Xueliang; Zheng, Yujian

2014-06-01

In this paper, by using a particle swarm optimization algorithm to solve the optimal parameter estimation problem, an improved Nash nonlinear grey Bernoulli model termed PSO-NNGBM(1,1) is proposed. To test the forecasting performance, the optimized model is applied for forecasting the incidence of hepatitis B in Xinjiang, China. Four models, traditional GM(1,1), grey Verhulst model (GVM), original nonlinear grey Bernoulli model (NGBM(1,1)) and Holt-Winters exponential smoothing method, are also established for comparison with the proposed model under the criteria of mean absolute percentage error and root mean square percent error. The prediction results show that the optimized NNGBM(1,1) model is more accurate and performs better than the traditional GM(1,1), GVM, NGBM(1,1) and Holt-Winters exponential smoothing method. Copyright © 2014. Published by Elsevier Ltd.

High-Order Local Pooling and Encoding Gaussians Over a Dictionary of Gaussians.

Science.gov (United States)

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.

Numerical solutions of incompressible Navier-Stokes equations using modified Bernoulli's law

Science.gov (United States)

Shatalov, A.; Hafez, M.

2003-11-01

Simulations of incompressible flows are important for many practical applications in aeronautics and beyond, particularly in the high Reynolds number regime. The present formulation is based on Helmholtz velocity decomposition where the velocity is presented as the gradient of a potential plus a rotational component. Substituting in the continuity equation yields a Poisson equation for the potential which is solved with a zero normal derivative at solid surfaces. The momentum equation is used to update the rotational component with no slip/no penetration surface boundary conditions. The pressure is related to the potential function through a special relation which is a generalization of Bernoulli's law, with a viscous term included. Results of calculations for two- and three-dimensional problems prove that the present formulation is a valid approach, with some possible benefits compared to existing methods.

An improved advertising CTR prediction approach based on the fuzzy deep neural network.

Science.gov (United States)

Jiang, Zilong; Gao, Shu; Li, Mingjiang

2018-01-01

Combining a deep neural network with fuzzy theory, this paper proposes an advertising click-through rate (CTR) prediction approach based on a fuzzy deep neural network (FDNN). In this approach, fuzzy Gaussian-Bernoulli restricted Boltzmann machine (FGBRBM) is first applied to input raw data from advertising datasets. Next, fuzzy restricted Boltzmann machine (FRBM) is used to construct the fuzzy deep belief network (FDBN) with the unsupervised method layer by layer. Finally, fuzzy logistic regression (FLR) is utilized for modeling the CTR. The experimental results show that the proposed FDNN model outperforms several baseline models in terms of both data representation capability and robustness in advertising click log datasets with noise.

On the discrete Frobenius-Perron operator of the Bernoulli map

International Nuclear Information System (INIS)

Bai Zaiqiao

2006-01-01

We study the spectra of a finite-dimensional Frobenius-Perron operator (matrix) of the Bernoulli map derived from phase space discretization. The eigenvalues and (right and left) eigenvectors are analytically calculated, which are closely related to periodic orbits on the partition points. In the degenerate case, Jordan decomposition of the matrix is explicitly constructed. Except for the isolated eigenvalue 1, there is no definite limit with respect to eigenvalues when n → ∞. The behaviour of the eigenvectors is discussed in the limit of large n

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.

Target 3-D reconstruction of streak tube imaging lidar based on Gaussian fitting

Science.gov (United States)

Yuan, Qingyu; Niu, Lihong; Hu, Cuichun; Wu, Lei; Yang, Hongru; Yu, Bing

2018-02-01

Streak images obtained by the streak tube imaging lidar (STIL) contain the distance-azimuth-intensity information of a scanned target, and a 3-D reconstruction of the target can be carried out through extracting the characteristic data of multiple streak images. Significant errors will be caused in the reconstruction result by the peak detection method due to noise and other factors. So as to get a more precise 3-D reconstruction, a peak detection method based on Gaussian fitting of trust region is proposed in this work. Gaussian modeling is performed on the returned wave of single time channel of each frame, then the modeling result which can effectively reduce the noise interference and possesses a unique peak could be taken as the new returned waveform, lastly extracting its feature data through peak detection. The experimental data of aerial target is for verifying this method. This work shows that the peak detection method based on Gaussian fitting reduces the extraction error of the feature data to less than 10%; utilizing this method to extract the feature data and reconstruct the target make it possible to realize the spatial resolution with a minimum 30 cm in the depth direction, and improve the 3-D imaging accuracy of the STIL concurrently.

Time Series Analysis of Non-Gaussian Observations Based on State Space Models from Both Classical and Bayesian Perspectives

NARCIS (Netherlands)

Durbin, J.; Koopman, S.J.M.

1998-01-01

The analysis of non-Gaussian time series using state space models is considered from both classical and Bayesian perspectives. The treatment in both cases is based on simulation using importance sampling and antithetic variables; Monte Carlo Markov chain methods are not employed. Non-Gaussian

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

New deconvolution method for microscopic images based on the continuous Gaussian radial basis function interpolation model.

Science.gov (United States)

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.

Pseudorandom number generation using chaotic true orbits of the Bernoulli map

Energy Technology Data Exchange (ETDEWEB)

Saito, Asaki, E-mail: saito@fun.ac.jp [Future University Hakodate, 116-2 Kamedanakano-cho, Hakodate, Hokkaido 041-8655 (Japan); Yamaguchi, Akihiro [Fukuoka Institute of Technology, 3-30-1 Wajiro-higashi, Higashi-ku, Fukuoka 811-0295 (Japan)

2016-06-15

We devise a pseudorandom number generator that exactly computes chaotic true orbits of the Bernoulli map on quadratic algebraic integers. Moreover, we describe a way to select the initial points (seeds) for generating multiple pseudorandom binary sequences. This selection method distributes the initial points almost uniformly (equidistantly) in the unit interval, and latter parts of the generated sequences are guaranteed not to coincide. We also demonstrate through statistical testing that the generated sequences possess good randomness properties.

Probabilistic mixture-based image modelling

Czech Academy of Sciences Publication Activity Database

Haindl, Michal; Havlíček, Vojtěch; Grim, Jiří

2011-01-01

Roč. 47, č. 3 (2011), s. 482-500 ISSN 0023-5954 R&D Projects: GA MŠk 1M0572; GA ČR GA102/08/0593 Grant - others:CESNET(CZ) 387/2010; GA MŠk(CZ) 2C06019; GA ČR(CZ) GA103/11/0335 Institutional research plan: CEZ:AV0Z10750506 Keywords : BTF texture modelling * discrete distribution mixtures * Bernoulli mixture * Gaussian mixture * multi-spectral texture modelling Subject RIV: BD - Theory of Information Impact factor: 0.454, year: 2011 http://library.utia.cas.cz/separaty/2011/RO/haindl-0360244.pdf

Nested polynomial trends for the improvement of Gaussian process-based predictors

Science.gov (United States)

Perrin, G.; Soize, C.; Marque-Pucheu, S.; Garnier, J.

2017-10-01

The role of simulation keeps increasing for the sensitivity analysis and the uncertainty quantification of complex systems. Such numerical procedures are generally based on the processing of a huge amount of code evaluations. When the computational cost associated with one particular evaluation of the code is high, such direct approaches based on the computer code only, are not affordable. Surrogate models have therefore to be introduced to interpolate the information given by a fixed set of code evaluations to the whole input space. When confronted to deterministic mappings, the Gaussian process regression (GPR), or kriging, presents a good compromise between complexity, efficiency and error control. Such a method considers the quantity of interest of the system as a particular realization of a Gaussian stochastic process, whose mean and covariance functions have to be identified from the available code evaluations. In this context, this work proposes an innovative parametrization of this mean function, which is based on the composition of two polynomials. This approach is particularly relevant for the approximation of strongly non linear quantities of interest from very little information. After presenting the theoretical basis of this method, this work compares its efficiency to alternative approaches on a series of examples.

Tip-tilt disturbance model identification based on non-linear least squares fitting for Linear Quadratic Gaussian control

Science.gov (United States)

Yang, Kangjian; Yang, Ping; Wang, Shuai; Dong, Lizhi; Xu, Bing

2018-05-01

We propose a method to identify tip-tilt disturbance model for Linear Quadratic Gaussian control. This identification method based on Levenberg-Marquardt method conducts with a little prior information and no auxiliary system and it is convenient to identify the tip-tilt disturbance model on-line for real-time control. This identification method makes it easy that Linear Quadratic Gaussian control runs efficiently in different adaptive optics systems for vibration mitigation. The validity of the Linear Quadratic Gaussian control associated with this tip-tilt disturbance model identification method is verified by experimental data, which is conducted in replay mode by simulation.

An ID-based Blind Signature Scheme from Bilinear Pairings

OpenAIRE

B.Umaprasada Rao; K.A.Ajmath

2010-01-01

Blind signatures, introduced by Chaum, allow a user to obtain a signature on a message without revealing any thing about the message to the signer. Blind signatures play on important role in plenty of applications such as e-voting, e-cash system where anonymity is of great concern. Identity based(ID-based) public key cryptography can be a good alternative for certified based public key setting, especially when efficient key management and moderate security are required. In this paper, we prop...

Approximation problems with the divergence criterion for Gaussian variablesand Gaussian processes

NARCIS (Netherlands)

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.

Broadband optical frequency comb generator based on driving N-cascaded modulators by Gaussian-shaped waveform

Science.gov (United States)

Hmood, Jassim K.; Harun, Sulaiman W.

2018-05-01

A new approach for realizing a wideband optical frequency comb (OFC) generator based on driving cascaded modulators by a Gaussian-shaped waveform, is proposed and numerically demonstrated. The setup includes N-cascaded MZMs, a single Gaussian-shaped waveform generator, and N-1 electrical time delayer. The first MZM is driven directly by a Gaussian-shaped waveform, while delayed replicas of the Gaussian-shaped waveform drive the other MZMs. An analytical model that describes the proposed OFC generator is provided to study the effect of number and chirp factor of cascaded MZM as well as pulse width on output spectrum. Optical frequency combs at frequency spacing of 1 GHz are generated by applying Gaussian-shaped waveform at pulse widths ranging from 200 to 400 ps. Our results reveal that, the number of comb lines is inversely proportional to the pulse width and directly proportional to both number and chirp factor of cascaded MZMs. At pulse width of 200 ps and chirp factor of 4, 67 frequency lines can be measured at output spectrum of two-cascaded MZMs setup. Whereas, increasing the number of cascaded stages to 3, 4, and 5, the optical spectra counts 89, 109 and 123 frequency lines; respectively. When the delay time is optimized, 61 comb lines can be achieved with power fluctuations of less than 1 dB for five-cascaded MZMs setup.

Blind compressed sensing image reconstruction based on alternating direction method

Science.gov (United States)

Liu, Qinan; Guo, Shuxu

2018-04-01

In order to solve the problem of how to reconstruct the original image under the condition of unknown sparse basis, this paper proposes an image reconstruction method based on blind compressed sensing model. In this model, the image signal is regarded as the product of a sparse coefficient matrix and a dictionary matrix. Based on the existing blind compressed sensing theory, the optimal solution is solved by the alternative minimization method. The proposed method solves the problem that the sparse basis in compressed sensing is difficult to represent, which restrains the noise and improves the quality of reconstructed image. This method ensures that the blind compressed sensing theory has a unique solution and can recover the reconstructed original image signal from a complex environment with a stronger self-adaptability. The experimental results show that the image reconstruction algorithm based on blind compressed sensing proposed in this paper can recover high quality image signals under the condition of under-sampling.

Image illumination enhancement with an objective no-reference measure of illumination assessment based on Gaussian distribution mapping

Directory of Open Access Journals (Sweden)

Gholamreza Anbarjafari

2015-12-01

Full Text Available Illumination problems have been an important concern in many image processing applications. The pattern of the histogram on an image introduces meaningful features; hence within the process of illumination enhancement, it is important not to destroy such information. In this paper we propose a method to enhance image illumination using Gaussian distribution mapping which also keeps the information laid on the pattern of the histogram on the original image. First a Gaussian distribution based on the mean and standard deviation of the input image will be calculated. Simultaneously a Gaussian distribution with the desired mean and standard deviation will be calculated. Then a cumulative distribution function of each of the Gaussian distributions will be calculated and used in order to map the old pixel value onto the new pixel value. Another important issue in the field of illumination enhancement is absence of a quantitative measure for the assessment of the illumination of an image. In this research work, a quantitative measure indicating the illumination state, i.e. contrast level and brightness of an image, is also proposed. The measure utilizes the estimated Gaussian distribution of the input image and the Kullback-Leibler Divergence (KLD between the estimated Gaussian and the desired Gaussian distributions to calculate the quantitative measure. The experimental results show the effectiveness and the reliability of the proposed illumination enhancement technique, as well as the proposed illumination assessment measure over conventional and state-of-the-art techniques.

Bending of Euler-Bernoulli nanobeams based on the strain-driven and stress-driven nonlocal integral models: a numerical approach

Science.gov (United States)

Oskouie, M. Faraji; Ansari, R.; Rouhi, H.

2018-04-01

Eringen's nonlocal elasticity theory is extensively employed for the analysis of nanostructures because it is able to capture nanoscale effects. Previous studies have revealed that using the differential form of the strain-driven version of this theory leads to paradoxical results in some cases, such as bending analysis of cantilevers, and recourse must be made to the integral version. In this article, a novel numerical approach is developed for the bending analysis of Euler-Bernoulli nanobeams in the context of strain- and stress-driven integral nonlocal models. This numerical approach is proposed for the direct solution to bypass the difficulties related to converting the integral governing equation into a differential equation. First, the governing equation is derived based on both strain-driven and stress-driven nonlocal models by means of the minimum total potential energy. Also, in each case, the governing equation is obtained in both strong and weak forms. To solve numerically the derived equations, matrix differential and integral operators are constructed based upon the finite difference technique and trapezoidal integration rule. It is shown that the proposed numerical approach can be efficiently applied to the strain-driven nonlocal model with the aim of resolving the mentioned paradoxes. Also, it is able to solve the problem based on the strain-driven model without inconsistencies of the application of this model that are reported in the literature.

Alternate Solution to Generalized Bernoulli Equations via an Integrating Factor: An Exact Differential Equation Approach

Science.gov (United States)

Tisdell, C. C.

2017-01-01

Solution methods to exact differential equations via integrating factors have a rich history dating back to Euler (1740) and the ideas enjoy applications to thermodynamics and electromagnetism. Recently, Azevedo and Valentino presented an analysis of the generalized Bernoulli equation, constructing a general solution by linearizing the problem…

Adaptive Laguerre-Gaussian variant of the Gaussian beam expansion method.

Science.gov (United States)

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.

Information-Dispersion-Entropy-Based Blind Recognition of Binary BCH Codes in Soft Decision Situations

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.

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.

Gaussian Mixture Model of Heart Rate Variability

Science.gov (United States)

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

The Research of Indoor Positioning Based on Double-peak Gaussian Model

Directory of Open Access Journals (Sweden)

Lina Chen

2014-04-01

Full Text Available Location fingerprinting using Wi-Fi signals has been very popular and is a well accepted indoor positioning method. The key issue of the fingerprinting approach is generating the fingerprint radio map. Limited by the practical workload, only a few samples of the received signal strength are collected at each reference point. Unfortunately, fewer samples cannot accurately represent the actual distribution of the signal strength from each access point. This study finds most Wi- Fi signals have two peaks. According to the new finding, a double-peak Gaussian arithmetic is proposed to generate a fingerprint radio map. This approach requires little time to receive WiFi signals and it easy to estimate the parameters of the double-peak Gaussian function. Compared to the Gaussian function and histogram method to generate a fingerprint radio map, this method better approximates the occurrence signal distribution. This paper also compared the positioning accuracy using K-Nearest Neighbour theory for three radio maps, the test results show that the positioning distance error utilizing the double-peak Gaussian function is better than the other two methods.

Computational simulations of vocal fold vibration: Bernoulli versus Navier-Stokes.

Science.gov (United States)

Decker, Gifford Z; Thomson, Scott L

2007-05-01

The use of the mechanical energy (ME) equation for fluid flow, an extension of the Bernoulli equation, to predict the aerodynamic loading on a two-dimensional finite element vocal fold model is examined. Three steady, one-dimensional ME flow models, incorporating different methods of flow separation point prediction, were compared. For two models, determination of the flow separation point was based on fixed ratios of the glottal area at separation to the minimum glottal area; for the third model, the separation point determination was based on fluid mechanics boundary layer theory. Results of flow rate, separation point, and intraglottal pressure distribution were compared with those of an unsteady, two-dimensional, finite element Navier-Stokes model. Cases were considered with a rigid glottal profile as well as with a vibrating vocal fold. For small glottal widths, the three ME flow models yielded good predictions of flow rate and intraglottal pressure distribution, but poor predictions of separation location. For larger orifice widths, the ME models were poor predictors of flow rate and intraglottal pressure, but they satisfactorily predicted separation location. For the vibrating vocal fold case, all models resulted in similar predictions of mean intraglottal pressure, maximum orifice area, and vibration frequency, but vastly different predictions of separation location and maximum flow rate.

An efficient coupled polynomial interpolation scheme to eliminate material-locking in the Euler-Bernoulli piezoelectric beam finite element

Directory of Open Access Journals (Sweden)

Litesh N. Sulbhewar

Full Text Available The convergence characteristic of the conventional two-noded Euler-Bernoulli piezoelectric beam finite element depends on the configuration of the beam cross-section. The element shows slower convergence for the asymmetric material distribution in the beam cross-section due to 'material-locking' caused by extension-bending coupling. Hence, the use of conventional Euler-Bernoulli beam finite element to analyze piezoelectric beams which are generally made of the host layer with asymmetrically surface bonded piezoelectric layers/patches, leads to increased computational effort to yield converged results. Here, an efficient coupled polynomial interpolation scheme is proposed to improve the convergence of the Euler-Bernoulli piezoelectric beam finite elements, by eliminating ill-effects of material-locking. The equilibrium equations, derived using a variational formulation, are used to establish relationships between field variables. These relations are used to find a coupled quadratic polynomial for axial displacement, having contributions from an assumed cubic polynomial for transverse displacement and assumed linear polynomials for layerwise electric potentials. A set of coupled shape functions derived using these polynomials efficiently handles extension-bending and electromechanical couplings at the field interpolation level itself in a variationally consistent manner, without increasing the number of nodal degrees of freedom. The comparison of results obtained from numerical simulation of test problems shows that the convergence characteristic of the proposed element is insensitive to the material configuration of the beam cross-section.

Nonlinear and non-Gaussian Bayesian based handwriting beautification

Science.gov (United States)

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.

A comparison on the propagation characteristics of focused Gaussian beam and fundamental Gaussian beam in vacuum

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

Non-Gaussian Systems Control Performance Assessment Based on Rational Entropy

Directory of Open Access Journals (Sweden)

Jinglin Zhou

2018-05-01

Full Text Available Control loop Performance Assessment (CPA plays an important role in system operations. Stochastic statistical CPA index, such as a minimum variance controller (MVC-based CPA index, is one of the most widely used CPA indices. In this paper, a new minimum entropy controller (MEC-based CPA method of linear non-Gaussian systems is proposed. In this method, probability density function (PDF and rational entropy (RE are respectively used to describe the characteristics and the uncertainty of random variables. To better estimate the performance benchmark, an improved EDA algorithm, which is used to estimate the system parameters and noise PDF, is given. The effectiveness of the proposed method is illustrated through case studies on an ARMAX system.

Analysis of MAP/PH(1, PH(2/2 Queue with Bernoulli Vacations

Directory of Open Access Journals (Sweden)

V. Thangaraj

2008-12-01

Full Text Available We consider a two-heterogeneous-server queueing system with Bernoulli vacation in which customers arrive according to a Markovian arrival process (MAP. Servers returning from vacation immediately take another vacation if no customer is waiting. Using matrix-geometric method, the steady-state probability of the number of customers in the system is investigated. Some important performance measures are obtained. The waiting time distribution and the mean waiting time are also discussed. Finally, some numerical illustrations are provided.

Normal-Gamma-Bernoulli Peak Detection for Analysis of Comprehensive Two-Dimensional Gas Chromatography Mass Spectrometry Data.

Science.gov (United States)

Kim, Seongho; Jang, Hyejeong; Koo, Imhoi; Lee, Joohyoung; Zhang, Xiang

2017-01-01

Compared to other analytical platforms, comprehensive two-dimensional gas chromatography coupled with mass spectrometry (GC×GC-MS) has much increased separation power for analysis of complex samples and thus is increasingly used in metabolomics for biomarker discovery. However, accurate peak detection remains a bottleneck for wide applications of GC×GC-MS. Therefore, the normal-exponential-Bernoulli (NEB) model is generalized by gamma distribution and a new peak detection algorithm using the normal-gamma-Bernoulli (NGB) model is developed. Unlike the NEB model, the NGB model has no closed-form analytical solution, hampering its practical use in peak detection. To circumvent this difficulty, three numerical approaches, which are fast Fourier transform (FFT), the first-order and the second-order delta methods (D1 and D2), are introduced. The applications to simulated data and two real GC×GC-MS data sets show that the NGB-D1 method performs the best in terms of both computational expense and peak detection performance.

Smartphone based face recognition tool for the blind.

Science.gov (United States)

Kramer, K M; Hedin, D S; Rolkosky, D J

2010-01-01

The inability to identify people during group meetings is a disadvantage for blind people in many professional and educational situations. To explore the efficacy of face recognition using smartphones in these settings, we have prototyped and tested a face recognition tool for blind users. The tool utilizes Smartphone technology in conjunction with a wireless network to provide audio feedback of the people in front of the blind user. Testing indicated that the face recognition technology can tolerate up to a 40 degree angle between the direction a person is looking and the camera's axis and a 96% success rate with no false positives. Future work will be done to further develop the technology for local face recognition on the smartphone in addition to remote server based face recognition.

Gaussian process-based Bayesian nonparametric inference of population size trajectories from gene genealogies.

Science.gov (United States)

Palacios, Julia A; Minin, Vladimir N

2013-03-01

Changes in population size influence genetic diversity of the population and, as a result, leave a signature of these changes in individual genomes in the population. We are interested in the inverse problem of reconstructing past population dynamics from genomic data. We start with a standard framework based on the coalescent, a stochastic process that generates genealogies connecting randomly sampled individuals from the population of interest. These genealogies serve as a glue between the population demographic history and genomic sequences. It turns out that only the times of genealogical lineage coalescences contain information about population size dynamics. Viewing these coalescent times as a point process, estimating population size trajectories is equivalent to estimating a conditional intensity of this point process. Therefore, our inverse problem is similar to estimating an inhomogeneous Poisson process intensity function. We demonstrate how recent advances in Gaussian process-based nonparametric inference for Poisson processes can be extended to Bayesian nonparametric estimation of population size dynamics under the coalescent. We compare our Gaussian process (GP) approach to one of the state-of-the-art Gaussian Markov random field (GMRF) methods for estimating population trajectories. Using simulated data, we demonstrate that our method has better accuracy and precision. Next, we analyze two genealogies reconstructed from real sequences of hepatitis C and human Influenza A viruses. In both cases, we recover more believed aspects of the viral demographic histories than the GMRF approach. We also find that our GP method produces more reasonable uncertainty estimates than the GMRF method. Copyright © 2013, The International Biometric Society.

Fast Gaussian kernel learning for classification tasks based on specially structured global optimization.

Science.gov (United States)

Zhong, Shangping; Chen, Tianshun; He, Fengying; Niu, Yuzhen

2014-09-01

For a practical pattern classification task solved by kernel methods, the computing time is mainly spent on kernel learning (or training). However, the current kernel learning approaches are based on local optimization techniques, and hard to have good time performances, especially for large datasets. Thus the existing algorithms cannot be easily extended to large-scale tasks. In this paper, we present a fast Gaussian kernel learning method by solving a specially structured global optimization (SSGO) problem. We optimize the Gaussian kernel function by using the formulated kernel target alignment criterion, which is a difference of increasing (d.i.) functions. Through using a power-transformation based convexification method, the objective criterion can be represented as a difference of convex (d.c.) functions with a fixed power-transformation parameter. And the objective programming problem can then be converted to a SSGO problem: globally minimizing a concave function over a convex set. The SSGO problem is classical and has good solvability. Thus, to find the global optimal solution efficiently, we can adopt the improved Hoffman's outer approximation method, which need not repeat the searching procedure with different starting points to locate the best local minimum. Also, the proposed method can be proven to converge to the global solution for any classification task. We evaluate the proposed method on twenty benchmark datasets, and compare it with four other Gaussian kernel learning methods. Experimental results show that the proposed method stably achieves both good time-efficiency performance and good classification performance. Copyright © 2014 Elsevier Ltd. All rights reserved.

A non-Gaussian multivariate distribution with all lower-dimensional Gaussians and related families

KAUST Repository

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

KAUST Repository

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 Process-Mixture Conditional Heteroscedasticity.

Science.gov (United States)

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.

Gaussian entanglement distribution via satellite

Science.gov (United States)

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.

Spatio-Temporal Data Analysis at Scale Using Models Based on Gaussian Processes

Energy Technology Data Exchange (ETDEWEB)

Stein, Michael [Univ. of Chicago, IL (United States)

2017-03-13

Gaussian processes are the most commonly used statistical model for spatial and spatio-temporal processes that vary continuously. They are broadly applicable in the physical sciences and engineering and are also frequently used to approximate the output of complex computer models, deterministic or stochastic. We undertook research related to theory, computation, and applications of Gaussian processes as well as some work on estimating extremes of distributions for which a Gaussian process assumption might be inappropriate. Our theoretical contributions include the development of new classes of spatial-temporal covariance functions with desirable properties and new results showing that certain covariance models lead to predictions with undesirable properties. To understand how Gaussian process models behave when applied to deterministic computer models, we derived what we believe to be the first significant results on the large sample properties of estimators of parameters of Gaussian processes when the actual process is a simple deterministic function. Finally, we investigated some theoretical issues related to maxima of observations with varying upper bounds and found that, depending on the circumstances, standard large sample results for maxima may or may not hold. Our computational innovations include methods for analyzing large spatial datasets when observations fall on a partially observed grid and methods for estimating parameters of a Gaussian process model from observations taken by a polar-orbiting satellite. In our application of Gaussian process models to deterministic computer experiments, we carried out some matrix computations that would have been infeasible using even extended precision arithmetic by focusing on special cases in which all elements of the matrices under study are rational and using exact arithmetic. The applications we studied include total column ozone as measured from a polar-orbiting satellite, sea surface temperatures over the

Numerical modeling of Gaussian beam propagation and diffraction in inhomogeneous media based on the complex eikonal equation

Science.gov (United States)

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.

Propagation of rotational Risley-prism-array-based Gaussian beams in turbulent atmosphere

Science.gov (United States)

Chen, Feng; Ma, Haotong; Dong, Li; Ren, Ge; Qi, Bo; Tan, Yufeng

2018-03-01

Limited by the size and weight of prism and optical assembling, Rotational Risley-prism-array system is a simple but effective way to realize high power and superior beam quality of deflecting laser output. In this paper, the propagation of the rotational Risley-prism-array-based Gaussian beam array in atmospheric turbulence is studied in detail. An analytical expression for the average intensity distribution at the receiving plane is derived based on nonparaxial ray tracing method and extended Huygens-Fresnel principle. Power in the diffraction-limited bucket is chosen to evaluate beam quality. The effect of deviation angle, propagation distance and intensity of turbulence on beam quality is studied in detail by quantitative simulation. It reveals that with the propagation distance increasing, the intensity distribution gradually evolves from multiple-petal-like shape into the pattern that contains one main-lobe in the center with multiple side-lobes in weak turbulence. The beam quality of rotational Risley-prism-array-based Gaussian beam array with lower deviation angle is better than its counterpart with higher deviation angle when propagating in weak and medium turbulent (i.e. Cn2 beam quality of higher deviation angle arrays degrades faster as the intensity of turbulence gets stronger. In the case of propagating in strong turbulence, the long propagation distance (i.e. z > 10km ) and deviation angle have no influence on beam quality.

When non-Gaussian states are Gaussian: Generalization of nonseparability criterion for continuous variables

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

Gaussian Mixture Random Coefficient model based framework for SHM in structures with time-dependent dynamics under uncertainty

Science.gov (United States)

Avendaño-Valencia, Luis David; Fassois, Spilios D.

2017-12-01

The problem of vibration-based damage diagnosis in structures characterized by time-dependent dynamics under significant environmental and/or operational uncertainty is considered. A stochastic framework consisting of a Gaussian Mixture Random Coefficient model of the uncertain time-dependent dynamics under each structural health state, proper estimation methods, and Bayesian or minimum distance type decision making, is postulated. The Random Coefficient (RC) time-dependent stochastic model with coefficients following a multivariate Gaussian Mixture Model (GMM) allows for significant flexibility in uncertainty representation. Certain of the model parameters are estimated via a simple procedure which is founded on the related Multiple Model (MM) concept, while the GMM weights are explicitly estimated for optimizing damage diagnostic performance. The postulated framework is demonstrated via damage detection in a simple simulated model of a quarter-car active suspension with time-dependent dynamics and considerable uncertainty on the payload. Comparisons with a simpler Gaussian RC model based method are also presented, with the postulated framework shown to be capable of offering considerable improvement in diagnostic performance.

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).

An Efficient Identity-Based Proxy Blind Signature for Semioffline Services

Directory of Open Access Journals (Sweden)

Hongfei Zhu

2018-01-01

Full Text Available Fog computing extends the cloud computing to the network edge and allows deploying a new type of semioffline services, which can provide real-time transactions between two entities, while the central cloud server is offline and network edge devices are online. For an e-payment system and e-voting with such feature, proxy blind signature is a cornerstone to protect users’ privacy. However, the signature based on number theorem, such as hard mathematical problems on factoring problem, discrete logarithm problem, and bilinear pairings, cannot defeat quantum computers attack. Meanwhile, these schemes need to depend on complex public key infrastructure. Thus, we construct an identity-based proxy blind signature scheme based on number theorem research unit lattice, which can defeat quantum computers attack and does not need to depend on public key infrastructure. The security of the proposed scheme is dependent on Ring-Small Integer Solution problem over number theorem research unit lattice. The proposed scheme meets the properties of blind signature and proxy signature. Then we compare the proposed scheme with other existing proxy blind signature schemes; the result shows that the proposed scheme outperforms ZM scheme except in proxy signer’s signature size and can be more secure than TA scheme and MMHP scheme.

Superstatistical generalised Langevin equation: non-Gaussian viscoelastic anomalous diffusion

Science.gov (United States)

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.

Gaussian-windowed frame based method of moments formulation of surface-integral-equation for extended apertures

Energy Technology Data Exchange (ETDEWEB)

Shlivinski, A., E-mail: amirshli@ee.bgu.ac.il [Department of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva 84105 (Israel); Lomakin, V., E-mail: vlomakin@eng.ucsd.edu [Department of Electrical and Computer Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0407 (United States)

2016-03-01

Scattering or coupling of electromagnetic beam-field at a surface discontinuity separating two homogeneous or inhomogeneous media with different propagation characteristics is formulated using surface integral equation, which are solved by the Method of Moments with the aid of the Gabor-based Gaussian window frame set of basis and testing functions. The application of the Gaussian window frame provides (i) a mathematically exact and robust tool for spatial-spectral phase-space formulation and analysis of the problem; (ii) a system of linear equations in a transmission-line like form relating mode-like wave objects of one medium with mode-like wave objects of the second medium; (iii) furthermore, an appropriate setting of the frame parameters yields mode-like wave objects that blend plane wave properties (as if solving in the spectral domain) with Green's function properties (as if solving in the spatial domain); and (iv) a representation of the scattered field with Gaussian-beam propagators that may be used in many large (in terms of wavelengths) systems.

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, ...

Non-Gaussian Methods for Causal Structure Learning.

Science.gov (United States)

Shimizu, Shohei

2018-05-22

Causal structure learning is one of the most exciting new topics in the fields of machine learning and statistics. In many empirical sciences including prevention science, the causal mechanisms underlying various phenomena need to be studied. Nevertheless, in many cases, classical methods for causal structure learning are not capable of estimating the causal structure of variables. This is because it explicitly or implicitly assumes Gaussianity of data and typically utilizes only the covariance structure. In many applications, however, non-Gaussian data are often obtained, which means that more information may be contained in the data distribution than the covariance matrix is capable of containing. Thus, many new methods have recently been proposed for using the non-Gaussian structure of data and inferring the causal structure of variables. This paper introduces prevention scientists to such causal structure learning methods, particularly those based on the linear, non-Gaussian, acyclic model known as LiNGAM. These non-Gaussian data analysis tools can fully estimate the underlying causal structures of variables under assumptions even in the presence of unobserved common causes. This feature is in contrast to other approaches. A simulated example is also provided.

H∞ control for uncertain linear system over networks with Bernoulli data dropout and actuator saturation.

Science.gov (United States)

Yu, Jimin; Yang, Chenchen; Tang, Xiaoming; Wang, Ping

2018-03-01

This paper investigates the H ∞ control problems for uncertain linear system over networks with random communication data dropout and actuator saturation. The random data dropout process is modeled by a Bernoulli distributed white sequence with a known conditional probability distribution and the actuator saturation is confined in a convex hull by introducing a group of auxiliary matrices. By constructing a quadratic Lyapunov function, effective conditions for the state feedback-based H ∞ controller and the observer-based H ∞ controller are proposed in the form of non-convex matrix inequalities to take the random data dropout and actuator saturation into consideration simultaneously, and the problem of non-convex feasibility is solved by applying cone complementarity linearization (CCL) procedure. Finally, two simulation examples are given to demonstrate the effectiveness of the proposed new design techniques. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

A new fractional nonlocal model and its application in free vibration of Timoshenko and Euler-Bernoulli beams

Science.gov (United States)

Rahimi, Zaher; Sumelka, Wojciech; Yang, Xiao-Jun

2017-11-01

The application of fractional calculus in fractional models (FMs) makes them more flexible than integer models inasmuch they can conclude all of integer and non-integer operators. In other words FMs let us use more potential of mathematics to modeling physical phenomena due to the use of both integer and fractional operators to present a better modeling of problems, which makes them more flexible and powerful. In the present work, a new fractional nonlocal model has been proposed, which has a simple form and can be used in different problems due to the simple form of numerical solutions. Then the model has been used to govern equations of the motion of the Timoshenko beam theory (TBT) and Euler-Bernoulli beam theory (EBT). Next, free vibration of the Timoshenko and Euler-Bernoulli simply-supported (S-S) beam has been investigated. The Galerkin weighted residual method has been used to solve the non-linear governing equations.

GPU-based parallel algorithm for blind image restoration using midfrequency-based methods

Science.gov (United States)

Xie, Lang; Luo, Yi-han; Bao, Qi-liang

2013-08-01

GPU-based general-purpose computing is a new branch of modern parallel computing, so the study of parallel algorithms specially designed for GPU hardware architecture is of great significance. In order to solve the problem of high computational complexity and poor real-time performance in blind image restoration, the midfrequency-based algorithm for blind image restoration was analyzed and improved in this paper. Furthermore, a midfrequency-based filtering method is also used to restore the image hardly with any recursion or iteration. Combining the algorithm with data intensiveness, data parallel computing and GPU execution model of single instruction and multiple threads, a new parallel midfrequency-based algorithm for blind image restoration is proposed in this paper, which is suitable for stream computing of GPU. In this algorithm, the GPU is utilized to accelerate the estimation of class-G point spread functions and midfrequency-based filtering. Aiming at better management of the GPU threads, the threads in a grid are scheduled according to the decomposition of the filtering data in frequency domain after the optimization of data access and the communication between the host and the device. The kernel parallelism structure is determined by the decomposition of the filtering data to ensure the transmission rate to get around the memory bandwidth limitation. The results show that, with the new algorithm, the operational speed is significantly increased and the real-time performance of image restoration is effectively improved, especially for high-resolution images.

Blind topological measurement-based quantum computation.

Science.gov (United States)

Morimae, Tomoyuki; Fujii, Keisuke

2012-01-01

Blind quantum computation is a novel secure quantum-computing protocol that enables Alice, who does not have sufficient quantum technology at her disposal, to delegate her quantum computation to Bob, who has a fully fledged quantum computer, in such a way that Bob cannot learn anything about Alice's input, output and algorithm. A recent proof-of-principle experiment demonstrating blind quantum computation in an optical system has raised new challenges regarding the scalability of blind quantum computation in realistic noisy conditions. Here we show that fault-tolerant blind quantum computation is possible in a topologically protected manner using the Raussendorf-Harrington-Goyal scheme. The error threshold of our scheme is 4.3 × 10(-3), which is comparable to that (7.5 × 10(-3)) of non-blind topological quantum computation. As the error per gate of the order 10(-3) was already achieved in some experimental systems, our result implies that secure cloud quantum computation is within reach.

Quantum System Identification via L1-norm Minimization

Science.gov (United States)

2011-06-30

Bernoulli . For QPT there is no freedom for random independent selection of every element of the Φ matrix. However, as described in the paper, the rows of Φ...symmetry considerations associated with total angular momentum [28]. For example the Hamiltonian for the case of four quantum dots (A,B,C,D) takes...independently selected from a ran- dom distribution such as Gaussian or Bernoulli . Whereas in the Hamiltonian estimation formulation (Eq. (4) in the paper

Modeling web-based information seeking by users who are blind.

Science.gov (United States)

Brunsman-Johnson, Carissa; Narayanan, Sundaram; Shebilske, Wayne; Alakke, Ganesh; Narakesari, Shruti

2011-01-01

This article describes website information seeking strategies used by users who are blind and compares those with sighted users. It outlines how assistive technologies and website design can aid users who are blind while information seeking. People who are blind and sighted are tested using an assessment tool and performing several tasks on websites. The times and keystrokes are recorded for all tasks as well as commands used and spatial questioning. Participants who are blind used keyword-based search strategies as their primary tool to seek information. Sighted users also used keyword search techniques if they were unable to find the information using a visual scan of the home page of a website. A proposed model based on the present study for information seeking is described. Keywords are important in the strategies used by both groups of participants and providing these common and consistent keywords in locations that are accessible to the users may be useful for efficient information searching. The observations suggest that there may be a difference in how users search a website that is familiar compared to one that is unfamiliar. © 2011 Informa UK, Ltd.

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...

On the Shaker Simulation of Wind-Induced Non-Gaussian Random Vibration

Directory of Open Access Journals (Sweden)

Fei Xu

2016-01-01

Full Text Available Gaussian signal is produced by ordinary random vibration controllers to test the products in the laboratory, while the field data is usually non-Gaussian. Two methodologies are presented in this paper for shaker simulation of wind-induced non-Gaussian vibration. The first methodology synthesizes the non-Gaussian signal offline and replicates it on the shaker in the Time Waveform Replication (TWR mode. A new synthesis method is used to model the non-Gaussian signal as a Gaussian signal multiplied by an amplitude modulation function (AMF. A case study is presented to show that the synthesized non-Gaussian signal has the same power spectral density (PSD, probability density function (PDF, and loading cycle distribution (LCD as the field data. The second methodology derives a damage equivalent Gaussian signal from the non-Gaussian signal based on the fatigue damage spectrum (FDS and the extreme response spectrum (ERS and reproduces it on the shaker in the closed-loop frequency domain control mode. The PSD level and the duration time of the derived Gaussian signal can be manipulated for accelerated testing purpose. A case study is presented to show that the derived PSD matches the damage potential of the non-Gaussian environment for both fatigue and peak response.

Probing primordial non Gaussianity in the BOOMERanG CMB maps: an analysis based on analytical Minkowski functionals

International Nuclear Information System (INIS)

Migliaccio, M.; Natoli, P.; De Troia, G.; Hikage, C.; Komatsu, E.; Ade, P.A.R.; Bock, J.J.; Bond, J.R.; Borrill, J.; Boscaleri, A.; Contaldi, C.R.; Crill, B.P.; Bernardis, P. de; Gasperis, G. de; Oliveira-Costa, A. de; Di Stefano, G.; Hivon, E.; Kisner, T.S.; Jones, W.C.; Lange, A.E.

2009-01-01

Minkowski functionals are a powerful tool to constrain the Gaussianity of the Cosmic Microwave Background (CMB). In the limit of a weakly non Gaussian field, a perturbative approach can be derived [Hikage C., Komatsu E., and Matsubara T., 2006, ApJ, 653, 11] that is completely based on analytical formulae without requiring computationally intensive, dedicated Monte Carlo non Gaussian simulations of the CMB anisotropy. We apply this machinery to an intensity map derived from the 1998 and 2003 flights of BOOMERanG, analyzed here together for the first time. We set limits on the non-linear coupling parameter f NL as -1020 NL <390 at 95% CL, markedly improving the previous constraints set by [De Troia G. et al., 2007, ApJ, 670, L73] whose analysis was limited to the BOOMERanG 2003 dataset. These limits are the most stringent ever set among suborbital experiments.

Probing primordial non Gaussianity in the BOOMERanG CMB maps: an analysis based on analytical Minkowski functionals

Energy Technology Data Exchange (ETDEWEB)

Migliaccio, M.; Natoli, P.; De Troia, G. [Dipartimento di Fisica, Universita di Roma ' Tor Vergata' , Via della Ricerca Scientifica, 1 I-00133 Roma (Italy); Hikage, C. [School of Physics and Astronomy, Cardiff University, Cardiff, CF24 3AA (United Kingdom); Komatsu, E. [Texas Cosmology Center, University of Texas at Austin, 1 University Station, C1400, Austin, TX 78712 (United States); Ade, P.A.R. [School of Physics and Astronomy, Cardiff University, Cardiff, CF24 3AA (United Kingdom); Bock, J.J. [Jet Propulsion Laboratory, Pasadena, CA (United States); Bond, J.R. [Canadian Institute for Theoretical Astrophysics, University of Toronto, Toronto, Ontario (Canada); Borrill, J. [Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA (United States); Boscaleri, A. [IFAC-CNR, Firenze (Italy); Contaldi, C.R. [Theoretical Physics Group, Imperial College, London (United Kingdom); Crill, B.P. [Jet Propulsion Laboratory, Pasadena, CA (United States); Bernardis, P. de [Dipartimento di Fisica, Universita La Sapienza, Roma (Italy); Gasperis, G. de [Dipartimento di Fisica, Universita di Roma ' Tor Vergata' , Via della Ricerca Scientifica, 1 I-00133 Roma (Italy); Oliveira-Costa, A. de [Department of Physics, MIT, Cambridge, MA 02139 (United States); Di Stefano, G. [Istituto Nazionale di Geofisica e Vulcanologia, 00143 Rome (Italy); Hivon, E. [Institut d' Astrophysique, Paris (France); Kisner, T.S. [Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA (United States); Jones, W.C. [Department of Physics, Princeton University, Princeton, NJ 0854 (United States); Lange, A.E. [Observational Cosmology, California Institute of Technology, Pasadena, CA (United States)

2009-10-15

Minkowski functionals are a powerful tool to constrain the Gaussianity of the Cosmic Microwave Background (CMB). In the limit of a weakly non Gaussian field, a perturbative approach can be derived [Hikage C., Komatsu E., and Matsubara T., 2006, ApJ, 653, 11] that is completely based on analytical formulae without requiring computationally intensive, dedicated Monte Carlo non Gaussian simulations of the CMB anisotropy. We apply this machinery to an intensity map derived from the 1998 and 2003 flights of BOOMERanG, analyzed here together for the first time. We set limits on the non-linear coupling parameter f{sub NL} as -1020

A Gaussian process and derivative spectral-based algorithm for red blood cell segmentation

Science.gov (United States)

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.

Illuminance-based slat angle selection model for automated control of split blinds

Energy Technology Data Exchange (ETDEWEB)

Hu, Jia; Olbina, Svetlana [Rinker School of Building Construction, University of Florida, Gainesville, FL 32611-5703 (United States)

2011-03-15

Venetian blinds play an important role in controlling daylight in buildings. Automated blinds overcome some limitations of manual blinds; however, the existing automated systems mainly control the direct solar radiation and glare and cannot be used for controlling innovative blind systems such as split blinds. This research developed an Illuminance-based Slat Angle Selection (ISAS) model that predicts the optimum slat angles of split blinds to achieve the designed indoor illuminance. The model was constructed based on a series of multi-layer feed-forward artificial neural networks (ANNs). The illuminance values at the sensor points used to develop the ANNs were obtained by the software EnergyPlus trademark. The weather determinants (such as horizontal illuminance and sun angles) were used as the input variables for the ANNs. The illuminance level at a sensor point was the output variable for the ANNs. The ISAS model was validated by evaluating the errors in the calculation of the: 1) illuminance and 2) optimum slat angles. The validation results showed that the power of the ISAS model to predict illuminance was 94.7% while its power to calculate the optimum slat angles was 98.5%. For about 90% of time in the year, the illuminance percentage errors were less than 10%, and the percentage errors in calculating the optimum slat angles were less than 5%. This research offers a new approach for the automated control of split blinds and a guide for future research to utilize the adaptive nature of ANNs to develop a more practical and applicable blind control system. (author)

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

Novel Blind Recognition Algorithm of Frame Synchronization Words Based on Soft-Decision in Digital Communication Systems.

Science.gov (United States)

Qin, Jiangyi; Huang, Zhiping; Liu, Chunwu; Su, Shaojing; Zhou, Jing

2015-01-01

A novel blind recognition algorithm of frame synchronization words is proposed to recognize the frame synchronization words parameters in digital communication systems. In this paper, a blind recognition method of frame synchronization words based on the hard-decision is deduced in detail. And the standards of parameter recognition are given. Comparing with the blind recognition based on the hard-decision, utilizing the soft-decision can improve the accuracy of blind recognition. Therefore, combining with the characteristics of Quadrature Phase Shift Keying (QPSK) signal, an improved blind recognition algorithm based on the soft-decision is proposed. Meanwhile, the improved algorithm can be extended to other signal modulation forms. Then, the complete blind recognition steps of the hard-decision algorithm and the soft-decision algorithm are given in detail. Finally, the simulation results show that both the hard-decision algorithm and the soft-decision algorithm can recognize the parameters of frame synchronization words blindly. What's more, the improved algorithm can enhance the accuracy of blind recognition obviously.

QIM blind video watermarking scheme based on Wavelet transform and principal component analysis

Directory of Open Access Journals (Sweden)

Nisreen I. Yassin

2014-12-01

Full Text Available In this paper, a blind scheme for digital video watermarking is proposed. The security of the scheme is established by using one secret key in the retrieval of the watermark. Discrete Wavelet Transform (DWT is applied on each video frame decomposing it into a number of sub-bands. Maximum entropy blocks are selected and transformed using Principal Component Analysis (PCA. Quantization Index Modulation (QIM is used to quantize the maximum coefficient of the PCA blocks of each sub-band. Then, the watermark is embedded into the selected suitable quantizer values. The proposed scheme is tested using a number of video sequences. Experimental results show high imperceptibility. The computed average PSNR exceeds 45 dB. Finally, the scheme is applied on two medical videos. The proposed scheme shows high robustness against several attacks such as JPEG coding, Gaussian noise addition, histogram equalization, gamma correction, and contrast adjustment in both cases of regular videos and medical videos.

Nonlinear earthquake analysis of reinforced concrete frames with fiber and Bernoulli-Euler beam-column element.

Science.gov (United States)

Karaton, Muhammet

2014-01-01

A beam-column element based on the Euler-Bernoulli beam theory is researched for nonlinear dynamic analysis of reinforced concrete (RC) structural element. Stiffness matrix of this element is obtained by using rigidity method. A solution technique that included nonlinear dynamic substructure procedure is developed for dynamic analyses of RC frames. A predicted-corrected form of the Bossak-α method is applied for dynamic integration scheme. A comparison of experimental data of a RC column element with numerical results, obtained from proposed solution technique, is studied for verification the numerical solutions. Furthermore, nonlinear cyclic analysis results of a portal reinforced concrete frame are achieved for comparing the proposed solution technique with Fibre element, based on flexibility method. However, seismic damage analyses of an 8-story RC frame structure with soft-story are investigated for cases of lumped/distributed mass and load. Damage region, propagation, and intensities according to both approaches are researched.

Gaussian Process Interpolation for Uncertainty Estimation in Image Registration

Science.gov (United States)

Wachinger, Christian; Golland, Polina; Reuter, Martin; Wells, William

2014-01-01

Intensity-based image registration requires resampling images on a common grid to evaluate the similarity function. The uncertainty of interpolation varies across the image, depending on the location of resampled points relative to the base grid. We propose to perform Bayesian inference with Gaussian processes, where the covariance matrix of the Gaussian process posterior distribution estimates the uncertainty in interpolation. The Gaussian process replaces a single image with a distribution over images that we integrate into a generative model for registration. Marginalization over resampled images leads to a new similarity measure that includes the uncertainty of the interpolation. We demonstrate that our approach increases the registration accuracy and propose an efficient approximation scheme that enables seamless integration with existing registration methods. PMID:25333127

Continuum mechanics through the eighteenth and nineteenth centuries historical perspectives from John Bernoulli (1727) to Ernst Hellinger (1914)

CERN Document Server

Maugin, Gérard A

2014-01-01

Conceived as a series of more or less autonomous essays, the present book critically exposes the initial developments of continuum thermo-mechanics in a post Newtonian period extending from the creative works of the Bernoullis to the First World war, i.e., roughly during first the “Age of reason” and next the “Birth of the modern world”. The emphasis is rightly placed on the original contributions from the “Continental” scientists (the Bernoulli family, Euler, d’Alembert, Lagrange, Cauchy, Piola, Duhamel, Neumann, Clebsch, Kirchhoff, Helmholtz, Saint-Venant, Boussinesq, the Cosserat brothers, Caratheodory) in competition with their British peers (Green, Kelvin, Stokes, Maxwell, Rayleigh, Love,..). It underlines the main breakthroughs as well as the secondary ones. It highlights the role of scientists who left essential prints in this history of scientific ideas. The book shows how the formidable developments that blossomed in the twentieth century (and perused in a previous book of the author in...

The effect of image enhancement on the statistical analysis of functional neuroimages : Wavelet-based denoising and Gaussian smoothing

NARCIS (Netherlands)

Wink, AM; Roerdink, JBTM; Sonka, M; Fitzpatrick, JM

2003-01-01

The quality of statistical analyses of functional neuroimages is studied after applying various preprocessing methods. We present wavelet-based denoising as an alternative to Gaussian smoothing, the standard denoising method in statistical parametric mapping (SPM). The wavelet-based denoising

Vortices in Gaussian beams

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...

Road following for blindBike: an assistive bike navigation system for low vision persons

Science.gov (United States)

Grewe, Lynne; Overell, William

2017-05-01

Road Following is a critical component of blindBike, our assistive biking application for the visually impaired. This paper talks about the overall blindBike system and goals prominently featuring Road Following, which is the task of directing the user to follow the right side of the road. This work unlike what is commonly found for self-driving cars does not depend on lane line markings. 2D computer vision techniques are explored to solve the problem of Road Following. Statistical techniques including the use of Gaussian Mixture Models are employed. blindBike is developed as an Android Application and is running on a smartphone device. Other sensors including Gyroscope and GPS are utilized. Both Urban and suburban scenarios are tested and results are given. The success and challenges faced by blindBike's Road Following module are presented along with future avenues of work.

Three dimensional indoor positioning based on visible light with Gaussian mixture sigma-point particle filter technique

Science.gov (United States)

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.

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.

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

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.

Gene regulatory network inference by point-based Gaussian approximation filters incorporating the prior information.

Science.gov (United States)

Jia, Bin; Wang, Xiaodong

2013-12-17

: The extended Kalman filter (EKF) has been applied to inferring gene regulatory networks. However, it is well known that the EKF becomes less accurate when the system exhibits high nonlinearity. In addition, certain prior information about the gene regulatory network exists in practice, and no systematic approach has been developed to incorporate such prior information into the Kalman-type filter for inferring the structure of the gene regulatory network. In this paper, an inference framework based on point-based Gaussian approximation filters that can exploit the prior information is developed to solve the gene regulatory network inference problem. Different point-based Gaussian approximation filters, including the unscented Kalman filter (UKF), the third-degree cubature Kalman filter (CKF3), and the fifth-degree cubature Kalman filter (CKF5) are employed. Several types of network prior information, including the existing network structure information, sparsity assumption, and the range constraint of parameters, are considered, and the corresponding filters incorporating the prior information are developed. Experiments on a synthetic network of eight genes and the yeast protein synthesis network of five genes are carried out to demonstrate the performance of the proposed framework. The results show that the proposed methods provide more accurate inference results than existing methods, such as the EKF and the traditional UKF.

Novel Blind Recognition Algorithm of Frame Synchronization Words Based on Soft-Decision in Digital Communication Systems.

Directory of Open Access Journals (Sweden)

Jiangyi Qin

Full Text Available A novel blind recognition algorithm of frame synchronization words is proposed to recognize the frame synchronization words parameters in digital communication systems. In this paper, a blind recognition method of frame synchronization words based on the hard-decision is deduced in detail. And the standards of parameter recognition are given. Comparing with the blind recognition based on the hard-decision, utilizing the soft-decision can improve the accuracy of blind recognition. Therefore, combining with the characteristics of Quadrature Phase Shift Keying (QPSK signal, an improved blind recognition algorithm based on the soft-decision is proposed. Meanwhile, the improved algorithm can be extended to other signal modulation forms. Then, the complete blind recognition steps of the hard-decision algorithm and the soft-decision algorithm are given in detail. Finally, the simulation results show that both the hard-decision algorithm and the soft-decision algorithm can recognize the parameters of frame synchronization words blindly. What's more, the improved algorithm can enhance the accuracy of blind recognition obviously.

Detecting the presence of a magnetic field under Gaussian and non-Gaussian noise by adaptive measurement

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.

Coherence of the vortex Bessel-Gaussian beam in turbulent atmosphere

Science.gov (United States)

Lukin, Igor P.

2017-11-01

In this paper the theoretical research of coherent properties of the vortex Bessel-Gaussian optical beams propagating in turbulent atmosphere are developed. The approach to the analysis of this problem is based on the analytical solution of the equation for the transverse second-order mutual coherence function of a field of optical radiation. The behavior of integral scale of coherence degree of vortex Bessel-Gaussian optical beams depending on parameters of an optical beam and characteristics of turbulent atmosphere is particularly considered. It is shown that the integral scale of coherence degree of a vortex Bessel-Gaussian optical beam essentially depends on value of a topological charge of a vortex optical beam. With increase in a topological charge of a vortex Bessel-Gaussian optical beam the value of integral scale of coherence degree of a vortex Bessel-Gaussian optical beam are decreased.

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.

Combined failure acoustical diagnosis based on improved frequency domain blind deconvolution

International Nuclear Information System (INIS)

Pan, Nan; Wu, Xing; Chi, YiLin; Liu, Xiaoqin; Liu, Chang

2012-01-01

According to gear box combined failure extraction in complex sound field, an acoustic fault detection method based on improved frequency domain blind deconvolution was proposed. Follow the frequency-domain blind deconvolution flow, the morphological filtering was firstly used to extract modulation features embedded in the observed signals, then the CFPA algorithm was employed to do complex-domain blind separation, finally the J-Divergence of spectrum was employed as distance measure to resolve the permutation. Experiments using real machine sound signals was carried out. The result demonstrate this algorithm can be efficiently applied to gear box combined failure detection in practice.

Football fever: goal distributions and non-Gaussian statistics

Science.gov (United States)

Bittner, E.; Nußbaumer, A.; Janke, W.; Weigel, M.

2009-02-01

Analyzing football score data with statistical techniques, we investigate how the not purely random, but highly co-operative nature of the game is reflected in averaged properties such as the probability distributions of scored goals for the home and away teams. As it turns out, especially the tails of the distributions are not well described by the Poissonian or binomial model resulting from the assumption of uncorrelated random events. Instead, a good effective description of the data is provided by less basic distributions such as the negative binomial one or the probability densities of extreme value statistics. To understand this behavior from a microscopical point of view, however, no waiting time problem or extremal process need be invoked. Instead, modifying the Bernoulli random process underlying the Poissonian model to include a simple component of self-affirmation seems to describe the data surprisingly well and allows to understand the observed deviation from Gaussian statistics. The phenomenological distributions used before can be understood as special cases within this framework. We analyzed historical football score data from many leagues in Europe as well as from international tournaments, including data from all past tournaments of the “FIFA World Cup” series, and found the proposed models to be applicable rather universally. In particular, here we analyze the results of the German women’s premier football league and consider the two separate German men’s premier leagues in the East and West during the cold war times as well as the unified league after 1990 to see how scoring in football and the component of self-affirmation depend on cultural and political circumstances.

Spectral Properties of Chaotic Signals Generated by the Bernoulli Map

Directory of Open Access Journals (Sweden)

Rafael A. da Costa

2014-11-01

Full Text Available In the last decades, the use of chaotic signals as broadband carriers has been considered in Telecommunications. Despite the relevance of the frequency domain analysis in this field, there are few studies that are concerned with spectral properties of chaotic signals. Bearing this in mind, this paper aims the characterization of the power spectral density (PSD of chaotic orbits generated by Bernoulli maps. We obtain analytic expressions for autocorrelation sequence, PSD and essential bandwidth for chaotic orbits generated by this map as function of the family parameter and Lyapunov exponent. Moreover, we verify that analytical expressions match numerical results. We conclude that the power of the generated orbits is concentrated in low frequencies for all parameters values. Besides, it is possible to obtain chaotic narrowband signals.

Investigation of non-Gaussian effects in the Brazilian option market

Science.gov (United States)

Sosa-Correa, William O.; Ramos, Antônio M. T.; Vasconcelos, Giovani L.

2018-04-01

An empirical study of the Brazilian option market is presented in light of three option pricing models, namely the Black-Scholes model, the exponential model, and a model based on a power law distribution, the so-called q-Gaussian distribution or Tsallis distribution. It is found that the q-Gaussian model performs better than the Black-Scholes model in about one third of the option chains analyzed. But among these cases, the exponential model performs better than the q-Gaussian model in 75% of the time. The superiority of the exponential model over the q-Gaussian model is particularly impressive for options close to the expiration date, where its success rate rises above ninety percent.

A Quantum Proxy Weak Blind Signature Scheme Based on Controlled Quantum Teleportation

Science.gov (United States)

Cao, Hai-Jing; Yu, Yao-Feng; Song, Qin; Gao, Lan-Xiang

2015-04-01

Proxy blind signature is applied to the electronic paying system, electronic voting system, mobile agent system, security of internet, etc. A quantum proxy weak blind signature scheme is proposed in this paper. It is based on controlled quantum teleportation. Five-qubit entangled state functions as quantum channel. The scheme uses the physical characteristics of quantum mechanics to implement message blinding, so it could guarantee not only the unconditional security of the scheme but also the anonymity of the messages owner.

A novel multitarget model of radiation-induced cell killing based on the Gaussian distribution.

Science.gov (United States)

Zhao, Lei; Mi, Dong; Sun, Yeqing

2017-05-07

The multitarget version of the traditional target theory based on the Poisson distribution is still used to describe the dose-survival curves of cells after ionizing radiation in radiobiology and radiotherapy. However, noting that the usual ionizing radiation damage is the result of two sequential stochastic processes, the probability distribution of the damage number per cell should follow a compound Poisson distribution, like e.g. Neyman's distribution of type A (N. A.). In consideration of that the Gaussian distribution can be considered as the approximation of the N. A. in the case of high flux, a multitarget model based on the Gaussian distribution is proposed to describe the cell inactivation effects in low linear energy transfer (LET) radiation with high dose-rate. Theoretical analysis and experimental data fitting indicate that the present theory is superior to the traditional multitarget model and similar to the Linear - Quadratic (LQ) model in describing the biological effects of low-LET radiation with high dose-rate, and the parameter ratio in the present model can be used as an alternative indicator to reflect the radiation damage and radiosensitivity of the cells. Copyright © 2017 Elsevier Ltd. All rights reserved.

Quantum steering of multimode Gaussian states by Gaussian measurements: monogamy relations and the Peres conjecture

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)

Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Gaussian Processes Mixture

Science.gov (United States)

Li, Lingling; Wang, Pengchong; Chao, Kuei-Hsiang; Zhou, Yatong; Xie, Yang

2016-01-01

The remaining useful life (RUL) prediction of Lithium-ion batteries is closely related to the capacity degeneration trajectories. Due to the self-charging and the capacity regeneration, the trajectories have the property of multimodality. Traditional prediction models such as the support vector machines (SVM) or the Gaussian Process regression (GPR) cannot accurately characterize this multimodality. This paper proposes a novel RUL prediction method based on the Gaussian Process Mixture (GPM). It can process multimodality by fitting different segments of trajectories with different GPR models separately, such that the tiny differences among these segments can be revealed. The method is demonstrated to be effective for prediction by the excellent predictive result of the experiments on the two commercial and chargeable Type 1850 Lithium-ion batteries, provided by NASA. The performance comparison among the models illustrates that the GPM is more accurate than the SVM and the GPR. In addition, GPM can yield the predictive confidence interval, which makes the prediction more reliable than that of traditional models. PMID:27632176

Accuracy of dynamical-decoupling-based spectroscopy of Gaussian noise

Science.gov (United States)

Szańkowski, Piotr; Cywiński, Łukasz

2018-03-01

The fundamental assumption of dynamical-decoupling-based noise spectroscopy is that the coherence decay rate of qubit (or qubits) driven with a sequence of many pulses, is well approximated by the environmental noise spectrum spanned on frequency comb defined by the sequence. Here we investigate the precise conditions under which this commonly used spectroscopic approach is quantitatively correct. To this end we focus on two representative examples of spectral densities: the long-tailed Lorentzian, and finite-ranged Gaussian—both expected to be encountered when using the qubit for nanoscale nuclear resonance imaging. We have found that, in contrast to Lorentz spectrum, for which the corrections to the standard spectroscopic formulas can easily be made negligible, the spectra with finite range are more challenging to reconstruct accurately. For Gaussian line shape of environmental spectral density, direct application of the standard dynamical-decoupling-based spectroscopy leads to erroneous attribution of long-tail behavior to the reconstructed spectrum. Fortunately, artifacts such as this, can be completely avoided with the simple extension to standard reconstruction method.

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...

A Smart Infrared Microcontroller-Based Blind Guidance System

Directory of Open Access Journals (Sweden)

Amjed S. Al-Fahoum

2013-01-01

Full Text Available Blindness is a state of lacking the visual perception due to physiological or neurological factors. The partial blindness represents the lack of integration in the growth of the optic nerve or visual centre of the eye, and total blindness is the full absence of the visual light perception. In this work, a simple, cheap, friendly user, smart blind guidance system is designed and implemented to improve the mobility of both blind and visually impaired people in a specific area. The proposed work includes a wearable equipment consists of head hat and mini hand stick to help the blind person to navigate alone safely and to avoid any obstacles that may be encountered, whether fixed or mobile, to prevent any possible accident. The main component of this system is the infrared sensor which is used to scan a predetermined area around blind by emitting-reflecting waves. The reflected signals received from the barrier objects are used as inputs to PIC microcontroller. The microcontroller is then used to determine the direction and distance of the objects around the blind. It also controls the peripheral components that alert the user about obstacle's shape, material, and direction. The implemented system is cheap, fast, and easy to use and an innovative affordable solution to blind and visually impaired people in third world countries.

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....

Blind Quantum Signature with Blind Quantum Computation

Science.gov (United States)

Li, Wei; Shi, Ronghua; Guo, Ying

2017-04-01

Blind quantum computation allows a client without quantum abilities to interact with a quantum server to perform a unconditional secure computing protocol, while protecting client's privacy. Motivated by confidentiality of blind quantum computation, a blind quantum signature scheme is designed with laconic structure. Different from the traditional signature schemes, the signing and verifying operations are performed through measurement-based quantum computation. Inputs of blind quantum computation are securely controlled with multi-qubit entangled states. The unique signature of the transmitted message is generated by the signer without leaking information in imperfect channels. Whereas, the receiver can verify the validity of the signature using the quantum matching algorithm. The security is guaranteed by entanglement of quantum system for blind quantum computation. It provides a potential practical application for e-commerce in the cloud computing and first-generation quantum computation.

Two-Phase Iteration for Value Function Approximation and Hyperparameter Optimization in Gaussian-Kernel-Based Adaptive Critic Design

Directory of Open Access Journals (Sweden)

Xin Chen

2015-01-01

Full Text Available Adaptive Dynamic Programming (ADP with critic-actor architecture is an effective way to perform online learning control. To avoid the subjectivity in the design of a neural network that serves as a critic network, kernel-based adaptive critic design (ACD was developed recently. There are two essential issues for a static kernel-based model: how to determine proper hyperparameters in advance and how to select right samples to describe the value function. They all rely on the assessment of sample values. Based on the theoretical analysis, this paper presents a two-phase simultaneous learning method for a Gaussian-kernel-based critic network. It is able to estimate the values of samples without infinitively revisiting them. And the hyperparameters of the kernel model are optimized simultaneously. Based on the estimated sample values, the sample set can be refined by adding alternatives or deleting redundances. Combining this critic design with actor network, we present a Gaussian-kernel-based Adaptive Dynamic Programming (GK-ADP approach. Simulations are used to verify its feasibility, particularly the necessity of two-phase learning, the convergence characteristics, and the improvement of the system performance by using a varying sample set.

Entanglement and Wigner Function Negativity of Multimode Non-Gaussian States

Science.gov (United States)

Walschaers, Mattia; Fabre, Claude; Parigi, Valentina; Treps, Nicolas

2017-11-01

Non-Gaussian operations are essential to exploit the quantum advantages in optical continuous variable quantum information protocols. We focus on mode-selective photon addition and subtraction as experimentally promising processes to create multimode non-Gaussian states. Our approach is based on correlation functions, as is common in quantum statistical mechanics and condensed matter physics, mixed with quantum optics tools. We formulate an analytical expression of the Wigner function after the subtraction or addition of a single photon, for arbitrarily many modes. It is used to demonstrate entanglement properties specific to non-Gaussian states and also leads to a practical and elegant condition for Wigner function negativity. Finally, we analyze the potential of photon addition and subtraction for an experimentally generated multimode Gaussian state.

Wavelength interrogation of fiber Bragg grating sensors based on crossed optical Gaussian filters.

Science.gov (United States)

Cheng, Rui; Xia, Li; Zhou, Jiaao; Liu, Deming

2015-04-15

Conventional intensity-modulated measurements require to be operated in linear range of filter or interferometric response to ensure a linear detection. Here, we present a wavelength interrogation system for fiber Bragg grating sensors where the linear transition is achieved with crossed Gaussian transmissions. This unique filtering characteristic makes the responses of the two branch detections follow Gaussian functions with the same parameters except for a delay. The substraction of these two delayed Gaussian responses (in dB) ultimately leads to a linear behavior, which is exploited for the sensor wavelength determination. Beside its flexibility and inherently power insensitivity, the proposal also shows a potential of a much wider operational range. Interrogation of a strain-tuned grating was accomplished, with a wide sensitivity tuning range from 2.56 to 8.7 dB/nm achieved.

Group Targets Tracking Using Multiple Models GGIW-CPHD Based on Best-Fitting Gaussian Approximation and Strong Tracking Filter

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.

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.

Biomechanics of hair cell kinocilia: experimental measurement of kinocilium shaft stiffness and base rotational stiffness with Euler–Bernoulli and Timoshenko beam analysis

Science.gov (United States)

Spoon, Corrie; Grant, Wally

2011-01-01

Vestibular hair cell bundles in the inner ear contain a single kinocilium composed of a 9+2 microtubule structure. Kinocilia play a crucial role in transmitting movement of the overlying mass, otoconial membrane or cupula to the mechanotransducing portion of the hair cell bundle. Little is known regarding the mechanical deformation properties of the kinocilium. Using a force-deflection technique, we measured two important mechanical properties of kinocilia in the utricle of a turtle, Trachemys (Pseudemys) scripta elegans. First, we measured the stiffness of kinocilia with different heights. These kinocilia were assumed to be homogenous cylindrical rods and were modeled as both isotropic Euler–Bernoulli beams and transversely isotropic Timoshenko beams. Two mechanical properties of the kinocilia were derived from the beam analysis: flexural rigidity (EI) and shear rigidity (kGA). The Timoshenko model produced a better fit to the experimental data, predicting EI=10,400 pN μm2 and kGA=247 pN. Assuming a homogenous rod, the shear modulus (G=1.9 kPa) was four orders of magnitude less than Young's modulus (E=14.1 MPa), indicating that significant shear deformation occurs within deflected kinocilia. When analyzed as an Euler–Bernoulli beam, which neglects translational shear, EI increased linearly with kinocilium height, giving underestimates of EI for shorter kinocilia. Second, we measured the rotational stiffness of the kinocilium insertion (κ) into the hair cell's apical surface. Following BAPTA treatment to break the kinocilial links, the kinocilia remained upright, and κ was measured as 177±47 pN μm rad–1. The mechanical parameters we quantified are important for understanding how forces arising from head movement are transduced and encoded by hair cells. PMID:21307074

Dynamic modelling and control of a rotating Euler-Bernoulli beam

Science.gov (United States)

Yang, J. B.; Jiang, L. J.; Chen, D. CH.

2004-07-01

Flexible motion of a uniform Euler-Bernoulli beam attached to a rotating rigid hub is investigated. Fully coupled non-linear integro-differential equations, describing axial, transverse and rotational motions of the beam, are derived by using the extended Hamilton's principle. The centrifugal stiffening effect is included in the derivation. A finite-dimensional model, including couplings of axial and transverse vibrations, and of elastic deformations and rigid motions, is obtained by the finite element method. By neglecting the axial motion, a simplified modelling, suitable for studying the transverse vibration and control of a beam with large angle and high-speed rotation, is presented. And suppressions of transverse vibrations of a rotating beam are simulated with the model by combining positive position feedback and momentum exchange feedback control laws. It is indicated that an improved performance for vibration control can be achieved with the method.

Enabling People Who Are Blind to Experience Science Inquiry Learning through Sound-Based Mediation

Science.gov (United States)

Levy, S. T.; Lahav, O.

2012-01-01

This paper addresses a central need among people who are blind, access to inquiry-based science learning materials, which are addressed by few other learning environments that use assistive technologies. In this study, we investigated ways in which learning environments based on sound mediation can support science learning by blind people. We used…

Blind Separation of Nonstationary Sources Based on Spatial Time-Frequency Distributions

Directory of Open Access Journals (Sweden)

Zhang Yimin

2006-01-01

Full Text Available Blind source separation (BSS based on spatial time-frequency distributions (STFDs provides improved performance over blind source separation methods based on second-order statistics, when dealing with signals that are localized in the time-frequency (t-f domain. In this paper, we propose the use of STFD matrices for both whitening and recovery of the mixing matrix, which are two stages commonly required in many BSS methods, to provide robust BSS performance to noise. In addition, a simple method is proposed to select the auto- and cross-term regions of time-frequency distribution (TFD. To further improve the BSS performance, t-f grouping techniques are introduced to reduce the number of signals under consideration, and to allow the receiver array to separate more sources than the number of array sensors, provided that the sources have disjoint t-f signatures. With the use of one or more techniques proposed in this paper, improved performance of blind separation of nonstationary signals can be achieved.

Diffusion weighted imaging in patients with rectal cancer: Comparison between Gaussian and non-Gaussian models.

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.

Learning non-Gaussian Time Series using the Box-Cox Gaussian Process

OpenAIRE

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...

Wind energy: an application of Bernoulli's theorem generalized to isentropic flow of ideal gases

International Nuclear Information System (INIS)

De Luca, R; Desideri, P

2013-01-01

By considering the extension of Bernoulli's theorem to the case of the isentropic flow of ideal gases we conceive a small-scale wind–energy system able to work in the presence of low wind velocities in any direction. The flow of air inside a hyperbolically shaped pipe is studied using elementary physics concepts. The results obtained show that wind velocity in the system increases for decreasing cross-sectional areas, allowing a lower cut-in wind speed and an increase in the annual energy production of the device. (paper)

Weighted Feature Gaussian Kernel SVM for Emotion Recognition.

Science.gov (United States)

Wei, Wei; Jia, Qingxuan

2016-01-01

Emotion recognition with weighted feature based on facial expression is a challenging research topic and has attracted great attention in the past few years. This paper presents a novel method, utilizing subregion recognition rate to weight kernel function. First, we divide the facial expression image into some uniform subregions and calculate corresponding recognition rate and weight. Then, we get a weighted feature Gaussian kernel function and construct a classifier based on Support Vector Machine (SVM). At last, the experimental results suggest that the approach based on weighted feature Gaussian kernel function has good performance on the correct rate in emotion recognition. The experiments on the extended Cohn-Kanade (CK+) dataset show that our method has achieved encouraging recognition results compared to the state-of-the-art methods.

Comparison of frequency-distance relationship and Gaussian-diffusion-based methods of compensation for distance-dependent spatial resolution in SPECT imaging

International Nuclear Information System (INIS)

Kohli, Vandana; King, Micgael A.; Glick, Stephen J.; Pan, Tin-Su

1998-01-01

The goal of this investigation was to compare resolution recovery versus noise level of two methods for compensation of distance-dependent resolution (DDR) in SPECT imaging. The two methods of compensation were restoration filtering based on the frequency-distance relationship (FDR) prior to iterative reconstruction, and modelling DDR in the projector/backprojector pair employed in iterative reconstruction. FDR restoration filtering was computationally faster than modelling the detector response in iterative reconstruction. Using Gaussian diffusion to model the detector response in iterative reconstruction sped up the process by a factor of 2.5 over frequency domain filtering in the projector/backprojector pair. Gaussian diffusion modelling resulted in a better resolution versus noise tradeoff than either FDR restoration filtering or solely modelling attenuation in the projector/backprojector pair of iterative reconstruction. For the pixel size investigated herein (0.317 cm), accounting for DDR in the projector/backprojector pair by Gaussian diffusion, or by applying a blurring function based on the distance from the face of the collimator at each distance, resulted in very similar resolution recovery and slice noise level. (author)

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

Calculations of Sobol indices for the Gaussian process metamodel

Energy Technology Data Exchange (ETDEWEB)

Marrel, Amandine [CEA, DEN, DTN/SMTM/LMTE, F-13108 Saint Paul lez Durance (France)], E-mail: amandine.marrel@cea.fr; Iooss, Bertrand [CEA, DEN, DER/SESI/LCFR, F-13108 Saint Paul lez Durance (France); Laurent, Beatrice [Institut de Mathematiques, Universite de Toulouse (UMR 5219) (France); Roustant, Olivier [Ecole des Mines de Saint-Etienne (France)

2009-03-15

Global sensitivity analysis of complex numerical models can be performed by calculating variance-based importance measures of the input variables, such as the Sobol indices. However, these techniques, requiring a large number of model evaluations, are often unacceptable for time expensive computer codes. A well-known and widely used decision consists in replacing the computer code by a metamodel, predicting the model responses with a negligible computation time and rending straightforward the estimation of Sobol indices. In this paper, we discuss about the Gaussian process model which gives analytical expressions of Sobol indices. Two approaches are studied to compute the Sobol indices: the first based on the predictor of the Gaussian process model and the second based on the global stochastic process model. Comparisons between the two estimates, made on analytical examples, show the superiority of the second approach in terms of convergence and robustness. Moreover, the second approach allows to integrate the modeling error of the Gaussian process model by directly giving some confidence intervals on the Sobol indices. These techniques are finally applied to a real case of hydrogeological modeling.

Calculations of Sobol indices for the Gaussian process metamodel

International Nuclear Information System (INIS)

Marrel, Amandine; Iooss, Bertrand; Laurent, Beatrice; Roustant, Olivier

2009-01-01

Global sensitivity analysis of complex numerical models can be performed by calculating variance-based importance measures of the input variables, such as the Sobol indices. However, these techniques, requiring a large number of model evaluations, are often unacceptable for time expensive computer codes. A well-known and widely used decision consists in replacing the computer code by a metamodel, predicting the model responses with a negligible computation time and rending straightforward the estimation of Sobol indices. In this paper, we discuss about the Gaussian process model which gives analytical expressions of Sobol indices. Two approaches are studied to compute the Sobol indices: the first based on the predictor of the Gaussian process model and the second based on the global stochastic process model. Comparisons between the two estimates, made on analytical examples, show the superiority of the second approach in terms of convergence and robustness. Moreover, the second approach allows to integrate the modeling error of the Gaussian process model by directly giving some confidence intervals on the Sobol indices. These techniques are finally applied to a real case of hydrogeological modeling

Micropolar curved rods. 2-D, high order, Timoshenko’s and Euler-Bernoulli models

Directory of Open Access Journals (Sweden)

Zozulya V.V.

2017-01-01

Full Text Available New models for micropolar plane curved rods have been developed. 2-D theory is developed from general 2-D equations of linear micropolar elasticity using a special curvilinear system of coordinates related to the middle line of the rod and special hypothesis based on assumptions that take into account the fact that the rod is thin.High order theory is based on the expansion of the equations of the theory of elasticity into Fourier series in terms of Legendre polynomials. First stress and strain tensors,vectors of displacements and rotation and body force shave been expanded into Fourier series in terms of Legendre polynomials with respect to a thickness coordinate.Thereby all equations of elasticity including Hooke’s law have been transformed to the corresponding equations for Fourier coefficients. Then in the same way as in the theory of elasticity, system of differential equations in term of displacements and boundary conditions for Fourier coefficients have been obtained. The Timoshenko’s and Euler-Bernoulli theories are based on the classical hypothesis and 2-D equations of linear micropolar elasticity in a special curvilinear system. The obtained equations can be used to calculate stress-strain and to model thin walled structures in macro, micro and nano scale when taking in to account micropolar couple stress and rotation effects.

Forecasting of foreign exchange rates of Taiwan’s major trading partners by novel nonlinear Grey Bernoulli model NGBM(1, 1)

Science.gov (United States)

Chen, Chun-I.; Chen, Hong Long; Chen, Shuo-Pei

2008-08-01

The traditional Grey Model is easy to understand and simple to calculate, with satisfactory accuracy, but it is also lack of flexibility to adjust the model to acquire higher forecasting precision. This research studies feasibility and effectiveness of a novel Grey model together with the concept of the Bernoulli differential equation in ordinary differential equation. In this research, the author names this newly proposed model as Nonlinear Grey Bernoulli Model (NGBM). The NGBM is nonlinear differential equation with power index n. By controlling n, the curvature of the solution curve could be adjusted to fit the result of one time accumulated generating operation (1-AGO) of raw data. One extreme case from Grey system textbook is studied by NGBM, and two published articles are chosen for practical tests of NGBM. The results prove the novel NGBM is feasible and efficient. Finally, NGBM is used to forecast 2005 foreign exchange rates of twelve Taiwan major trading partners, including Taiwan.

The stochastic model for ternary and quaternary alloys: Application of the Bernoulli relation to the phonon spectra of mixed crystals

Energy Technology Data Exchange (ETDEWEB)

Marchewka, M., E-mail: marmi@ur.edu.pl; Woźny, M.; Polit, J.; Sheregii, E. M. [Faculty of Mathematics and Natural Sciences, Centre for Microelectronics and Nanotechnology, University of Rzeszów, Pigonia 1, 35-959 Rzeszów (Poland); Kisiel, A. [Institute of Physics, Jagiellonian University, Reymonta 4, Kraków 30-059 (Poland); Robouch, B. V.; Marcelli, A. [INFN-Laboratori Nazionali di Frascati, Via E. Fermi 40, I-00044 Frascati (Italy)

2014-03-21

To understand and interpret the experimental data on the phonon spectra of the solid solutions, it is necessary to describe mathematically the non-regular distribution of atoms in their lattices. It appears that such description is possible in case of the strongly stochastically homogenous distribution which requires a great number of atoms and very carefully mixed alloys. These conditions are generally fulfilled in case of high quality homogenous semiconductor solid solutions of the III–V and II–VI semiconductor compounds. In this case, we can use the Bernoulli relation describing probability of the occurrence of one n equivalent event which can be applied, to the probability of finding one from n configurations in the solid solution lattice. The results described in this paper for ternary HgCdTe and GaAsP as well as quaternary ZnCdHgTe can provide an affirmative answer to the question: whether stochastic geometry, e.g., the Bernoulli relation, is enough to describe the observed phonon spectra.

The stochastic model for ternary and quaternary alloys: Application of the Bernoulli relation to the phonon spectra of mixed crystals

International Nuclear Information System (INIS)

Marchewka, M.; Woźny, M.; Polit, J.; Sheregii, E. M.; Kisiel, A.; Robouch, B. V.; Marcelli, A.

2014-01-01

To understand and interpret the experimental data on the phonon spectra of the solid solutions, it is necessary to describe mathematically the non-regular distribution of atoms in their lattices. It appears that such description is possible in case of the strongly stochastically homogenous distribution which requires a great number of atoms and very carefully mixed alloys. These conditions are generally fulfilled in case of high quality homogenous semiconductor solid solutions of the III–V and II–VI semiconductor compounds. In this case, we can use the Bernoulli relation describing probability of the occurrence of one n equivalent event which can be applied, to the probability of finding one from n configurations in the solid solution lattice. The results described in this paper for ternary HgCdTe and GaAsP as well as quaternary ZnCdHgTe can provide an affirmative answer to the question: whether stochastic geometry, e.g., the Bernoulli relation, is enough to describe the observed phonon spectra

The stochastic model for ternary and quaternary alloys: Application of the Bernoulli relation to the phonon spectra of mixed crystals

Science.gov (United States)

Marchewka, M.; Woźny, M.; Polit, J.; Kisiel, A.; Robouch, B. V.; Marcelli, A.; Sheregii, E. M.

2014-03-01

To understand and interpret the experimental data on the phonon spectra of the solid solutions, it is necessary to describe mathematically the non-regular distribution of atoms in their lattices. It appears that such description is possible in case of the strongly stochastically homogenous distribution which requires a great number of atoms and very carefully mixed alloys. These conditions are generally fulfilled in case of high quality homogenous semiconductor solid solutions of the III-V and II-VI semiconductor compounds. In this case, we can use the Bernoulli relation describing probability of the occurrence of one n equivalent event which can be applied, to the probability of finding one from n configurations in the solid solution lattice. The results described in this paper for ternary HgCdTe and GaAsP as well as quaternary ZnCdHgTe can provide an affirmative answer to the question: whether stochastic geometry, e.g., the Bernoulli relation, is enough to describe the observed phonon spectra.

Scaled unscented transform Gaussian sum filter: Theory and application

KAUST Repository

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

Off-Axis Gaussian Beams with Random Displacement in Atmospheric Turbulence

Directory of Open Access Journals (Sweden)

Yahya Baykal

2006-10-01

Full Text Available Our recent work in which we study the propagation of the general Hermite-sinusoidal-Gaussian laser beams in wireless broadband access telecommunication systems is elaborated in this paper to cover the special case of an off-axis Gaussian beam. We mainly investigate the propagation characteristics in atmospheric turbulence of an off-axis Gaussian beam possessing Gaussian distributed random displacement parameters. Our interest is to search for different types of laser beams that will improve the performance of a wireless broadband access system when atmospheric turbulence is considered. Our formulation is based on the basic solution of the second order mutual coherence function evaluated at the receiver plane. For fixed turbulence strength, the coherence length calculated at the receiver plane is found to decrease as the variance of the random displacement is increased. It is shown that as the turbulence becomes stronger, coherence lengths due to off-axis Gaussian beams tend to approach the same value, irrespective of the variance of the random displacement. As expected, the beam spreading is found to be pronounced for larger variance of displacement parameter. Average intensity profiles when atmospheric turbulence is present are plotted for different values of the variance of the random displacement parameter of the off-axis Gaussian beam.

Prediction of Geological Subsurfaces Based on Gaussian Random Field Models

Energy Technology Data Exchange (ETDEWEB)

Abrahamsen, Petter

1997-12-31

During the sixties, random functions became practical tools for predicting ore reserves with associated precision measures in the mining industry. This was the start of the geostatistical methods called kriging. These methods are used, for example, in petroleum exploration. This thesis reviews the possibilities for using Gaussian random functions in modelling of geological subsurfaces. It develops methods for including many sources of information and observations for precise prediction of the depth of geological subsurfaces. The simple properties of Gaussian distributions make it possible to calculate optimal predictors in the mean square sense. This is done in a discussion of kriging predictors. These predictors are then extended to deal with several subsurfaces simultaneously. It is shown how additional velocity observations can be used to improve predictions. The use of gradient data and even higher order derivatives are also considered and gradient data are used in an example. 130 refs., 44 figs., 12 tabs.

Sign reversals of the output autocorrelation function for the stochastic Bernoulli-Verhulst equation

Energy Technology Data Exchange (ETDEWEB)

Lumi, N., E-mail: Neeme.Lumi@tlu.ee; Mankin, R., E-mail: Romi.Mankin@tlu.ee [Institute of Mathematics and Natural Sciences, Tallinn University, 29 Narva Road, 10120 Tallinn (Estonia)

2015-10-28

We consider a stochastic Bernoulli-Verhulst equation as a model for population growth processes. The effect of fluctuating environment on the carrying capacity of a population is modeled as colored dichotomous noise. Relying on the composite master equation an explicit expression for the stationary autocorrelation function (ACF) of population sizes is found. On the basis of this expression a nonmonotonic decay of the ACF by increasing lag-time is shown. Moreover, in a certain regime of the noise parameters the ACF demonstrates anticorrelation as well as related sign reversals at some values of the lag-time. The conditions for the appearance of this highly unexpected effect are also discussed.

On the number of polynomial solutions of Bernoulli and Abel polynomial differential equations

Science.gov (United States)

Cima, A.; Gasull, A.; Mañosas, F.

2017-12-01

In this paper we determine the maximum number of polynomial solutions of Bernoulli differential equations and of some integrable polynomial Abel differential equations. As far as we know, the tools used to prove our results have not been utilized before for studying this type of questions. We show that the addressed problems can be reduced to know the number of polynomial solutions of a related polynomial equation of arbitrary degree. Then we approach to these equations either applying several tools developed to study extended Fermat problems for polynomial equations, or reducing the question to the computation of the genus of some associated planar algebraic curves.

Mean intensity of the fundamental Bessel-Gaussian beam in turbulent atmosphere

Science.gov (United States)

Lukin, Igor P.

2017-11-01

In the given article mean intensity of a fundamental Bessel-Gaussian optical beam in turbulent atmosphere is studied. The problem analysis is based on the solution of the equation for the transverse second-order mutual coherence function of a fundamental Bessel-Gaussian beam of optical radiation. Distributions of mean intensity of a fundamental Bessel- Gaussian beam optical beam in longitudinal and transverse to a direction of propagation of optical radiation are investigated in detail. Influence of atmospheric turbulence on change of radius of the central part of a Bessel optical beam is estimated. Values of parameters at which it is possible to generate in turbulent atmosphere a nondiffracting pseudo-Bessel optical beam by means of a fundamental Bessel-Gaussian optical beam are established.

Response moments of dynamic systems under non-Gaussian random excitation by the equivalent non-Gaussian excitation method

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)

Instance-optimality in probability with an KAUST Repository

DeVore, Ronald; Petrova, Guergana; Wojtaszczyk, Przemyslaw

2009-01-01

measurements in compressed sensing, Found. Comput. Math., in press] who showed this property when η is a normalized Gaussian random variable. We extend this property to more general random variables, including the particular case where η is the Bernoulli random

An Identity-Based Anti-Quantum Privacy-Preserving Blind Authentication in Wireless Sensor Networks.

Science.gov (United States)

Zhu, Hongfei; Tan, Yu-An; Zhu, Liehuang; Wang, Xianmin; Zhang, Quanxin; Li, Yuanzhang

2018-05-22

With the development of wireless sensor networks, IoT devices are crucial for the Smart City; these devices change people's lives such as e-payment and e-voting systems. However, in these two systems, the state-of-art authentication protocols based on traditional number theory cannot defeat a quantum computer attack. In order to protect user privacy and guarantee trustworthy of big data, we propose a new identity-based blind signature scheme based on number theorem research unit lattice, this scheme mainly uses a rejection sampling theorem instead of constructing a trapdoor. Meanwhile, this scheme does not depend on complex public key infrastructure and can resist quantum computer attack. Then we design an e-payment protocol using the proposed scheme. Furthermore, we prove our scheme is secure in the random oracle, and satisfies confidentiality, integrity, and non-repudiation. Finally, we demonstrate that the proposed scheme outperforms the other traditional existing identity-based blind signature schemes in signing speed and verification speed, outperforms the other lattice-based blind signature in signing speed, verification speed, and signing secret key size.

An Identity-Based Anti-Quantum Privacy-Preserving Blind Authentication in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Hongfei Zhu

2018-05-01

Full Text Available With the development of wireless sensor networks, IoT devices are crucial for the Smart City; these devices change people’s lives such as e-payment and e-voting systems. However, in these two systems, the state-of-art authentication protocols based on traditional number theory cannot defeat a quantum computer attack. In order to protect user privacy and guarantee trustworthy of big data, we propose a new identity-based blind signature scheme based on number theorem research unit lattice, this scheme mainly uses a rejection sampling theorem instead of constructing a trapdoor. Meanwhile, this scheme does not depend on complex public key infrastructure and can resist quantum computer attack. Then we design an e-payment protocol using the proposed scheme. Furthermore, we prove our scheme is secure in the random oracle, and satisfies confidentiality, integrity, and non-repudiation. Finally, we demonstrate that the proposed scheme outperforms the other traditional existing identity-based blind signature schemes in signing speed and verification speed, outperforms the other lattice-based blind signature in signing speed, verification speed, and signing secret key size.

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....

Coherence degree of the fundamental Bessel-Gaussian beam in turbulent atmosphere

Science.gov (United States)

Lukin, Igor P.

2017-11-01

In this article the coherence of a fundamental Bessel-Gaussian optical beam in turbulent atmosphere is analyzed. The problem analysis is based on the solution of the equation for the transverse second-order mutual coherence function of a fundamental Bessel-Gaussian optical beam of optical radiation. The behavior of a coherence degree of a fundamental Bessel-Gaussian optical beam depending on parameters of an optical beam and characteristics of turbulent atmosphere is examined. It was revealed that at low levels of fluctuations in turbulent atmosphere the coherence degree of a fundamental Bessel-Gaussian optical beam has the characteristic oscillating appearance. At high levels of fluctuations in turbulent atmosphere the coherence degree of a fundamental Bessel-Gaussian optical beam is described by an one-scale decreasing curve which in process of increase of level of fluctuations on a line of formation of a laser beam becomes closer to the same characteristic of a spherical optical wave.

Semiparametric inference on the fractal index of Gaussian and conditionally Gaussian time series data

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...

Improving satellite-based PM2.5 estimates in China using Gaussian processes modeling in a Bayesian hierarchical setting.

Science.gov (United States)

Yu, Wenxi; Liu, Yang; Ma, Zongwei; Bi, Jun

2017-08-01

Using satellite-based aerosol optical depth (AOD) measurements and statistical models to estimate ground-level PM 2.5 is a promising way to fill the areas that are not covered by ground PM 2.5 monitors. The statistical models used in previous studies are primarily Linear Mixed Effects (LME) and Geographically Weighted Regression (GWR) models. In this study, we developed a new regression model between PM 2.5 and AOD using Gaussian processes in a Bayesian hierarchical setting. Gaussian processes model the stochastic nature of the spatial random effects, where the mean surface and the covariance function is specified. The spatial stochastic process is incorporated under the Bayesian hierarchical framework to explain the variation of PM 2.5 concentrations together with other factors, such as AOD, spatial and non-spatial random effects. We evaluate the results of our model and compare them with those of other, conventional statistical models (GWR and LME) by within-sample model fitting and out-of-sample validation (cross validation, CV). The results show that our model possesses a CV result (R 2  = 0.81) that reflects higher accuracy than that of GWR and LME (0.74 and 0.48, respectively). Our results indicate that Gaussian process models have the potential to improve the accuracy of satellite-based PM 2.5 estimates.

Equivalent non-Gaussian excitation method for response moment calculation of systems under non-Gaussian random excitation

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)

Construction of Capacity Achieving Lattice Gaussian Codes

KAUST Repository

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].

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

Time-domain least-squares migration using the Gaussian beam summation method

Science.gov (United States)

Yang, Jidong; Zhu, Hejun; McMechan, George; Yue, Yubo

2018-04-01

With a finite recording aperture, a limited source spectrum and unbalanced illumination, traditional imaging methods are insufficient to generate satisfactory depth profiles with high resolution and high amplitude fidelity. This is because traditional migration uses the adjoint operator of the forward modeling rather than the inverse operator. We propose a least-squares migration approach based on the time-domain Gaussian beam summation, which helps to balance subsurface illumination and improve image resolution. Based on the Born approximation for the isotropic acoustic wave equation, we derive a linear time-domain Gaussian beam modeling operator, which significantly reduces computational costs in comparison with the spectral method. Then, we formulate the corresponding adjoint Gaussian beam migration, as the gradient of an L2-norm waveform misfit function. An L1-norm regularization is introduced to the inversion to enhance the robustness of least-squares migration, and an approximated diagonal Hessian is used as a preconditioner to speed convergence. Synthetic and field data examples demonstrate that the proposed approach improves imaging resolution and amplitude fidelity in comparison with traditional Gaussian beam migration.

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.

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.)

Wavelet-transform-based time–frequency domain reflectometry for reduction of blind spot

International Nuclear Information System (INIS)

Lee, Sin Ho; Park, Jin Bae; Choi, Yoon Ho

2012-01-01

In this paper, wavelet-transform-based time–frequency domain reflectometry (WTFDR) is proposed to reduce the blind spot in reflectometry. TFDR has a blind spot problem when the time delay between the reference signal and the reflected signal is short enough compared with the time duration of the reference signal. To solve the blind spot problem, the wavelet transform (WT) is used because the WT has linearity. Using the characteristics of the WT, the overlapped reference signal at the measured signal can be separated and the blind spot is reduced by obtaining the difference of the wavelet coefficients for the reference and reflected signals. In the proposed method, the complex wavelet is utilized as a mother wavelet because the reference signal in WTFDR has a complex form. Finally, the computer simulations and the real experiments are carried out to confirm the effectiveness and accuracy of the proposed method. (paper)

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.

Solar-blind ultraviolet band-pass filter based on metal—dielectric multilayer structures

International Nuclear Information System (INIS)

Wang Tian-Jiao; Xu Wei-Zong; Lu Hai; Ren Fang-Fang; Chen Dun-Jun; Zhang Rong; Zheng You-Dou

2014-01-01

Solar-blind ultraviolet (UV) band-pass filter has significant value in many scientific, commercial, and military applications, in which the detection of weak UV signal against a strong background of solar radiation is required. In this work, a solar-blind filter is designed based on the concept of “transparent metal”. The filter consisting of Al/SiO 2 multilayers could exhibit a high transmission in the solar-blind wavelength region and a wide stopband extending from near-ultraviolet to infrared wavelength range. The central wavelength, bandwidth, Q factor, and rejection ratio of the passband are numerically studied as a function of individual layer thickness and multilayer period. (electromagnetism, optics, acoustics, heat transfer, classical mechanics, and fluid dynamics)

Global sensitivity analysis using a Gaussian Radial Basis Function metamodel

International Nuclear Information System (INIS)

Wu, Zeping; Wang, Donghui; Okolo N, Patrick; Hu, Fan; Zhang, Weihua

2016-01-01

Sensitivity analysis plays an important role in exploring the actual impact of adjustable parameters on response variables. Amongst the wide range of documented studies on sensitivity measures and analysis, Sobol' indices have received greater portion of attention due to the fact that they can provide accurate information for most models. In this paper, a novel analytical expression to compute the Sobol' indices is derived by introducing a method which uses the Gaussian Radial Basis Function to build metamodels of computationally expensive computer codes. Performance of the proposed method is validated against various analytical functions and also a structural simulation scenario. Results demonstrate that the proposed method is an efficient approach, requiring a computational cost of one to two orders of magnitude less when compared to the traditional Quasi Monte Carlo-based evaluation of Sobol' indices. - Highlights: • RBF based sensitivity analysis method is proposed. • Sobol' decomposition of Gaussian RBF metamodel is obtained. • Sobol' indices of Gaussian RBF metamodel are derived based on the decomposition. • The efficiency of proposed method is validated by some numerical examples.

An adaptive Gaussian process-based iterative ensemble smoother for data assimilation

Science.gov (United States)

Ju, Lei; Zhang, Jiangjiang; Meng, Long; Wu, Laosheng; Zeng, Lingzao

2018-05-01

Accurate characterization of subsurface hydraulic conductivity is vital for modeling of subsurface flow and transport. The iterative ensemble smoother (IES) has been proposed to estimate the heterogeneous parameter field. As a Monte Carlo-based method, IES requires a relatively large ensemble size to guarantee its performance. To improve the computational efficiency, we propose an adaptive Gaussian process (GP)-based iterative ensemble smoother (GPIES) in this study. At each iteration, the GP surrogate is adaptively refined by adding a few new base points chosen from the updated parameter realizations. Then the sensitivity information between model parameters and measurements is calculated from a large number of realizations generated by the GP surrogate with virtually no computational cost. Since the original model evaluations are only required for base points, whose number is much smaller than the ensemble size, the computational cost is significantly reduced. The applicability of GPIES in estimating heterogeneous conductivity is evaluated by the saturated and unsaturated flow problems, respectively. Without sacrificing estimation accuracy, GPIES achieves about an order of magnitude of speed-up compared with the standard IES. Although subsurface flow problems are considered in this study, the proposed method can be equally applied to other hydrological models.

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

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.

Bootstrapping realized volatility and realized beta under a local Gaussianity assumption

DEFF Research Database (Denmark)

Hounyo, Ulrich

The main contribution of this paper is to propose a new bootstrap method for statistics based on high frequency returns. The new method exploits the local Gaussianity and the local constancy of volatility of high frequency returns, two assumptions that can simplify inference in the high frequency...... context, as recently explained by Mykland and Zhang (2009). Our main contributions are as follows. First, we show that the local Gaussian bootstrap is firstorder consistent when used to estimate the distributions of realized volatility and ealized betas. Second, we show that the local Gaussian bootstrap...... matches accurately the first four cumulants of realized volatility, implying that this method provides third-order refinements. This is in contrast with the wild bootstrap of Gonçalves and Meddahi (2009), which is only second-order correct. Third, we show that the local Gaussian bootstrap is able...

Blind Test of Physics-Based Prediction of Protein Structures

Science.gov (United States)

Shell, M. Scott; Ozkan, S. Banu; Voelz, Vincent; Wu, Guohong Albert; Dill, Ken A.

2009-01-01

We report here a multiprotein blind test of a computer method to predict native protein structures based solely on an all-atom physics-based force field. We use the AMBER 96 potential function with an implicit (GB/SA) model of solvation, combined with replica-exchange molecular-dynamics simulations. Coarse conformational sampling is performed using the zipping and assembly method (ZAM), an approach that is designed to mimic the putative physical routes of protein folding. ZAM was applied to the folding of six proteins, from 76 to 112 monomers in length, in CASP7, a community-wide blind test of protein structure prediction. Because these predictions have about the same level of accuracy as typical bioinformatics methods, and do not utilize information from databases of known native structures, this work opens up the possibility of predicting the structures of membrane proteins, synthetic peptides, or other foldable polymers, for which there is little prior knowledge of native structures. This approach may also be useful for predicting physical protein folding routes, non-native conformations, and other physical properties from amino acid sequences. PMID:19186130

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)

Predictive Active Set Selection Methods for Gaussian Processes

DEFF Research Database (Denmark)

Henao, Ricardo; Winther, Ole

2012-01-01

We propose an active set selection framework for Gaussian process classification for cases when the dataset is large enough to render its inference prohibitive. Our scheme consists of a two step alternating procedure of active set update rules and hyperparameter optimization based upon marginal...... high impact to the classifier decision process while removing those that are less relevant. We introduce two active set rules based on different criteria, the first one prefers a model with interpretable active set parameters whereas the second puts computational complexity first, thus a model...... with active set parameters that directly control its complexity. We also provide both theoretical and empirical support for our active set selection strategy being a good approximation of a full Gaussian process classifier. Our extensive experiments show that our approach can compete with state...

High-Performance Visible-Blind UV Phototransistors Based on n-Type Naphthalene Diimide Nanomaterials.

Science.gov (United States)

Song, Inho; Lee, Seung-Chul; Shang, Xiaobo; Ahn, Jaeyong; Jung, Hoon-Joo; Jeong, Chan-Uk; Kim, Sang-Wook; Yoon, Woojin; Yun, Hoseop; Kwon, O-Pil; Oh, Joon Hak

2018-04-11

This study investigates the performance of single-crystalline nanomaterials of wide-band gap naphthalene diimide (NDI) derivatives with methylene-bridged aromatic side chains. Such materials are found to be easily used as high-performance, visible-blind near-UV light detectors. NDI single-crystalline nanoribbons are assembled using a simple solution-based process (without solvent-inclusion problems), which is then applied to organic phototransistors (OPTs). Such OPTs exhibit excellent n-channel transistor characteristics, including an average electron mobility of 1.7 cm 2 V -1 s -1 , sensitive UV detection properties with a detection limit of ∼1 μW cm -2 , millisecond-level responses, and detectivity as high as 10 15 Jones, demonstrating the highly sensitive organic visible-blind UV detectors. The high performance of our OPTs originates from the large face-to-face π-π stacking area between the NDI semiconducting cores, which is facilitated by methylene-bridged aromatic side chains. Interestingly, NDI-based nanoribbon OPTs exhibit a distinct visible-blind near-UV detection with an identical detection limit, even under intense visible light illumination (for example, 10 4 times higher intensity than UV light intensity). Our findings demonstrate that wide-band gap NDI-based nanomaterials are highly promising for developing high-performance visible-blind UV photodetectors. Such photodetectors could potentially be used for various applications including environmental and health-monitoring systems.

A nationwide population-based study of low vision and blindness in South Korea.

Science.gov (United States)

Park, Shin Hae; Lee, Ji Sung; Heo, Hwan; Suh, Young-Woo; Kim, Seung-Hyun; Lim, Key Hwan; Moon, Nam Ju; Lee, Sung Jin; Park, Song Hee; Baek, Seung-Hee

2014-12-18

To investigate the prevalence and associated risk factors of low vision and blindness in the Korean population. This cross-sectional, population-based study examined the ophthalmologic data of 22,135 Koreans aged ≥5 years from the fifth Korea National Health and Nutrition Examination Survey (KNHANES V, 2010-2012). According to the World Health Organization criteria, blindness was defined as visual acuity (VA) less than 20/400 in the better-seeing eye, and low vision as VA of 20/60 or worse but 20/400 or better in the better-seeing eye. The prevalence rates were calculated from either presenting VA (PVA) or best-corrected VA (BCVA). Multivariate regression analysis was conducted for adults aged ≥20 years. The overall prevalence rates of PVA-defined low vision and blindness were 4.98% and 0.26%, respectively, and those of BCVA-defined low vision and blindness were 0.46% and 0.05%, respectively. Prevalence increased rapidly above the age of 70 years. For subjects aged ≥70 years, the population-weighted prevalence rates of low vision, based on PVA and BCVA, were 12.85% and 3.87%, respectively, and the corresponding rates of blindness were 0.49% and 0.42%, respectively. The presenting vision problems were significantly associated with age (younger adults or elderly subjects), female sex, low educational level, and lowest household income, whereas the best-corrected vision problems were associated with age ≥ 70 years, a low educational level, and rural residence. This population-based study provides useful information for planning optimal public eye health care services in South Korea. Copyright 2015 The Association for Research in Vision and Ophthalmology, Inc.

Free Vibration and Stability of Axially Functionally Graded Tapered Euler-Bernoulli Beams

Directory of Open Access Journals (Sweden)

Ahmad Shahba

2011-01-01

Full Text Available Structural analysis of axially functionally graded tapered Euler-Bernoulli beams is studied using finite element method. A beam element is proposed which takes advantage of the shape functions of homogeneous uniform beam elements. The effects of varying cross-sectional dimensions and mechanical properties of the functionally graded material are included in the evaluation of structural matrices. This method could be used for beam elements with any distributions of mass density and modulus of elasticity with arbitrarily varying cross-sectional area. Assuming polynomial distributions of modulus of elasticity and mass density, the competency of the element is examined in stability analysis, free longitudinal vibration and free transverse vibration of double tapered beams with different boundary conditions and the convergence rate of the element is then investigated.

Effective convergence to complete orbital bases and to the atomic Hartree--Fock limit through systematic sequences of Gaussian primitives

International Nuclear Information System (INIS)

Schmidt, M.W.; Ruedenberg, K.

1979-01-01

Optimal starting points for expanding molecular orbitals in terms of atomic orbitals are the self-consistent-field orbitals of the free atoms and accurate information about the latter is essential for the construction of effective AO bases for molecular calculations. For expansions of atomic SCF orbitals in terms of Gaussian primitives, which are of particular interest for applications in polyatomic quantum chemistry, previous information has been limited in accuracy. In the present investigation a simple procedure is given for finding expansions of atomic self-consistent-field orbitals in terms of Gaussian primitives to arbitrarily high accuracy. The method furthermore opens the first avenue so far for approaching complete basis sets through systematic sequences of atomic orbitals

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.

Monogamy inequality for distributed gaussian entanglement.

Science.gov (United States)

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.

Fault diagnosis of active magnetic bearings based on Gaussian GLRT detector

DEFF Research Database (Denmark)

Nagel, Leon; Galeazzi, Roberto; Voigt, Andreas Jauernik

2016-01-01

generalized likelihood ratio test is proposed for detecting faults striking the electromagnet. The detector is capable of detecting and isolating the occurrence of faults in e.g. the windings of bearing by tracking changes in the mean value of a Gaussian distribution. The statistical distribution...

Fair quantum blind signatures

International Nuclear Information System (INIS)

Tian-Yin, Wang; Qiao-Yan, Wen

2010-01-01

We present a new fair blind signature scheme based on the fundamental properties of quantum mechanics. In addition, we analyse the security of this scheme, and show that it is not possible to forge valid blind signatures. Moreover, comparisons between this scheme and public key blind signature schemes are also discussed. (general)

Applications of Fuss-Catalan Numbers to Success Runs of Bernoulli Trials

Directory of Open Access Journals (Sweden)

S. J. Dilworth

2016-01-01

Full Text Available In a recent paper, the authors derived the exact solution for the probability mass function of the geometric distribution of order k, expressing the roots of the associated auxiliary equation in terms of generating functions for Fuss-Catalan numbers. This paper applies the above formalism for the Fuss-Catalan numbers to treat additional problems pertaining to occurrences of success runs. New exact analytical expressions for the probability mass function and probability generating function and so forth are derived. First, we treat sequences of Bernoulli trials with r≥1 occurrences of success runs of length k with l-overlapping. The case l<0, where there must be a gap of at least l trials between success runs, is also studied. Next we treat the distribution of the waiting time for the rth nonoverlapping appearance of a pair of successes separated by at most k-2 failures (k≥2.

A Novel Partial Discharge Ultra-High Frequency Signal De-Noising Method Based on a Single-Channel Blind Source Separation Algorithm

Directory of Open Access Journals (Sweden)

Liangliang Wei

2018-02-01

Full Text Available To effectively de-noise the Gaussian white noise and periodic narrow-band interference in the background noise of partial discharge ultra-high frequency (PD UHF signals in field tests, a novel de-noising method, based on a single-channel blind source separation algorithm, is proposed. Compared with traditional methods, the proposed method can effectively de-noise the noise interference, and the distortion of the de-noising PD signal is smaller. Firstly, the PD UHF signal is time-frequency analyzed by S-transform to obtain the number of source signals. Then, the single-channel detected PD signal is converted into multi-channel signals by singular value decomposition (SVD, and background noise is separated from multi-channel PD UHF signals by the joint approximate diagonalization of eigen-matrix method. At last, the source PD signal is estimated and recovered by the l1-norm minimization method. The proposed de-noising method was applied on the simulation test and field test detected signals, and the de-noising performance of the different methods was compared. The simulation and field test results demonstrate the effectiveness and correctness of the proposed method.

Gaussian particle filter based pose and motion estimation

Institute of Scientific and Technical Information of China (English)

无

2007-01-01

Determination of relative three-dimensional (3D) position, orientation, and relative motion between two reference frames is an important problem in robotic guidance, manipulation, and assembly as well as in other fields such as photogrammetry.A solution to pose and motion estimation problem that uses two-dimensional (2D) intensity images from a single camera is desirable for real-time applications. The difficulty in performing this measurement is that the process of projecting 3D object features to 2D images is a nonlinear transformation. In this paper, the 3D transformation is modeled as a nonlinear stochastic system with the state estimation providing six degrees-of-freedom motion and position values, using line features in image plane as measuring inputs and dual quaternion to represent both rotation and translation in a unified notation. A filtering method called the Gaussian particle filter (GPF) based on the particle filtering concept is presented for 3D pose and motion estimation of a moving target from monocular image sequences. The method has been implemented with simulated data, and simulation results are provided along with comparisons to the extended Kalman filter (EKF) and the unscented Kalman filter (UKF) to show the relative advantages of the GPF. Simulation results showed that GPF is a superior alternative to EKF and UKF.

20 CFR 416.986 - Why and when we will find that you are no longer entitled to benefits based on statutory blindness.

Science.gov (United States)

2010-04-01

... longer entitled to benefits based on statutory blindness. 416.986 Section 416.986 Employees' Benefits... Disability and Blindness Blindness § 416.986 Why and when we will find that you are no longer entitled to benefits based on statutory blindness. (a) If your vision does not meet the definition of blindness. If you...

A Third-Party E-Payment Protocol Based on Quantum Group Blind Signature

Science.gov (United States)

Zhang, Jian-Zhong; Yang, Yuan-Yuan; Xie, Shu-Cui

2017-09-01

A third-party E-payment protocol based on quantum group blind signature is proposed in this paper. Our E-payment protocol could protect user's anonymity as the traditional E-payment systems do, and also have unconditional security which the classical E-payment systems can not provide. To achieve that, quantum key distribution, one-time pad and quantum group blind signature are adopted in our scheme. Furthermore, if there were a dispute, the manager Trent can identify who tells a lie.

Arbitrated Quantum Signature with Hamiltonian Algorithm Based on Blind Quantum Computation

Science.gov (United States)

Shi, Ronghua; Ding, Wanting; Shi, Jinjing

2018-03-01

A novel arbitrated quantum signature (AQS) scheme is proposed motivated by the Hamiltonian algorithm (HA) and blind quantum computation (BQC). The generation and verification of signature algorithm is designed based on HA, which enables the scheme to rely less on computational complexity. It is unnecessary to recover original messages when verifying signatures since the blind quantum computation is applied, which can improve the simplicity and operability of our scheme. It is proved that the scheme can be deployed securely, and the extended AQS has some extensive applications in E-payment system, E-government, E-business, etc.

Non-Gaussianity from inflation: theory and observations

Science.gov (United States)

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.

A Grasp-Pose Generation Method Based on Gaussian Mixture Models

Directory of Open Access Journals (Sweden)

Wenjia Wu

2015-11-01

Full Text Available A Gaussian Mixture Model (GMM-based grasp-pose generation method is proposed in this paper. Through offline training, the GMM is set up and used to depict the distribution of the robot's reachable orientations. By dividing the robot's workspace into small 3D voxels and training the GMM for each voxel, a look-up table covering all the workspace is built with the x, y and z positions as the index and the GMM as the entry. Through the definition of Task Space Regions (TSR, an object's feasible grasp poses are expressed as a continuous region. With the GMM, grasp poses can be preferentially sampled from regions with high reachability probabilities in the online grasp-planning stage. The GMM can also be used as a preliminary judgement of a grasp pose's reachability. Experiments on both a simulated and a real robot show the superiority of our method over the existing method.

Non-gaussianity versus nonlinearity of cosmological perturbations.

Science.gov (United States)

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.

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.

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

Investigating Einstein-Podolsky-Rosen steering of continuous-variable bipartite states by non-Gaussian pseudospin measurements

Science.gov (United States)

Xiang, Yu; Xu, Buqing; Mišta, Ladislav; Tufarelli, Tommaso; He, Qiongyi; Adesso, Gerardo

2017-10-01

Einstein-Podolsky-Rosen (EPR) steering is an asymmetric form of correlations which is intermediate between quantum entanglement and Bell nonlocality, and can be exploited as a resource for quantum communication with one untrusted party. In particular, steering of continuous-variable Gaussian states has been extensively studied theoretically and experimentally, as a fundamental manifestation of the EPR paradox. While most of these studies focused on quadrature measurements for steering detection, two recent works revealed that there exist Gaussian states which are only steerable by suitable non-Gaussian measurements. In this paper we perform a systematic investigation of EPR steering of bipartite Gaussian states by pseudospin measurements, complementing and extending previous findings. We first derive the density-matrix elements of two-mode squeezed thermal Gaussian states in the Fock basis, which may be of independent interest. We then use such a representation to investigate steering of these states as detected by a simple nonlinear criterion, based on second moments of the correlation matrix constructed from pseudospin operators. This analysis reveals previously unexplored regimes where non-Gaussian measurements are shown to be more effective than Gaussian ones to witness steering of Gaussian states in the presence of local noise. We further consider an alternative set of pseudospin observables, whose expectation value can be expressed more compactly in terms of Wigner functions for all two-mode Gaussian states. However, according to the adopted criterion, these observables are found to be always less sensitive than conventional Gaussian observables for steering detection. Finally, we investigate continuous-variable Werner states, which are non-Gaussian mixtures of Gaussian states, and find that pseudospin measurements are always more effective than Gaussian ones to reveal their steerability. Our results provide useful insights on the role of non-Gaussian

Object-Based Classification of Grasslands from High Resolution Satellite Image Time Series Using Gaussian Mean Map Kernels

Directory of Open Access Journals (Sweden)

Mailys Lopes

2017-07-01

Full Text Available This paper deals with the classification of grasslands using high resolution satellite image time series. Grasslands considered in this work are semi-natural elements in fragmented landscapes, i.e., they are heterogeneous and small elements. The first contribution of this study is to account for grassland heterogeneity while working at the object level by modeling its pixels distributions by a Gaussian distribution. To measure the similarity between two grasslands, a new kernel is proposed as a second contribution: the α -Gaussian mean kernel. It allows one to weight the influence of the covariance matrix when comparing two Gaussian distributions. This kernel is introduced in support vector machines for the supervised classification of grasslands from southwest France. A dense intra-annual multispectral time series of the Formosat-2 satellite is used for the classification of grasslands’ management practices, while an inter-annual NDVI time series of Formosat-2 is used for old and young grasslands’ discrimination. Results are compared to other existing pixel- and object-based approaches in terms of classification accuracy and processing time. The proposed method is shown to be a good compromise between processing speed and classification accuracy. It can adapt to the classification constraints, and it encompasses several similarity measures known in the literature. It is appropriate for the classification of small and heterogeneous objects such as grasslands.

Los valores del juego de parada óptima para medias aritméticas de variables de Bernoulli

OpenAIRE

Jaime Lobo Segura; Santiago Cambronero

2009-01-01

We study optimal stopping problems for generalized averages of identically distributed Bernoulli variables, taking values in the set D = {d0, d1}. We obtain a recurrent formula in the finite horizon case, which gives the value of the game in terms of associated problems of smaller horizon. This allows us to create algorithms for computing the value of the game, as well as the optimal stopping time in these cases. Moreover, we present a series of aplicattions to the study of pro...

Rao-Blackwellization for Adaptive Gaussian Sum Nonlinear Model Propagation

Science.gov (United States)

Semper, Sean R.; Crassidis, John L.; George, Jemin; Mukherjee, Siddharth; Singla, Puneet

2015-01-01

When dealing with imperfect data and general models of dynamic systems, the best estimate is always sought in the presence of uncertainty or unknown parameters. In many cases, as the first attempt, the Extended Kalman filter (EKF) provides sufficient solutions to handling issues arising from nonlinear and non-Gaussian estimation problems. But these issues may lead unacceptable performance and even divergence. In order to accurately capture the nonlinearities of most real-world dynamic systems, advanced filtering methods have been created to reduce filter divergence while enhancing performance. Approaches, such as Gaussian sum filtering, grid based Bayesian methods and particle filters are well-known examples of advanced methods used to represent and recursively reproduce an approximation to the state probability density function (pdf). Some of these filtering methods were conceptually developed years before their widespread uses were realized. Advanced nonlinear filtering methods currently benefit from the computing advancements in computational speeds, memory, and parallel processing. Grid based methods, multiple-model approaches and Gaussian sum filtering are numerical solutions that take advantage of different state coordinates or multiple-model methods that reduced the amount of approximations used. Choosing an efficient grid is very difficult for multi-dimensional state spaces, and oftentimes expensive computations must be done at each point. For the original Gaussian sum filter, a weighted sum of Gaussian density functions approximates the pdf but suffers at the update step for the individual component weight selections. In order to improve upon the original Gaussian sum filter, Ref. [2] introduces a weight update approach at the filter propagation stage instead of the measurement update stage. This weight update is performed by minimizing the integral square difference between the true forecast pdf and its Gaussian sum approximation. By adaptively updating

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).

Non-Markovianity of Gaussian Channels.

Science.gov (United States)

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.

Adaptive smoothing based on Gaussian processes regression increases the sensitivity and specificity of fMRI data.

Science.gov (United States)

Strappini, Francesca; Gilboa, Elad; Pitzalis, Sabrina; Kay, Kendrick; McAvoy, Mark; Nehorai, Arye; Snyder, Abraham Z

2017-03-01

Temporal and spatial filtering of fMRI data is often used to improve statistical power. However, conventional methods, such as smoothing with fixed-width Gaussian filters, remove fine-scale structure in the data, necessitating a tradeoff between sensitivity and specificity. Specifically, smoothing may increase sensitivity (reduce noise and increase statistical power) but at the cost loss of specificity in that fine-scale structure in neural activity patterns is lost. Here, we propose an alternative smoothing method based on Gaussian processes (GP) regression for single subjects fMRI experiments. This method adapts the level of smoothing on a voxel by voxel basis according to the characteristics of the local neural activity patterns. GP-based fMRI analysis has been heretofore impractical owing to computational demands. Here, we demonstrate a new implementation of GP that makes it possible to handle the massive data dimensionality of the typical fMRI experiment. We demonstrate how GP can be used as a drop-in replacement to conventional preprocessing steps for temporal and spatial smoothing in a standard fMRI pipeline. We present simulated and experimental results that show the increased sensitivity and specificity compared to conventional smoothing strategies. Hum Brain Mapp 38:1438-1459, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Searching for primordial non-Gaussianity in Planck CMB maps using a combined estimator

Energy Technology Data Exchange (ETDEWEB)

Novaes, C.P.; Wuensche, C.A. [Divisão de Astrofísica, Instituto Nacional de Pesquisas Espaciais, Av. dos Astronautas 1758, São José dos Campos 12227-010, SP (Brazil); Bernui, A. [Observatório Nacional, Rua General José Cristino 77, São Cristóvão, 20921-400, Rio de Janeiro, RJ (Brazil); Ferreira, I.S., E-mail: camilapnovaes@gmail.com, E-mail: bernui@on.br, E-mail: ivan@fis.unb.br, E-mail: ca.wuensche@inpe.br [Instituto de Física, Universidade de Brasília, Campus Universitário Darcy Ribeiro, Asa Norte, 70919-970, Brasília, DF (Brazil)

2014-01-01

The extensive search for deviations from Gaussianity in cosmic microwave background radiation (CMB) data is very important due to the information about the very early moments of the universe encoded there. Recent analyses from Planck CMB data do not exclude the presence of non-Gaussianity of small amplitude, although they are consistent with the Gaussian hypothesis. The use of different techniques is essential to provide information about types and amplitudes of non-Gaussianities in the CMB data. In particular, we find interesting to construct an estimator based upon the combination of two powerful statistical tools that appears to be sensitive enough to detect tiny deviations from Gaussianity in CMB maps. This estimator combines the Minkowski functionals with a Neural Network, maximizing a tool widely used to study non-Gaussian signals with a reinforcement of another tool designed to identify patterns in a data set. We test our estimator by analyzing simulated CMB maps contaminated with different amounts of local primordial non-Gaussianity quantified by the dimensionless parameter f{sub  NL}. We apply it to these sets of CMB maps and find ∼> 98% of chance of positive detection, even for small intensity local non-Gaussianity like f{sub  NL} = 38±18, the current limit from Planck data for large angular scales. Additionally, we test the suitability to distinguish between primary and secondary non-Gaussianities: first we train the Neural Network with two sets, one of nearly Gaussian CMB maps (|f{sub  NL}| ≤ 10) but contaminated with realistic inhomogeneous Planck noise (i.e., secondary non-Gaussianity) and the other of non-Gaussian CMB maps, that is, maps endowed with weak primordial non-Gaussianity (28 ≤ f{sub  NL} ≤ 48); after that we test an ensemble composed of CMB maps either with one of these non-Gaussian contaminations, and find out that our method successfully classifies ∼ 95% of the tested maps as being CMB maps containing primordial or

Change-readiness of the blind: a hospital based study in a coastal town of South India.

Science.gov (United States)

Shetty, Ramya; Kulkarni, Uma D

2014-01-01

Blindness is a devastating condition with psychosocial and economic effects. The shortcomings result in a burden to the blind person, the family and society. Rehabilitation of the blind can transform their lives. The aim of this study was to assess the "change-readiness" of the blind to undergo a "change-management". The study was a semi-structured pre-tested questionnaire-based study of 50 blind subjects in a medical college hospital. The blind participants were assessed for depression using the Beck Depression Inventory II, for the perceived effect of blindness on family, social life and occupation. The participants were counseled to undergo psychiatric management, vocational training, use blind aids and learn Braille. The willingness of the participants with reasons was assessed using a verbal analogue scale. Pearson Chi-square test, ANOVA and the t-test were used for statistical analysis. Over two-thirds of the subjects were depressed. Family life, social life and occupation were perceived to be affected by 44%, 66% and 74%, respectively. Change-readiness scores were low for low vision and blind aids, vocational training, psychiatric management, change of job and learning Braille. The low score was due to the associated taboo, dependence, lack of skills, embarrassment, etc., The most valuable feature was the family cohesiveness. The results suggest that there is a need to modify health policy to include blind rehabilitation, to improve visibility of blind rehabilitation centers, to include family members and co-professionals while managing the blind so that we treat the "blind person" and not a "pair of blind eyes".

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...

Perturbative Gaussianizing transforms for cosmological fields

Science.gov (United States)

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.

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

Generalized Gaussian Error Calculus

CERN Document Server

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...

Interaction of Airy–Gaussian beams in saturable media

International Nuclear Information System (INIS)

Zhou Meiling; Peng Yulian; Chen Chidao; Chen Bo; Peng Xi; Deng Dongmei

2016-01-01

Based on the nonlinear Schrödinger equation, the interactions of the two Airy–Gaussian components in the incidence are analyzed in saturable media, under the circumstances of the same amplitude and different amplitudes, respectively. It is found that the interaction can be both attractive and repulsive depending on the relative phase. The smaller the interval between two Airy–Gaussian components in the incidence is, the stronger the intensity of the interaction. However, with the equal amplitude, the symmetry is shown and the change of quasi-breathers is opposite in the in-phase case and out-of-phase case. As the distribution factor is increased, the phenomena of the quasi-breather and the self-accelerating of the two Airy–Gaussian components are weakened. When the amplitude is not equal, the image does not have symmetry. The obvious phenomenon of the interaction always arises on the side of larger input power in the incidence. The maximum intensity image is also simulated. Many of the characteristics which are contained within other images can also be concluded in this figure. (paper)

Elastic–plastic adhesive contact of non-Gaussian rough surfaces

Indian Academy of Sciences (India)

Adhesion; asymmetric roughness; elastic–plastic contact; non-Gaussian rough surfaces. ... model of contact deformation that is based on accurate Finite Element Analysis (FEA) of an elastic–plastic single asperity contact. ... Sadhana | News.

Dynamic Euler-Bernoulli Beam Equation: Classification and Reductions

Directory of Open Access Journals (Sweden)

R. Naz

2015-01-01

Full Text Available We study a dynamic fourth-order Euler-Bernoulli partial differential equation having a constant elastic modulus and area moment of inertia, a variable lineal mass density g(x, and the applied load denoted by f(u, a function of transverse displacement u(t,x. The complete Lie group classification is obtained for different forms of the variable lineal mass density g(x and applied load f(u. The equivalence transformations are constructed to simplify the determining equations for the symmetries. The principal algebra is one-dimensional and it extends to two- and three-dimensional algebras for an arbitrary applied load, general power-law, exponential, and log type of applied loads for different forms of g(x. For the linear applied load case, we obtain an infinite-dimensional Lie algebra. We recover the Lie symmetry classification results discussed in the literature when g(x is constant with variable applied load f(u. For the general power-law and exponential case the group invariant solutions are derived. The similarity transformations reduce the fourth-order partial differential equation to a fourth-order ordinary differential equation. For the power-law applied load case a compatible initial-boundary value problem for the clamped and free end beam cases is formulated. We deduce the fourth-order ordinary differential equation with appropriate initial and boundary conditions.

MCEM algorithm for the log-Gaussian Cox process

OpenAIRE

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 ...

Bridging asymptotic independence and dependence in spatial exbtremes using Gaussian scale mixtures

KAUST Repository

Huser, Raphaël

2017-06-23

Gaussian scale mixtures are constructed as Gaussian processes with a random variance. They have non-Gaussian marginals and can exhibit asymptotic dependence unlike Gaussian processes, which are asymptotically independent except in the case of perfect dependence. In this paper, we study the extremal dependence properties of Gaussian scale mixtures and we unify and extend general results on their joint tail decay rates in both asymptotic dependence and independence cases. Motivated by the analysis of spatial extremes, we propose flexible yet parsimonious parametric copula models that smoothly interpolate from asymptotic dependence to independence and include the Gaussian dependence as a special case. We show how these new models can be fitted to high threshold exceedances using a censored likelihood approach, and we demonstrate that they provide valuable information about tail characteristics. In particular, by borrowing strength across locations, our parametric model-based approach can also be used to provide evidence for or against either asymptotic dependence class, hence complementing information given at an exploratory stage by the widely used nonparametric or parametric estimates of the χ and χ̄ coefficients. We demonstrate the capacity of our methodology by adequately capturing the extremal properties of wind speed data collected in the Pacific Northwest, US.

Bridging asymptotic independence and dependence in spatial exbtremes using Gaussian scale mixtures

KAUST Repository

Huser, Raphaë l; Opitz, Thomas; Thibaud, Emeric

2017-01-01

Gaussian scale mixtures are constructed as Gaussian processes with a random variance. They have non-Gaussian marginals and can exhibit asymptotic dependence unlike Gaussian processes, which are asymptotically independent except in the case of perfect dependence. In this paper, we study the extremal dependence properties of Gaussian scale mixtures and we unify and extend general results on their joint tail decay rates in both asymptotic dependence and independence cases. Motivated by the analysis of spatial extremes, we propose flexible yet parsimonious parametric copula models that smoothly interpolate from asymptotic dependence to independence and include the Gaussian dependence as a special case. We show how these new models can be fitted to high threshold exceedances using a censored likelihood approach, and we demonstrate that they provide valuable information about tail characteristics. In particular, by borrowing strength across locations, our parametric model-based approach can also be used to provide evidence for or against either asymptotic dependence class, hence complementing information given at an exploratory stage by the widely used nonparametric or parametric estimates of the χ and χ̄ coefficients. We demonstrate the capacity of our methodology by adequately capturing the extremal properties of wind speed data collected in the Pacific Northwest, US.

A Robust Non-Gaussian Data Assimilation Method for Highly Non-Linear Models

Directory of Open Access Journals (Sweden)

Elias D. Nino-Ruiz

2018-03-01

Full Text Available In this paper, we propose an efficient EnKF implementation for non-Gaussian data assimilation based on Gaussian Mixture Models and Markov-Chain-Monte-Carlo (MCMC methods. The proposed method works as follows: based on an ensemble of model realizations, prior errors are estimated via a Gaussian Mixture density whose parameters are approximated by means of an Expectation Maximization method. Then, by using an iterative method, observation operators are linearized about current solutions and posterior modes are estimated via a MCMC implementation. The acceptance/rejection criterion is similar to that of the Metropolis-Hastings rule. Experimental tests are performed on the Lorenz 96 model. The results show that the proposed method can decrease prior errors by several order of magnitudes in a root-mean-square-error sense for nearly sparse or dense observational networks.

Blind Source Separation Based on Covariance Ratio and Artificial Bee Colony Algorithm

Directory of Open Access Journals (Sweden)

Lei Chen

2014-01-01

Full Text Available The computation amount in blind source separation based on bioinspired intelligence optimization is high. In order to solve this problem, we propose an effective blind source separation algorithm based on the artificial bee colony algorithm. In the proposed algorithm, the covariance ratio of the signals is utilized as the objective function and the artificial bee colony algorithm is used to solve it. The source signal component which is separated out, is then wiped off from mixtures using the deflation method. All the source signals can be recovered successfully by repeating the separation process. Simulation experiments demonstrate that significant improvement of the computation amount and the quality of signal separation is achieved by the proposed algorithm when compared to previous algorithms.

Linking network usage patterns to traffic Gaussianity fit

NARCIS (Netherlands)

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,

Parametric estimation of covariance function in Gaussian-process based Kriging models. Application to uncertainty quantification for computer experiments

International Nuclear Information System (INIS)

Bachoc, F.

2013-01-01

The parametric estimation of the covariance function of a Gaussian process is studied, in the framework of the Kriging model. Maximum Likelihood and Cross Validation estimators are considered. The correctly specified case, in which the covariance function of the Gaussian process does belong to the parametric set used for estimation, is first studied in an increasing-domain asymptotic framework. The sampling considered is a randomly perturbed multidimensional regular grid. Consistency and asymptotic normality are proved for the two estimators. It is then put into evidence that strong perturbations of the regular grid are always beneficial to Maximum Likelihood estimation. The incorrectly specified case, in which the covariance function of the Gaussian process does not belong to the parametric set used for estimation, is then studied. It is shown that Cross Validation is more robust than Maximum Likelihood in this case. Finally, two applications of the Kriging model with Gaussian processes are carried out on industrial data. For a validation problem of the friction model of the thermal-hydraulic code FLICA 4, where experimental results are available, it is shown that Gaussian process modeling of the FLICA 4 code model error enables to considerably improve its predictions. Finally, for a meta modeling problem of the GERMINAL thermal-mechanical code, the interest of the Kriging model with Gaussian processes, compared to neural network methods, is shown. (author) [fr

Stochastic differential calculus for Gaussian and non-Gaussian noises: A critical review

Science.gov (United States)

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.

Underlay Spectrum Sharing Techniques with In-Band Full-Duplex Systems using Improper Gaussian Signaling

KAUST Repository

Gaafar, Mohamed

2016-10-26

Sharing the spectrum with in-band full-duplex (FD) primary users (PUs) is a challenging and interesting problem in the underlay cognitive radio (CR) systems. The self-interference introduced at the primary network may dramatically impede the secondary user (SU) opportunity to access the spectrum. To tackle this problem, we use the so-called improper Gaussian signaling. Particularly, we assume the downlink transmission of a SU that uses improper Gaussian signaling while the FD PU pair implements the regular proper Gaussian signaling. First, we derive a closed form expression and an upper bound for the SU and PUs outage probabilities, respectively. Second, we optimize the SU signal parameters to minimize its outage probability while maintaining the required PUs quality-of-service based on the average channel state information (CSI). Moreover, we provide the conditions to reap merits from employing improper Gaussian signaling at the SU. Third, we design the SU signal parameters based on perfect knowledge of its direct link instantaneous CSI and investigate all benefits that can be achieved at both the SU and PUs. Finally, we provide some numerical results that demonstrate the advantages of using improper Gaussian signaling to access the spectrum of the FD PUs.

Effect of Post-Reconstruction Gaussian Filtering on Image Quality and Myocardial Blood Flow Measurement with N-13 Ammonia PET

Directory of Open Access Journals (Sweden)

Hyeon Sik Kim

2014-10-01

Full Text Available Objective(s: In order to evaluate the effect of post-reconstruction Gaussian filtering on image quality and myocardial blood flow (MBF measurement by dynamic N-13 ammonia positron emission tomography (PET, we compared various reconstruction and filtering methods with image characteristics. Methods: Dynamic PET images of three patients with coronary artery disease (male-female ratio of 2:1; age: 57, 53, and 76 years were reconstructed, using filtered back projection (FBP and ordered subset expectation maximization (OSEM methods. OSEM reconstruction consisted of OSEM_2I, OSEM_4I, and OSEM_6I with 2, 4, and 6 iterations, respectively. The images, reconstructed and filtered by Gaussian filters of 5, 10, and 15 mm, were obtained, as well as non-filtered images. Visual analysis of image quality (IQ was performed using a 3-grade scoring system by 2 independent readers, blinded to the reconstruction and filtering methods of stress images. Then, signal-to-noise ratio (SNR was calculated by noise and contrast recovery (CR. Stress and rest MBF and coronary flow reserve (CFR were obtained for each method. IQ scores, stress and rest MBF, and CFR were compared between the methods, using Chi-square and Kruskal-Wallis tests. Results: In the visual analysis, IQ was significantly higher by 10 mm Gaussian filtering, compared to other sizes of filter (PP=0.923 and 0.855 for readers 1 and 2, respectively. SNR was significantly higher in 10 mm Gaussian filter. There was a significant difference in stress and rest MBF between several vascular territories. However CFR was not significantly different according to various filtering methods. Conclusion: Post-reconstruction Gaussian filtering with a filter size of 10 mm significantly enhances the IQ of N-13 ammonia PET-CT, without changing the results of CFR calculation. .

Effect of Post-Reconstruction Gaussian Filtering on Image Quality and Myocardial Blood Flow Measurement with N-13 Ammonia PET

International Nuclear Information System (INIS)

Kim, Hyeon Sik; Cho, Sang-Geon; Kim, Ju Han; Kwon, Seong Young; Lee, Byeong-il; Bom, Hee-Seung

2014-01-01

In order to evaluate the effect of post-reconstruction Gaussian filtering on image quality and myocardial blood flow (MBF) measurement by dynamic N-13 ammonia positron emission tomography (PET), we compared various reconstruction and filtering methods with image characteristics. Dynamic PET images of three patients with coronary artery disease (male-female ratio of 2:1; age: 57, 53, and 76 years) were reconstructed, using filtered back projection (FBP) and ordered subset expectation maximization (OSEM) methods. OSEM reconstruction consisted of OSEM-2I, OSEM-4I, and OSEM-6I with 2, 4, and 6 iterations, respectively. The images, reconstructed and filtered by Gaussian filters of 5, 10, and 15 mm, were obtained, as well as non-filtered images. Visual analysis of image quality (IQ) was performed using a 3-grade scoring system by 2 independent readers, blinded to the reconstruction and filtering methods of stress images. Then, signal-to-noise ratio (SNR) was calculated by noise and contrast recovery (CR). Stress and rest MBF and coronary flow reserve (CFR) were obtained for each method. IQ scores, stress and rest MBF, and CFR were compared between the methods, using Chi-square and Kruskal-Wallis tests. In the visual analysis, IQ was significantly higher by 10 mm Gaussian filtering, compared to other sizes of filter (P<0.001 for both readers). However, no significant difference of IQ was found between FBP and various numbers of iteration in OSEM (P=0.923 and 0.855 for readers 1 and 2, respectively). SNR was significantly higher in 10 mm Gaussian filter. There was a significant difference in stress and rest MBF between several vascular territories. However CFR was not significantly different according to various filtering methods. Post-reconstruction Gaussian filtering with a filter size of 10 mm significantly enhances the IQ of N-13 ammonia PET-CT, without changing the results of CFR calculation

Pseudo inputs for pairwise learning with Gaussian processes

DEFF Research Database (Denmark)

Nielsen, Jens Brehm; Jensen, Bjørn Sand; Larsen, Jan

2012-01-01

We consider learning and prediction of pairwise comparisons between instances. The problem is motivated from a perceptual view point, where pairwise comparisons serve as an effective and extensively used paradigm. A state-of-the-art method for modeling pairwise data in high dimensional domains...... is based on a classical pairwise probit likelihood imposed with a Gaussian process prior. While extremely flexible, this non-parametric method struggles with an inconvenient O(n3) scaling in terms of the n input instances which limits the method only to smaller problems. To overcome this, we derive...... to other similar approximations that have been applied in standard Gaussian process regression and classification problems such as FI(T)C and PI(T)C....

Planck 2013 Results. XXIV. Constraints on primordial non-Gaussianity

CERN Document Server

Ade, P.A.R.; Armitage-Caplan, C.; Arnaud, M.; Ashdown, M.; Atrio-Barandela, F.; Aumont, J.; Baccigalupi, C.; Banday, A.J.; Barreiro, R.B.; Bartlett, J.G.; Bartolo, N.; Battaner, E.; Benabed, K.; Benoit, A.; Benoit-Levy, A.; Bernard, J.P.; Bersanelli, M.; Bielewicz, P.; Bobin, J.; Bock, J.J.; Bonaldi, A.; Bonavera, L.; Bond, J.R.; Borrill, J.; Bouchet, F.R.; Bridges, M.; Bucher, M.; Burigana, C.; Butler, R.C.; Cardoso, J.F.; Catalano, A.; Challinor, A.; Chamballu, A.; Chiang, L.Y.; Chiang, H.C.; Christensen, P.R.; Church, S.; Clements, D.L.; Colombi, S.; Colombo, L.P.L.; Couchot, F.; Coulais, A.; Crill, B.P.; Curto, A.; Cuttaia, F.; Danese, L.; Davies, R.D.; Davis, R.J.; de Bernardis, P.; de Rosa, A.; de Zotti, G.; Delabrouille, J.; Delouis, J.M.; Desert, F.X.; Diego, J.M.; Dole, H.; Donzelli, S.; Dore, O.; Douspis, M.; Ducout, A.; Dunkley, J.; Dupac, X.; Efstathiou, G.; Elsner, F.; Ensslin, T.A.; Eriksen, H.K.; Fergusson, J.; Finelli, F.; Forni, O.; Frailis, M.; Franceschi, E.; Galeotta, S.; Ganga, K.; Giard, M.; Giraud-Heraud, Y.; Gonzalez-Nuevo, J.; Gorski, K.M.; Gratton, S.; Gregorio, A.; Gruppuso, A.; Hansen, F.K.; Hanson, D.; Harrison, D.; Heavens, A.; Henrot-Versille, S.; Hernandez-Monteagudo, C.; Herranz, D.; Hildebrandt, S.R.; Hivon, E.; Hobson, M.; Holmes, W.A.; Hornstrup, A.; Hovest, W.; Huffenberger, K.M.; Jaffe, T.R.; Jaffe, A.H.; Jones, W.C.; Juvela, M.; Keihanen, E.; Keskitalo, R.; Kisner, T.S.; Knoche, J.; Knox, L.; Kunz, M.; Kurki-Suonio, H.; Lacasa, F.; Lagache, G.; Lahteenmaki, A.; Lamarre, J.M.; Lasenby, A.; Laureijs, R.J.; Lawrence, C.R.; Leahy, J.P.; Leonardi, R.; Lesgourgues, J.; Lewis, A.; Liguori, M.; Lilje, P.B.; Linden-Vornle, M.; Lopez-Caniego, M.; Lubin, P.M.; Macias-Perez, J.F.; Maffei, B.; Maino, D.; Mandolesi, N.; Mangilli, A.; Marinucci, D.; Maris, M.; Marshall, D.J.; Martin, P.G.; Martinez-Gonzalez, E.; Masi, S.; Matarrese, S.; Matthai, F.; Mazzotta, P.; Meinhold, P.R.; Melchiorri, A.; Mendes, L.; Mennella, A.; Migliaccio, M.; Mitra, S.; Miville-Deschenes, M.A.; Moneti, A.; Montier, L.; Morgante, G.; Mortlock, D.; Moss, A.; Munshi, D.; Naselsky, P.; Natoli, P.; Netterfield, C.B.; Norgaard-Nielsen, H.U.; Noviello, F.; Novikov, D.; Novikov, I.; Osborne, S.; Oxborrow, C.A.; Paci, F.; Pagano, L.; Pajot, F.; Paoletti, D.; Pasian, F.; Patanchon, G.; Peiris, H.V.; Perdereau, O.; Perotto, L.; Perrotta, F.; Piacentini, F.; Piat, M.; Pierpaoli, E.; Pietrobon, D.; Plaszczynski, S.; Pointecouteau, E.; Polenta, G.; Ponthieu, N.; Popa, L.; Poutanen, T.; Pratt, G.W.; Prezeau, G.; Prunet, S.; Puget, J.L.; Rachen, J.P.; Racine, B.; Rebolo, R.; Reinecke, M.; Remazeilles, M.; Renault, C.; Renzi, A.; Ricciardi, S.; Riller, T.; Ristorcelli, I.; Rocha, G.; Rosset, C.; Roudier, G.; Rubino-Martin, J.A.; Rusholme, B.; Sandri, M.; Santos, D.; Savini, G.; Scott, D.; Seiffert, M.D.; Shellard, E.P.S.; Smith, K.; Spencer, L.D.; Starck, J.L.; Stolyarov, V.; Stompor, R.; Sudiwala, R.; Sunyaev, R.; Sureau, F.; Sutton, D.; Suur-Uski, A.S.; Sygnet, J.F.; Tauber, J.A.; Tavagnacco, D.; Terenzi, L.; Toffolatti, L.; Tomasi, M.; Tristram, M.; Tucci, M.; Tuovinen, J.; Valenziano, L.; Valiviita, J.; Van Tent, B.; Varis, J.; Vielva, P.; Villa, F.; Vittorio, N.; Wade, L.A.; Wandelt, B.D.; White, M.; White, S.D.M.; Yvon, D.; Zacchei, A.; Zonca, A.

2014-01-01

The Planck nominal mission cosmic microwave background (CMB) maps yield unprecedented constraints on primordial non-Gaussianity (NG). Using three optimal bispectrum estimators, separable template-fitting (KSW), binned, and modal, we obtain consistent values for the primordial local, equilateral, and orthogonal bispectrum amplitudes, quoting as our final result fNL^local= 2.7+/-5.8, fNL^equil= -42+/-75, and fNL^ortho= -25+\\-39 (68% CL statistical). NG is detected in the data; using skew-C_l statistics we find a nonzero bispectrum from residual point sources, and the ISW-lensing bispectrum at a level expected in the LambdaCDM scenario. The results are based on comprehensive cross-validation of these estimators on Gaussian and non-Gaussian simulations, are stable across component separation techniques, pass an extensive suite of tests, and are confirmed by skew-C_l, wavelet bispectrum and Minkowski functional estimators. Beyond estimates of individual shape amplitudes, we present model-independent, 3-dimensional...

General Galilei Covariant Gaussian Maps

Science.gov (United States)

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 maximally multipartite-entangled states

Science.gov (United 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.

Hybrid algorithm of ensemble transform and importance sampling for assimilation of non-Gaussian observations

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.

Nonlocal theory of curved rods. 2-D, high order, Timoshenko’s and Euler-Bernoulli models

Directory of Open Access Journals (Sweden)

Zozulya V.V.

2017-09-01

Full Text Available New models for plane curved rods based on linear nonlocal theory of elasticity have been developed. The 2-D theory is developed from general 2-D equations of linear nonlocal elasticity using a special curvilinear system of coordinates related to the middle line of the rod along with special hypothesis based on assumptions that take into account the fact that the rod is thin. High order theory is based on the expansion of the equations of the theory of elasticity into Fourier series in terms of Legendre polynomials. First, stress and strain tensors, vectors of displacements and body forces have been expanded into Fourier series in terms of Legendre polynomials with respect to a thickness coordinate. Thereby, all equations of elasticity including nonlocal constitutive relations have been transformed to the corresponding equations for Fourier coefficients. Then, in the same way as in the theory of local elasticity, a system of differential equations in terms of displacements for Fourier coefficients has been obtained. First and second order approximations have been considered in detail. Timoshenko’s and Euler-Bernoulli theories are based on the classical hypothesis and the 2-D equations of linear nonlocal theory of elasticity which are considered in a special curvilinear system of coordinates related to the middle line of the rod. The obtained equations can be used to calculate stress-strain and to model thin walled structures in micro- and nanoscales when taking into account size dependent and nonlocal effects.

Design and Development of a Mobile Sensor Based the Blind Assistance Wayfinding System

Science.gov (United States)

Barati, F.; Delavar, M. R.

2015-12-01

The blind and visually impaired people are facing a number of challenges in their daily life. One of the major challenges is finding their way both indoor and outdoor. For this reason, routing and navigation independently, especially in urban areas are important for the blind. Most of the blind undertake route finding and navigation with the help of a guide. In addition, other tools such as a cane, guide dog or electronic aids are used by the blind. However, in some cases these aids are not efficient enough in a wayfinding around obstacles and dangerous areas for the blind. As a result, the need to develop effective methods as decision support using a non-visual media is leading to improve quality of life for the blind through their increased mobility and independence. In this study, we designed and implemented an outdoor mobile sensor-based wayfinding system for the blind. The objectives of this study are to guide the blind for the obstacle recognition and the design and implementation of a wayfinding and navigation mobile sensor system for them. In this study an ultrasonic sensor is used to detect obstacles and GPS is employed for positioning and navigation in the wayfinding. This type of ultrasonic sensor measures the interval between sending waves and receiving the echo signals with respect to the speed of sound in the environment to estimate the distance to the obstacles. In this study the coordinates and characteristics of all the obstacles in the study area are already stored in a GIS database. All of these obstacles were labeled on the map. The ultrasonic sensor designed and constructed in this study has the ability to detect the obstacles in a distance of 2cm to 400cm. The implementation and the results obtained from the interview of a number of blind persons who employed the sensor verified that the designed mobile sensor system for wayfinding was very satisfactory.

DESIGN AND DEVELOPMENT OF A MOBILE SENSOR BASED THE BLIND ASSISTANCE WAYFINDING SYSTEM

Directory of Open Access Journals (Sweden)

F. Barati

2015-12-01

Full Text Available The blind and visually impaired people are facing a number of challenges in their daily life. One of the major challenges is finding their way both indoor and outdoor. For this reason, routing and navigation independently, especially in urban areas are important for the blind. Most of the blind undertake route finding and navigation with the help of a guide. In addition, other tools such as a cane, guide dog or electronic aids are used by the blind. However, in some cases these aids are not efficient enough in a wayfinding around obstacles and dangerous areas for the blind. As a result, the need to develop effective methods as decision support using a non-visual media is leading to improve quality of life for the blind through their increased mobility and independence. In this study, we designed and implemented an outdoor mobile sensor-based wayfinding system for the blind. The objectives of this study are to guide the blind for the obstacle recognition and the design and implementation of a wayfinding and navigation mobile sensor system for them. In this study an ultrasonic sensor is used to detect obstacles and GPS is employed for positioning and navigation in the wayfinding. This type of ultrasonic sensor measures the interval between sending waves and receiving the echo signals with respect to the speed of sound in the environment to estimate the distance to the obstacles. In this study the coordinates and characteristics of all the obstacles in the study area are already stored in a GIS database. All of these obstacles were labeled on the map. The ultrasonic sensor designed and constructed in this study has the ability to detect the obstacles in a distance of 2cm to 400cm. The implementation and the results obtained from the interview of a number of blind persons who employed the sensor verified that the designed mobile sensor system for wayfinding was very satisfactory.

Loop corrections to primordial non-Gaussianity

Science.gov (United States)

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.

Statistical tests for the Gaussian nature of primordial fluctuations through CBR experiments

International Nuclear Information System (INIS)

Luo, X.

1994-01-01

Information about the physical processes that generate the primordial fluctuations in the early Universe can be gained by testing the Gaussian nature of the fluctuations through cosmic microwave background radiation (CBR) temperature anisotropy experiments. One of the crucial aspects of density perturbations that are produced by the standard inflation scenario is that they are Gaussian, whereas seeds produced by topological defects left over from an early cosmic phase transition tend to be non-Gaussian. To carry out this test, sophisticated statistical tools are required. In this paper, we will discuss several such statistical tools, including multivariant skewness and kurtosis, Euler-Poincare characteristics, the three-point temperature correlation function, and Hotelling's T 2 statistic defined through bispectral estimates of a one-dimensional data set. The effect of noise present in the current data is discussed in detail and the COBE 53 GHz data set is analyzed. Our analysis shows that, on the large angular scale to which COBE is sensitive, the statistics are probably Gaussian. On the small angular scales, the importance of Hotelling's T 2 statistic is stressed, and the minimum sample size required to test Gaussianity is estimated. Although the current data set available from various experiments at half-degree scales is still too small, improvement of the data set by roughly a factor of 2 will be enough to test the Gaussianity statistically. On the arc min scale, we analyze the recent RING data through bispectral analysis, and the result indicates possible deviation from Gaussianity. Effects of point sources are also discussed. It is pointed out that the Gaussianity problem can be resolved in the near future by ground-based or balloon-borne experiments

Variational Gaussian approximation for Poisson data

Science.gov (United States)

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.

BlindSense: An Accessibility-inclusive Universal User Interface for Blind People

Directory of Open Access Journals (Sweden)

A. Khan

2018-04-01

Full Text Available A large number of blind people use smartphone-based assistive technology to perform their common activities. In order to provide a better user experience the existing user interface paradigm needs to be revisited. A new user interface model has been proposed in this paper. A simplified, semantically consistent, and blind-friendly adaptive user interface is provided. The proposed solution is evaluated through an empirical study on 63 blind people leveraging an improved user experience in performing common activities on a smartphone.

Finding Objects for Assisting Blind People

OpenAIRE

Yi, Chucai; Flores, Roberto W.; Chincha, Ricardo; Tian, YingLi

2013-01-01

Computer vision technology has been widely used for blind assistance, such as navigation and wayfinding. However, few camera-based systems are developed for helping blind or visually-impaired people to find daily necessities. In this paper, we propose a prototype system of blind-assistant object finding by camera-based network and matching-based recognition. We collect a dataset of daily necessities and apply Speeded-Up Robust Features (SURF) and Scale Invariant Feature Transform (SIFT) featu...

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.

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....

Electronic white cane with GPS radar-based concept as blind mobility enhancement without distance limitation

Science.gov (United States)

Halim, Suharsono; Handafiah, Finna; Aprilliyani, Ria; Udhiarto, Arief

2018-02-01

The Indonesian Ministry of Social Affairs, in July 2012, informed that the number of blind in Indonesia has been the largest among to the people with other disabilities. The most common tools utilized to help the blind was a conventional cane which has limited features and therefore it was difficult to be used as a mobilization tools. Moreover, the conventional cane cannot assist them or their family when the blind gets lost. In this research, we designed and implemented an electronic white cane with the concept of radar and global positioning system (GPS). The purpose of this research was to design and develop an electronic white cane which can enhance the mobility of the blind without distance coverage limitation. Utilizing ultrasonic sensors as a distance measurement and a servo motor as an actuator, the produced radar system is able to map an area with maximum distance and coverage angle of 5 meters and 180° respectively. The blind senses the obstacle around them from the vibration generated by five vibration motors. The vibration becomes more intense when the obstacle is detected closer. In addition, we implemented a GPS to monitor the blind's position and allow their family to find them easily when the blind need a help. Based on the tests performed, we have successfully developed an electronic white cane that can be a solution to improve the blind's mobility.

On the insufficiency of arbitrarily precise covariance matrices: non-Gaussian weak-lensing likelihoods

Science.gov (United States)

Sellentin, Elena; Heavens, Alan F.

2018-01-01

We investigate whether a Gaussian likelihood, as routinely assumed in the analysis of cosmological data, is supported by simulated survey data. We define test statistics, based on a novel method that first destroys Gaussian correlations in a data set, and then measures the non-Gaussian correlations that remain. This procedure flags pairs of data points that depend on each other in a non-Gaussian fashion, and thereby identifies where the assumption of a Gaussian likelihood breaks down. Using this diagnosis, we find that non-Gaussian correlations in the CFHTLenS cosmic shear correlation functions are significant. With a simple exclusion of the most contaminated data points, the posterior for s8 is shifted without broadening, but we find no significant reduction in the tension with s8 derived from Planck cosmic microwave background data. However, we also show that the one-point distributions of the correlation statistics are noticeably skewed, such that sound weak-lensing data sets are intrinsically likely to lead to a systematically low lensing amplitude being inferred. The detected non-Gaussianities get larger with increasing angular scale such that for future wide-angle surveys such as Euclid or LSST, with their very small statistical errors, the large-scale modes are expected to be increasingly affected. The shifts in posteriors may then not be negligible and we recommend that these diagnostic tests be run as part of future analyses.

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.

Super-resolving random-Gaussian apodized photon sieve.

Science.gov (United States)

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.

A novel Gaussian model based battery state estimation approach: State-of-Energy

International Nuclear Information System (INIS)

He, HongWen; Zhang, YongZhi; Xiong, Rui; Wang, Chun

2015-01-01

Highlights: • The Gaussian model is employed to construct a novel battery model. • The genetic algorithm is used to implement model parameter identification. • The AIC is used to decide the best hysteresis order of the battery model. • A novel battery SoE estimator is proposed and verified by two kinds of batteries. - Abstract: State-of-energy (SoE) is a very important index for battery management system (BMS) used in electric vehicles (EVs), it is indispensable for ensuring safety and reliable operation of batteries. For achieving battery SoE accurately, the main work can be summarized in three aspects. (1) In considering that different kinds of batteries show different open circuit voltage behaviors, the Gaussian model is employed to construct the battery model. What is more, the genetic algorithm is employed to locate the optimal parameter for the selecting battery model. (2) To determine an optimal tradeoff between battery model complexity and prediction precision, the Akaike information criterion (AIC) is used to determine the best hysteresis order of the combined battery model. Results from a comparative analysis show that the first-order hysteresis battery model is thought of being the best based on the AIC values. (3) The central difference Kalman filter (CDKF) is used to estimate the real-time SoE and an erroneous initial SoE is considered to evaluate the robustness of the SoE estimator. Lastly, two kinds of lithium-ion batteries are used to verify the proposed SoE estimation approach. The results show that the maximum SoE estimation error is within 1% for both LiFePO 4 and LiMn 2 O 4 battery datasets

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

A Component-Based Study of the Effect of Diameter on Bond and Anchorage Characteristics of Blind-Bolted Connections.

Directory of Open Access Journals (Sweden)

Muhammad Nasir Amin

Full Text Available Structural hollow sections are gaining worldwide importance due to their structural and architectural advantages over open steel sections. The only obstacle to their use is their connection with other structural members. To overcome the obstacle of tightening the bolt from one side has given birth to the concept of blind bolts. Blind bolts, being the practical solution to the connection hindrance for the use of hollow and concrete filled hollow sections play a vital role. Flowdrill, the Huck High Strength Blind Bolt and the Lindapter Hollobolt are the well-known commercially available blind bolts. Although the development of blind bolts has largely resolved this issue, the use of structural hollow sections remains limited to shear resistance. Therefore, a new modified version of the blind bolt, known as the "Extended Hollo-Bolt" (EHB due to its enhanced capacity for bonding with concrete, can overcome the issue of low moment resistance capacity associated with blind-bolted connections. The load transfer mechanism of this recently developed blind bolt remains unclear, however. This study uses a parametric approach to characterising the EHB, using diameter as the variable parameter. Stiffness and load-carrying capacity were evaluated at two different bolt sizes. To investigate the load transfer mechanism, a component-based study of the bond and anchorage characteristics was performed by breaking down the EHB into its components. The results of the study provide insight into the load transfer mechanism of the blind bolt in question. The proposed component-based model was validated by a spring model, through which the stiffness of the EHB was compared to that of its components combined. The combined stiffness of the components was found to be roughly equivalent to that of the EHB as a whole, validating the use of this component-based approach.

A Component-Based Study of the Effect of Diameter on Bond and Anchorage Characteristics of Blind-Bolted Connections.

Science.gov (United States)

Amin, Muhammad Nasir; Zaheer, Salman; Alazba, Abdulrahman Ali; Saleem, Muhammad Umair; Niazi, Muhammad Umar Khan; Khurram, Nauman; Amin, Muhammad Tahir

2016-01-01

Structural hollow sections are gaining worldwide importance due to their structural and architectural advantages over open steel sections. The only obstacle to their use is their connection with other structural members. To overcome the obstacle of tightening the bolt from one side has given birth to the concept of blind bolts. Blind bolts, being the practical solution to the connection hindrance for the use of hollow and concrete filled hollow sections play a vital role. Flowdrill, the Huck High Strength Blind Bolt and the Lindapter Hollobolt are the well-known commercially available blind bolts. Although the development of blind bolts has largely resolved this issue, the use of structural hollow sections remains limited to shear resistance. Therefore, a new modified version of the blind bolt, known as the "Extended Hollo-Bolt" (EHB) due to its enhanced capacity for bonding with concrete, can overcome the issue of low moment resistance capacity associated with blind-bolted connections. The load transfer mechanism of this recently developed blind bolt remains unclear, however. This study uses a parametric approach to characterising the EHB, using diameter as the variable parameter. Stiffness and load-carrying capacity were evaluated at two different bolt sizes. To investigate the load transfer mechanism, a component-based study of the bond and anchorage characteristics was performed by breaking down the EHB into its components. The results of the study provide insight into the load transfer mechanism of the blind bolt in question. The proposed component-based model was validated by a spring model, through which the stiffness of the EHB was compared to that of its components combined. The combined stiffness of the components was found to be roughly equivalent to that of the EHB as a whole, validating the use of this component-based approach.

Comparison of results from dispersion models for regulatory purposes based on Gaussian-and Lagrangian-algorithms: an evaluating literature study

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.)

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)

From plane waves to local Gaussians for the simulation of correlated periodic systems

International Nuclear Information System (INIS)

Booth, George H.; Tsatsoulis, Theodoros; Grüneis, Andreas; Chan, Garnet Kin-Lic

2016-01-01

We present a simple, robust, and black-box approach to the implementation and use of local, periodic, atom-centered Gaussian basis functions within a plane wave code, in a computationally efficient manner. The procedure outlined is based on the representation of the Gaussians within a finite bandwidth by their underlying plane wave coefficients. The core region is handled within the projected augment wave framework, by pseudizing the Gaussian functions within a cutoff radius around each nucleus, smoothing the functions so that they are faithfully represented by a plane wave basis with only moderate kinetic energy cutoff. To mitigate the effects of the basis set superposition error and incompleteness at the mean-field level introduced by the Gaussian basis, we also propose a hybrid approach, whereby the complete occupied space is first converged within a large plane wave basis, and the Gaussian basis used to construct a complementary virtual space for the application of correlated methods. We demonstrate that these pseudized Gaussians yield compact and systematically improvable spaces with an accuracy comparable to their non-pseudized Gaussian counterparts. A key advantage of the described method is its ability to efficiently capture and describe electronic correlation effects of weakly bound and low-dimensional systems, where plane waves are not sufficiently compact or able to be truncated without unphysical artifacts. We investigate the accuracy of the pseudized Gaussians for the water dimer interaction, neon solid, and water adsorption on a LiH surface, at the level of second-order Møller–Plesset perturbation theory.

From plane waves to local Gaussians for the simulation of correlated periodic systems

Energy Technology Data Exchange (ETDEWEB)

Booth, George H., E-mail: george.booth@kcl.ac.uk [Department of Physics, King’s College London, Strand, London WC2R 2LS (United Kingdom); Tsatsoulis, Theodoros; Grüneis, Andreas, E-mail: a.grueneis@fkf.mpg.de [Max Planck Institute for Solid State Research, Heisenbergstraße 1, 70569 Stuttgart (Germany); Chan, Garnet Kin-Lic [Frick Laboratory, Department of Chemistry, Princeton University, Princeton, New Jersey 08544 (United States)

2016-08-28

We present a simple, robust, and black-box approach to the implementation and use of local, periodic, atom-centered Gaussian basis functions within a plane wave code, in a computationally efficient manner. The procedure outlined is based on the representation of the Gaussians within a finite bandwidth by their underlying plane wave coefficients. The core region is handled within the projected augment wave framework, by pseudizing the Gaussian functions within a cutoff radius around each nucleus, smoothing the functions so that they are faithfully represented by a plane wave basis with only moderate kinetic energy cutoff. To mitigate the effects of the basis set superposition error and incompleteness at the mean-field level introduced by the Gaussian basis, we also propose a hybrid approach, whereby the complete occupied space is first converged within a large plane wave basis, and the Gaussian basis used to construct a complementary virtual space for the application of correlated methods. We demonstrate that these pseudized Gaussians yield compact and systematically improvable spaces with an accuracy comparable to their non-pseudized Gaussian counterparts. A key advantage of the described method is its ability to efficiently capture and describe electronic correlation effects of weakly bound and low-dimensional systems, where plane waves are not sufficiently compact or able to be truncated without unphysical artifacts. We investigate the accuracy of the pseudized Gaussians for the water dimer interaction, neon solid, and water adsorption on a LiH surface, at the level of second-order Møller–Plesset perturbation theory.

On the construction of capacity-achieving lattice Gaussian codes

KAUST Repository

Alghamdi, Wael Mohammed Abdullah

2016-08-15

In this paper, 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]. © 2016 IEEE.

On the construction of capacity-achieving lattice Gaussian codes

KAUST Repository

Alghamdi, Wael; Abediseid, Walid; Alouini, Mohamed-Slim

2016-01-01

In this paper, 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]. © 2016 IEEE.

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.

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

Postural control in blind subjects.

Science.gov (United States)

Soares, Antonio Vinicius; Oliveira, Cláudia Silva Remor de; Knabben, Rodrigo José; Domenech, Susana Cristina; Borges Junior, Noe Gomes

2011-12-01

To analyze postural control in acquired and congenitally blind adults. A total of 40 visually impaired adults participated in the research, divided into 2 groups, 20 with acquired blindness and 20 with congenital blindness - 21 males and 19 females, mean age 35.8 ± 10.8. The Brazilian version of Berg Balance Scale and the motor domain of functional independence measure were utilized. On Berg Balance Scale the mean for acquired blindness was 54.0 ± 2.4 and 54.4 ± 2.5 for congenitally blind subjects; on functional independence measure the mean for acquired blind group was 87.1 ± 4.8 and 87.3 ± 2.3 for congenitally blind group. Based upon the scale used the results suggest the ability to control posture can be developed by compensatory mechanisms and it is not affected by visual loss in congenitally and acquired blindness.

Equivariant Gröbner bases and the Gaussian two-factor model

NARCIS (Netherlands)

Brouwer, A.E.; Draisma, J.

2009-01-01

We show that the kernel I of the ring homomorphism R[yij | I, j ¿ N, i > j] ¿ R[si, ti | i ¿ N] determined by yij ¿ sisj +titj is generated by two types of polynomials: off-diagonal 3 x 3-minors and pentads. This confirms a conjecture by Drton, Sturmfels, and Sullivant on the Gaussian two-factor

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...

User-centered Technologies For Blind Children

Directory of Open Access Journals (Sweden)

Jaime Sánchez

2008-01-01

Full Text Available The purpose of this paper is to review, summarize, and illustrate research work involving four audio-based games created within a user-centered design methodology through successive usability tasks and evaluations. These games were designed by considering the mental model of blind children and their styles of interaction to perceive and process data and information. The goal of these games was to enhance the cognitive development of spatial structures, memory, haptic perception, mathematical skills, navigation and orientation, and problem solving of blind children. Findings indicate significant improvements in learning and cognition from using audio-based tools specially tailored for the blind. That is, technologies for blind children, carefully tailored through user-centered design approaches, can make a significant contribution to cognitive development of these children. This paper contributes new insight into the design and implementation of audio-based virtual environments to facilitate learning and cognition in blind children.

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 ...

Neural-network-based depth computation for blind navigation

Science.gov (United States)

Wong, Farrah; Nagarajan, Ramachandran R.; Yaacob, Sazali

2004-12-01

A research undertaken to help blind people to navigate autonomously or with minimum assistance is termed as "Blind Navigation". In this research, an aid that could help blind people in their navigation is proposed. Distance serves as an important clue during our navigation. A stereovision navigation aid implemented with two digital video cameras that are spaced apart and fixed on a headgear to obtain the distance information is presented. In this paper, a neural network methodology is used to obtain the required parameters of the camera which is known as camera calibration. These parameters are not known but obtained by adjusting the weights in the network. The inputs to the network consist of the matching features in the stereo pair images. A back propagation network with 16-input neurons, 3 hidden neurons and 1 output neuron, which gives depth, is created. The distance information is incorporated into the final processed image as four gray levels such as white, light gray, dark gray and black. Preliminary results have shown that the percentage errors fall below 10%. It is envisaged that the distance provided by neural network shall enable blind individuals to go near and pick up an object of interest.

Measurement of Hubble constant: non-Gaussian errors in HST Key Project data

Energy Technology Data Exchange (ETDEWEB)

Singh, Meghendra [Dr. A.P.J. Abdul Kalam Technical University, Uttar Pradesh, Lucknow, 226021 India (India); Gupta, Shashikant; Pandey, Ashwini [Amity University Haryana, Gurgaon, Haryana, 122413 India (India); Sharma, Satendra, E-mail: meghendrasingh_db@yahoo.co.in, E-mail: shashikantgupta.astro@gmail.com, E-mail: satyamkashwini@gmail.com, E-mail: ssharma_phy@yahoo.co.uk [Yobe State University, Damaturu, Yobe State (Nigeria)

2016-08-01

Assuming the Central Limit Theorem, experimental uncertainties in any data set are expected to follow the Gaussian distribution with zero mean. We propose an elegant method based on Kolmogorov-Smirnov statistic to test the above; and apply it on the measurement of Hubble constant which determines the expansion rate of the Universe. The measurements were made using Hubble Space Telescope. Our analysis shows that the uncertainties in the above measurement are non-Gaussian.

Contaminant dispersion prediction and source estimation with integrated Gaussian-machine learning network model for point source emission in atmosphere

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.

Contaminant dispersion prediction and source estimation with integrated Gaussian-machine learning network model for point source emission in atmosphere

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.

Postural control in blind subjects

Directory of Open Access Journals (Sweden)

Antonio Vinicius Soares

2011-12-01

Full Text Available Objective: To analyze postural control in acquired and congenitally blind adults. Methods: A total of 40 visually impaired adults participated in the research, divided into 2 groups, 20 with acquired blindness and 20 with congenital blindness - 21 males and 19 females, mean age 35.8 ± 10.8. The Brazilian version of Berg Balance Scale and the motor domain of functional independence measure were utilized. Results: On Berg Balance Scale the mean for acquired blindness was 54.0 ± 2.4 and 54.4 ± 2.5 for congenitally blind subjects; on functional independence measure the mean for acquired blind group was 87.1 ± 4.8 and 87.3 ± 2.3 for congenitally blind group. Conclusion: Based upon the scale used the results suggest the ability to control posture can be developed by compensatory mechanisms and it is not affected by visual loss in congenitally and acquired blindness.

An approximate fractional Gaussian noise model with computational cost

KAUST Repository

Sørbye, Sigrunn H.

2017-09-18

Fractional Gaussian noise (fGn) is a stationary time series model with long memory properties applied in various fields like econometrics, hydrology and climatology. The computational cost in fitting an fGn model of length $n$ using a likelihood-based approach is ${\\\\mathcal O}(n^{2})$, exploiting the Toeplitz structure of the covariance matrix. In most realistic cases, we do not observe the fGn process directly but only through indirect Gaussian observations, so the Toeplitz structure is easily lost and the computational cost increases to ${\\\\mathcal O}(n^{3})$. This paper presents an approximate fGn model of ${\\\\mathcal O}(n)$ computational cost, both with direct or indirect Gaussian observations, with or without conditioning. This is achieved by approximating fGn with a weighted sum of independent first-order autoregressive processes, fitting the parameters of the approximation to match the autocorrelation function of the fGn model. The resulting approximation is stationary despite being Markov and gives a remarkably accurate fit using only four components. The performance of the approximate fGn model is demonstrated in simulations and two real data examples.

Revisiting non-Gaussianity from non-attractor inflation models

Science.gov (United States)

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.

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.)

Cat Swarm Optimization Based Functional Link Artificial Neural Network Filter for Gaussian Noise Removal from Computed Tomography Images

Directory of Open Access Journals (Sweden)

M. Kumar

2016-01-01

Full Text Available Gaussian noise is one of the dominant noises, which degrades the quality of acquired Computed Tomography (CT image data. It creates difficulties in pathological identification or diagnosis of any disease. Gaussian noise elimination is desirable to improve the clarity of a CT image for clinical, diagnostic, and postprocessing applications. This paper proposes an evolutionary nonlinear adaptive filter approach, using Cat Swarm Functional Link Artificial Neural Network (CS-FLANN to remove the unwanted noise. The structure of the proposed filter is based on the Functional Link Artificial Neural Network (FLANN and the Cat Swarm Optimization (CSO is utilized for the selection of optimum weight of the neural network filter. The applied filter has been compared with the existing linear filters, like the mean filter and the adaptive Wiener filter. The performance indices, such as peak signal to noise ratio (PSNR, have been computed for the quantitative analysis of the proposed filter. The experimental evaluation established the superiority of the proposed filtering technique over existing methods.

Non-Gaussian Autoregressive Processes with Tukey g-and-h Transformations

KAUST Repository

Yan, Yuan

2017-11-20

When performing a time series analysis of continuous data, for example from climate or environmental problems, the assumption that the process is Gaussian is often violated. Therefore, we introduce two non-Gaussian autoregressive time series models that are able to fit skewed and heavy-tailed time series data. Our two models are based on the Tukey g-and-h transformation. We discuss parameter estimation, order selection, and forecasting procedures for our models and examine their performances in a simulation study. We demonstrate the usefulness of our models by applying them to two sets of wind speed data.

Non-Gaussian Autoregressive Processes with Tukey g-and-h Transformations

KAUST Repository

Yan, Yuan; Genton, Marc G.

2017-01-01

When performing a time series analysis of continuous data, for example from climate or environmental problems, the assumption that the process is Gaussian is often violated. Therefore, we introduce two non-Gaussian autoregressive time series models that are able to fit skewed and heavy-tailed time series data. Our two models are based on the Tukey g-and-h transformation. We discuss parameter estimation, order selection, and forecasting procedures for our models and examine their performances in a simulation study. We demonstrate the usefulness of our models by applying them to two sets of wind speed data.

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

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)

Multipoint propagators for non-Gaussian initial conditions

International Nuclear Information System (INIS)

Bernardeau, Francis; Sefusatti, Emiliano; Crocce, Martin

2010-01-01

We show here how renormalized perturbation theory calculations applied to the quasilinear growth of the large-scale structure can be carried on in presence of primordial non-Gaussian (PNG) initial conditions. It is explicitly demonstrated that the series reordering scheme proposed in Bernardeau, Crocce, and Scoccimarro [Phys. Rev. D 78, 103521 (2008)] is preserved for non-Gaussian initial conditions. This scheme applies to the power spectrum and higher-order spectra and is based on a reorganization of the contributing terms into the sum of products of multipoint propagators. In case of PNG, new contributing terms appear, the importance of which is discussed in the context of current PNG models. The properties of the building blocks of such resummation schemes, the multipoint propagators, are then investigated. It is first remarked that their expressions are left unchanged at one-loop order irrespective of statistical properties of the initial field. We furthermore show that the high-momentum limit of each of these propagators can be explicitly computed even for arbitrary initial conditions. They are found to be damped by an exponential cutoff whose expression is directly related to the moment generating function of the one-dimensional displacement field. This extends what had been established for multipoint propagators for Gaussian initial conditions. Numerical forms of the cutoff are shown for the so-called local model of PNG.

Quantifying entanglement in two-mode Gaussian states

Science.gov (United 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.

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.

A dynamic feedforward neural network based on gaussian particle swarm optimization and its application for predictive control.

Science.gov (United States)

Han, Min; Fan, Jianchao; Wang, Jun

2011-09-01

A dynamic feedforward neural network (DFNN) is proposed for predictive control, whose adaptive parameters are adjusted by using Gaussian particle swarm optimization (GPSO) in the training process. Adaptive time-delay operators are added in the DFNN to improve its generalization for poorly known nonlinear dynamic systems with long time delays. Furthermore, GPSO adopts a chaotic map with Gaussian function to balance the exploration and exploitation capabilities of particles, which improves the computational efficiency without compromising the performance of the DFNN. The stability of the particle dynamics is analyzed, based on the robust stability theory, without any restrictive assumption. A stability condition for the GPSO+DFNN model is derived, which ensures a satisfactory global search and quick convergence, without the need for gradients. The particle velocity ranges could change adaptively during the optimization process. The results of a comparative study show that the performance of the proposed algorithm can compete with selected algorithms on benchmark problems. Additional simulation results demonstrate the effectiveness and accuracy of the proposed combination algorithm in identifying and controlling nonlinear systems with long time delays.

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.

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

Science.gov (United 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.

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....

Finding Objects for Assisting Blind People.

Science.gov (United States)

Yi, Chucai; Flores, Roberto W; Chincha, Ricardo; Tian, Yingli

2013-07-01

Computer vision technology has been widely used for blind assistance, such as navigation and wayfinding. However, few camera-based systems are developed for helping blind or visually-impaired people to find daily necessities. In this paper, we propose a prototype system of blind-assistant object finding by camera-based network and matching-based recognition. We collect a dataset of daily necessities and apply Speeded-Up Robust Features (SURF) and Scale Invariant Feature Transform (SIFT) feature descriptors to perform object recognition. Experimental results demonstrate the effectiveness of our prototype system.

Couple stress theory of curved rods. 2-D, high order, Timoshenko’s and Euler-Bernoulli models

Directory of Open Access Journals (Sweden)

Zozulya V.V.

2017-01-01

Full Text Available New models for plane curved rods based on linear couple stress theory of elasticity have been developed.2-D theory is developed from general 2-D equations of linear couple stress elasticity using a special curvilinear system of coordinates related to the middle line of the rod as well as special hypothesis based on assumptions that take into account the fact that the rod is thin. High order theory is based on the expansion of the equations of the theory of elasticity into Fourier series in terms of Legendre polynomials. First, stress and strain tensors, vectors of displacements and rotation along with body forces have been expanded into Fourier series in terms of Legendre polynomials with respect to a thickness coordinate.Thereby, all equations of elasticity including Hooke’s law have been transformed to the corresponding equations for Fourier coefficients. Then, in the same way as in the theory of elasticity, a system of differential equations in terms of displacements and boundary conditions for Fourier coefficients have been obtained. Timoshenko’s and Euler-Bernoulli theories are based on the classical hypothesis and the 2-D equations of linear couple stress theory of elasticity in a special curvilinear system. The obtained equations can be used to calculate stress-strain and to model thin walled structures in macro, micro and nano scales when taking into account couple stress and rotation effects.

Generation of correlated finite alphabet waveforms using gaussian random variables

KAUST Repository

Ahmed, Sajid

2016-01-13

Various examples of methods and systems are provided for generation of correlated finite alphabet waveforms using Gaussian random variables in, e.g., radar and communication applications. In one example, a method includes mapping an input signal comprising Gaussian random variables (RVs) onto finite-alphabet non-constant-envelope (FANCE) symbols using a predetermined mapping function, and transmitting FANCE waveforms through a uniform linear array of antenna elements to obtain a corresponding beampattern. The FANCE waveforms can be based upon the mapping of the Gaussian RVs onto the FANCE symbols. In another example, a system includes a memory unit that can store a plurality of digital bit streams corresponding to FANCE symbols and a front end unit that can transmit FANCE waveforms through a uniform linear array of antenna elements to obtain a corresponding beampattern. The system can include a processing unit that can encode the input signal and/or determine the mapping function.

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

Generation of correlated finite alphabet waveforms using gaussian random variables

KAUST Repository

Ahmed, Sajid; Alouini, Mohamed-Slim; Jardak, Seifallah

2016-01-01

Various examples of methods and systems are provided for generation of correlated finite alphabet waveforms using Gaussian random variables in, e.g., radar and communication applications. In one example, a method includes mapping an input signal comprising Gaussian random variables (RVs) onto finite-alphabet non-constant-envelope (FANCE) symbols using a predetermined mapping function, and transmitting FANCE waveforms through a uniform linear array of antenna elements to obtain a corresponding beampattern. The FANCE waveforms can be based upon the mapping of the Gaussian RVs onto the FANCE symbols. In another example, a system includes a memory unit that can store a plurality of digital bit streams corresponding to FANCE symbols and a front end unit that can transmit FANCE waveforms through a uniform linear array of antenna elements to obtain a corresponding beampattern. The system can include a processing unit that can encode the input signal and/or determine the mapping function.

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.

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.

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...

Gaussian discriminating strength

Science.gov (United States)

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.

Gaussian mixture models-based ship target recognition algorithm in remote sensing infrared images

Science.gov (United States)

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.

Back to Normal! Gaussianizing posterior distributions for cosmological probes

Science.gov (United States)

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.

Comparing Approaches to Deal With Non-Gaussianity of Rainfall Data in Kriging-Based Radar-Gauge Rainfall Merging

Science.gov (United States)

Cecinati, F.; Wani, O.; Rico-Ramirez, M. A.

2017-11-01

Merging radar and rain gauge rainfall data is a technique used to improve the quality of spatial rainfall estimates and in particular the use of Kriging with External Drift (KED) is a very effective radar-rain gauge rainfall merging technique. However, kriging interpolations assume Gaussianity of the process. Rainfall has a strongly skewed, positive, probability distribution, characterized by a discontinuity due to intermittency. In KED rainfall residuals are used, implicitly calculated as the difference between rain gauge data and a linear function of the radar estimates. Rainfall residuals are non-Gaussian as well. The aim of this work is to evaluate the impact of applying KED to non-Gaussian rainfall residuals, and to assess the best techniques to improve Gaussianity. We compare Box-Cox transformations with λ parameters equal to 0.5, 0.25, and 0.1, Box-Cox with time-variant optimization of λ, normal score transformation, and a singularity analysis technique. The results suggest that Box-Cox with λ = 0.1 and the singularity analysis is not suitable for KED. Normal score transformation and Box-Cox with optimized λ, or λ = 0.25 produce satisfactory results in terms of Gaussianity of the residuals, probability distribution of the merged rainfall products, and rainfall estimate quality, when validated through cross-validation. However, it is observed that Box-Cox transformations are strongly dependent on the temporal and spatial variability of rainfall and on the units used for the rainfall intensity. Overall, applying transformations results in a quantitative improvement of the rainfall estimates only if the correct transformations for the specific data set are used.

Characterizing Dynamic Walking Patterns and Detecting Falls with Wearable Sensors Using Gaussian Process Methods

Directory of Open Access Journals (Sweden)

Taehwan Kim

2017-05-01

Full Text Available By incorporating a growing number of sensors and adopting machine learning technologies, wearable devices have recently become a prominent health care application domain. Among the related research topics in this field, one of the most important issues is detecting falls while walking. Since such falls may lead to serious injuries, automatically and promptly detecting them during daily use of smartphones and/or smart watches is a particular need. In this paper, we investigate the use of Gaussian process (GP methods for characterizing dynamic walking patterns and detecting falls while walking with built-in wearable sensors in smartphones and/or smartwatches. For the task of characterizing dynamic walking patterns in a low-dimensional latent feature space, we propose a novel approach called auto-encoded Gaussian process dynamical model, in which we combine a GP-based state space modeling method with a nonlinear dimensionality reduction method in a unique manner. The Gaussian process methods are fit for this task because one of the most import strengths of the Gaussian process methods is its capability of handling uncertainty in the model parameters. Also for detecting falls while walking, we propose to recycle the latent samples generated in training the auto-encoded Gaussian process dynamical model for GP-based novelty detection, which can lead to an efficient and seamless solution to the detection task. Experimental results show that the combined use of these GP-based methods can yield promising results for characterizing dynamic walking patterns and detecting falls while walking with the wearable sensors.

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

Optimal multicopy asymmetric Gaussian cloning of coherent states

Science.gov (United 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.

A Nonlinear Framework of Delayed Particle Smoothing Method for Vehicle Localization under Non-Gaussian Environment

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.

Information Seeking Behavior in Blind People of Iran : a Survey based on Various Experiences faced by them

Directory of Open Access Journals (Sweden)

Hasan Siamian

2016-12-01

Full Text Available Access to information and its dissemination for the planning of health and social care is essential. While information is not always available as per the needs of the blind people, thus the public libraries and information centers led for meeting the information needs culture and proper knowledge. The study was based on a Descriptive-analytic method in which we included 384 blind people of both the sex selected by the multi-clustering method from 10 provinces of Iran. Health information of the subjects was collect through a researcher-based questionnaire. Results showed that religion, occupations, and access to healthy living, were the first top priority of blind people for meeting information needs in three cultural; social in addition, health forms. The blind people try to look for information on a daily basis and seeking up-to-date technologies. They are most used to audio media rather than any other media to access, utilise information, and rarely used new technologies. Unlike normal people, blind people have high expectations from the library. An attempt has also been taken to present a new model.

Sequence based prediction of DNA-binding proteins based on hybrid feature selection using random forest and Gaussian naïve Bayes.

Directory of Open Access Journals (Sweden)

Wangchao Lou

Full Text Available Developing an efficient method for determination of the DNA-binding proteins, due to their vital roles in gene regulation, is becoming highly desired since it would be invaluable to advance our understanding of protein functions. In this study, we proposed a new method for the prediction of the DNA-binding proteins, by performing the feature rank using random forest and the wrapper-based feature selection using forward best-first search strategy. The features comprise information from primary sequence, predicted secondary structure, predicted relative solvent accessibility, and position specific scoring matrix. The proposed method, called DBPPred, used Gaussian naïve Bayes as the underlying classifier since it outperformed five other classifiers, including decision tree, logistic regression, k-nearest neighbor, support vector machine with polynomial kernel, and support vector machine with radial basis function. As a result, the proposed DBPPred yields the highest average accuracy of 0.791 and average MCC of 0.583 according to the five-fold cross validation with ten runs on the training benchmark dataset PDB594. Subsequently, blind tests on the independent dataset PDB186 by the proposed model trained on the entire PDB594 dataset and by other five existing methods (including iDNA-Prot, DNA-Prot, DNAbinder, DNABIND and DBD-Threader were performed, resulting in that the proposed DBPPred yielded the highest accuracy of 0.769, MCC of 0.538, and AUC of 0.790. The independent tests performed by the proposed DBPPred on completely a large non-DNA binding protein dataset and two RNA binding protein datasets also showed improved or comparable quality when compared with the relevant prediction methods. Moreover, we observed that majority of the selected features by the proposed method are statistically significantly different between the mean feature values of the DNA-binding and the non DNA-binding proteins. All of the experimental results indicate that

Validity of the assumption of Gaussian turbulence; Gyldighed af antagelsen om Gaussisk turbulence

Energy Technology Data Exchange (ETDEWEB)

Nielsen, M.; Hansen, K.S.; Juul Pedersen, B.

2000-07-01

Wind turbines are designed to withstand the impact of turbulent winds, which fluctuations usually are assumed of Gaussian probability distribution. Based on a large number of measurements from many sites, this seems a reasonable assumption in flat homogeneous terrain whereas it may fail in complex terrain. At these sites the wind speed often has a skew distribution with more frequent lulls than gusts. In order to simulate aerodynamic loads, a numerical turbulence simulation method was developed and implemented. This method may simulate multiple time series of variable not necessarily Gaussian distribution without distortion of the spectral distribution or spatial coherence. The simulated time series were used as input to the dynamic-response simulation program Vestas Turbine Simulator (VTS). In this way we simulated the dynamic response of systems exposed to turbulence of either Gaussian or extreme, yet realistic, non-Gaussian probability distribution. Certain loads on turbines with active pitch regulation were enhanced by up to 15% compared to pure Gaussian turbulence. It should, however, be said that the undesired effect depends on the dynamic system, and it might be mitigated by optimisation of the wind turbine regulation system after local turbulence characteristics. (au)

From the second gradient operator and second class of integral theorems to Gaussian or spherical mapping invariants

Institute of Scientific and Technical Information of China (English)

YIN Ya-jun; WU Ji-ye; HUANG Ke-zhi; FAN Qin-shan

2008-01-01

By combining of the second gradient operator, the second class of integral theorems, the Gaussian-curvature-based integral theorems and the Gaussian (or spherical) mapping, a series of invariants or geometric conservation quantities under Gaussian (or spherical) mapping are revealed. From these mapping invariants important transformations between original curved surface and the spherical surface are derived. The potential applications of these invariants and transformations to geometry are discussed.

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).

How Gaussian can our Universe be?

Science.gov (United States)

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).

Zero-biased solar-blind photodetector based on ZnBeMgO/Si heterojunction

International Nuclear Information System (INIS)

Yang, C; Li, X M; Yu, W D; Gao, X D; Cao, X; Li, Y Z

2009-01-01

An n-type Zn 1-x-y Be x Mg y O thin film was deposited on a p-type Si substrate by pulsed laser deposition to obtain a solar-blind photodetector. The spectral response characteristic with a cutoff wavelength of 280 nm was demonstrated to realize the photodetection of the solar-blind wave zone. The responsivity of the device was improved by inserting an Al-doped ZnO (AZO) contact layer, which was expected to enhance the carrier collection efficiency significantly. Correspondingly, the peak responsivity was improved from 0.003 to 0.11 A W -1 at zero bias, and a high external quantum efficiency of 53% at 270 nm was achieved. The fast rise time reached 20 ns. This work demonstrated the possibility of a wurtzite ZnO based oxide system to realize high performance zero-biased solar-blind photodetectors. (fast track communication)

Blind I/Q Signal Separation-Based Solutions for Receiver Signal Processing

Directory of Open Access Journals (Sweden)

Visa Koivunen

2005-09-01

Full Text Available This paper introduces some novel digital signal processing (DSP-based approaches to some of the most fundamental tasks of radio receivers, namely, channel equalization, carrier synchronization, and I/Q mismatch compensation. The leading principle is to show that all these problems can be solved blindly (i.e., without training signals by forcing the I and Q components of the observed data as independent as possible. Blind signal separation (BSS is then introduced as an efficient tool to carry out these tasks, and simulation examples are used to illustrate the performance of the proposed approaches. The main application area of the presented carrier synchronization and I/Q mismatch compensation techniques is in direct-conversion type receivers, while the proposed channel equalization principles basically apply to any radio architecture.

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.

Blindness and severe visual impairment in pupils at schools for the blind in Burundi.

Science.gov (United States)

Ruhagaze, Patrick; Njuguna, Kahaki Kimani Margaret; Kandeke, Lévi; Courtright, Paul

2013-01-01

To determine the causes of childhood blindness and severe visual impairment in pupils attending schools for the blind in Burundi in order to assist planning for services in the country. All pupils attending three schools for the blind in Burundi were examined. A modified WHO/PBL eye examination record form for children with blindness and low vision was used to record the findings. Data was analyzed for those who became blind or severely visually impaired before the age of 16 years. Overall, 117 pupils who became visually impaired before 16 years of age were examined. Of these, 109 (93.2%) were blind or severely visually impaired. The major anatomical cause of blindness or severe visual impairment was cornea pathology/phthisis (23.9%), followed by lens pathology (18.3%), uveal lesions (14.7%) and optic nerve lesions (11.9%). In the majority of pupils with blindness or severe visual impairment, the underlying etiology of visual loss was unknown (74.3%). More than half of the pupils with lens related blindness had not had surgery; among those who had surgery, outcomes were generally poor. The causes identified indicate the importance of continuing preventive public health strategies, as well as the development of specialist pediatric ophthalmic services in the management of childhood blindness in Burundi. The geographic distribution of pupils at the schools for the blind indicates a need for community-based programs to identify and refer children in need of services.

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 processes for machine learning.

Science.gov (United States)

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.

Determination of signal intensity affected by Gaussian noise

International Nuclear Information System (INIS)

Blostein, Jeronimo J.; Bennun, Leonardo

1999-01-01

A methodology based on maximum likelihood criteria, to identify and quantify an arbitrary signal affected by Gaussian noise is shown. To use this methodology it is necessary to know the position in the spectrum where the signal of interest should appear, and the shape of the signal when the background is null or unappreciable. (author)

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...

A Hybrid Technique for Blind Separation of Non-Gaussian and Time-Correlated Sources Using a Multicomponent Approach

Czech Academy of Sciences Publication Activity Database

Tichavský, Petr; Koldovský, Zbyněk; Yeredor, A.; Gómez-Herrero, G.; Doron, E.

2008-01-01

Roč. 19, č. 3 (2008), s. 421-430 ISSN 1045-9227 R&D Projects: GA MŠk 1M0572 Grant - others:GA ČR(CZ) GP102/07/P384 Program:GP Institutional research plan: CEZ:AV0Z10750506 Keywords : blind source separation * independent component analysis Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 3.726, year: 2008

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...

The blind leading the blind: use and misuse of blinding in randomized controlled trials.

Science.gov (United States)

Miller, Larry E; Stewart, Morgan E

2011-03-01

The use of blinding strengthens the credibility of randomized controlled trials (RCTs) by minimizing bias. However, there is confusion surrounding the definition of blinding as well as the terms single, double, and triple blind. It has been suggested that these terms should be discontinued due to their broad misinterpretation. We recommend that, instead of abandoning the use of these terms, explicit definitions of blinding should be adopted. We address herein the concept of blinding, propose standard definitions for the consistent use of these terms, and detail when different types of blinding should be utilized. Standardizing the definition of blinding and utilizing proper blinding methods will improve the quality and clarity of reporting in RCTs. Copyright © 2010 Elsevier Inc. All rights reserved.

Multi-fidelity Gaussian process regression for prediction of random fields

International Nuclear Information System (INIS)

Parussini, L.; Venturi, D.; Perdikaris, P.; Karniadakis, G.E.

2017-01-01

We propose a new multi-fidelity Gaussian process regression (GPR) approach for prediction of random fields based on observations of surrogate models or hierarchies of surrogate models. Our method builds upon recent work on recursive Bayesian techniques, in particular recursive co-kriging, and extends it to vector-valued fields and various types of covariances, including separable and non-separable ones. The framework we propose is general and can be used to perform uncertainty propagation and quantification in model-based simulations, multi-fidelity data fusion, and surrogate-based optimization. We demonstrate the effectiveness of the proposed recursive GPR techniques through various examples. Specifically, we study the stochastic Burgers equation and the stochastic Oberbeck–Boussinesq equations describing natural convection within a square enclosure. In both cases we find that the standard deviation of the Gaussian predictors as well as the absolute errors relative to benchmark stochastic solutions are very small, suggesting that the proposed multi-fidelity GPR approaches can yield highly accurate results.

Multi-fidelity Gaussian process regression for prediction of random fields

Energy Technology Data Exchange (ETDEWEB)

Parussini, L. [Department of Engineering and Architecture, University of Trieste (Italy); Venturi, D., E-mail: venturi@ucsc.edu [Department of Applied Mathematics and Statistics, University of California Santa Cruz (United States); Perdikaris, P. [Department of Mechanical Engineering, Massachusetts Institute of Technology (United States); Karniadakis, G.E. [Division of Applied Mathematics, Brown University (United States)

2017-05-01

We propose a new multi-fidelity Gaussian process regression (GPR) approach for prediction of random fields based on observations of surrogate models or hierarchies of surrogate models. Our method builds upon recent work on recursive Bayesian techniques, in particular recursive co-kriging, and extends it to vector-valued fields and various types of covariances, including separable and non-separable ones. The framework we propose is general and can be used to perform uncertainty propagation and quantification in model-based simulations, multi-fidelity data fusion, and surrogate-based optimization. We demonstrate the effectiveness of the proposed recursive GPR techniques through various examples. Specifically, we study the stochastic Burgers equation and the stochastic Oberbeck–Boussinesq equations describing natural convection within a square enclosure. In both cases we find that the standard deviation of the Gaussian predictors as well as the absolute errors relative to benchmark stochastic solutions are very small, suggesting that the proposed multi-fidelity GPR approaches can yield highly accurate results.

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)

Symplectic invariants, entropic measures and correlations of Gaussian states

Energy Technology Data Exchange (ETDEWEB)

Serafini, Alessio; Illuminati, Fabrizio; Siena, Silvio De [Dipartimento di Fisica ' E R Caianiello' , Universita di Salerno, INFM UdR Salerno, INFN Sezione di Napoli, Gruppo Collegato di Salerno, Via S Allende, 84081 Baronissi, SA (Italy)

2004-01-28

We present a derivation of the Von Neumann entropy and mutual information of arbitrary two-mode Gaussian states, based on the explicit determination of the symplectic eigenvalues of a generic covariance matrix. The key role of the symplectic invariants in such a determination is pointed out. We show that the Von Neumann entropy depends on two symplectic invariants, while the purity (or the linear entropy) is determined by only one invariant, so that the two quantities provide two different hierarchies of mixed Gaussian states. A comparison between mutual information and entanglement of formation for symmetric states is considered, taking note of the crucial role of the symplectic eigenvalues in qualifying and quantifying the correlations present in a generic state. (letter to the editor)

Symplectic invariants, entropic measures and correlations of Gaussian states

International Nuclear Information System (INIS)

Serafini, Alessio; Illuminati, Fabrizio; Siena, Silvio De

2004-01-01

We present a derivation of the Von Neumann entropy and mutual information of arbitrary two-mode Gaussian states, based on the explicit determination of the symplectic eigenvalues of a generic covariance matrix. The key role of the symplectic invariants in such a determination is pointed out. We show that the Von Neumann entropy depends on two symplectic invariants, while the purity (or the linear entropy) is determined by only one invariant, so that the two quantities provide two different hierarchies of mixed Gaussian states. A comparison between mutual information and entanglement of formation for symmetric states is considered, taking note of the crucial role of the symplectic eigenvalues in qualifying and quantifying the correlations present in a generic state. (letter to the editor)

Real-Time EEG Signal Enhancement Using Canonical Correlation Analysis and Gaussian Mixture Clustering

Directory of Open Access Journals (Sweden)

Chin-Teng Lin

2018-01-01

Full Text Available Electroencephalogram (EEG signals are usually contaminated with various artifacts, such as signal associated with muscle activity, eye movement, and body motion, which have a noncerebral origin. The amplitude of such artifacts is larger than that of the electrical activity of the brain, so they mask the cortical signals of interest, resulting in biased analysis and interpretation. Several blind source separation methods have been developed to remove artifacts from the EEG recordings. However, the iterative process for measuring separation within multichannel recordings is computationally intractable. Moreover, manually excluding the artifact components requires a time-consuming offline process. This work proposes a real-time artifact removal algorithm that is based on canonical correlation analysis (CCA, feature extraction, and the Gaussian mixture model (GMM to improve the quality of EEG signals. The CCA was used to decompose EEG signals into components followed by feature extraction to extract representative features and GMM to cluster these features into groups to recognize and remove artifacts. The feasibility of the proposed algorithm was demonstrated by effectively removing artifacts caused by blinks, head/body movement, and chewing from EEG recordings while preserving the temporal and spectral characteristics of the signals that are important to cognitive research.

Calculating emittance for Gaussian and Non-Gaussian distributions by the method of correlations for slits

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

Non-Gaussianity in a quasiclassical electronic circuit

Science.gov (United States)

Suzuki, Takafumi J.; Hayakawa, Hisao

2017-05-01

We study the non-Gaussian dynamics of a quasiclassical electronic circuit coupled to a mesoscopic conductor. Non-Gaussian noise accompanying the nonequilibrium transport through the conductor significantly modifies the stationary probability density function (PDF) of the flux in the dissipative circuit. We incorporate weak quantum fluctuation of the dissipative LC circuit with a stochastic method and evaluate the quantum correction of the stationary PDF. Furthermore, an inverse formula to infer the statistical properties of the non-Gaussian noise from the stationary PDF is derived in the classical-quantum crossover regime. The quantum correction is indispensable to correctly estimate the microscopic transfer events in the QPC with the quasiclassical inverse formula.

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

Science.gov (United States)

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.

A fully connected network of Bernoulli units with correlation learning

Science.gov (United States)

Dente, J. A.; Vilela Mendes, R.

1996-02-01

Biological evidence suggests that pattern recognition and associative memory in the mammalian nervous system operates through the establishment of spatio-temporal patterns of activity and not by the evolution towards an equilibrium point as in attractor neural networks. Information is carried by the space-time correlation of the activity intensities rather than by the details of individual neuron signals. Furthermore the fast recognition times that are achieved with relatively slow biological neurons seem to be associated to the chaotic nature of the basal nervous activity. To copy the biology hardware may not be technologically sound, but to look for inspiration in the efficient biological information processing methods is an idea that deserves consideration. Inspired by the mechanisms at work in the mammalian olfactory system we study a network where, in the absence of external inputs, the units have a dynamics of the Bernoulli shift type. When an external signal is presented, the pattern of excitation bursts depends on the learning history of the network. Association and pattern identification in the network operates by the selection, by the external stimulus, of distinct invariant measures in the chaotic system. The simplicity of the node dynamics, that is chosen, allows a reasonable analytical control of the network behavior.

A Charrelation Matrix-Based Blind Adaptive Detector for DS-CDMA Systems

Directory of Open Access Journals (Sweden)

Zhongqiang Luo

2015-08-01

Full Text Available In this paper, a blind adaptive detector is proposed for blind separation of user signals and blind estimation of spreading sequences in DS-CDMA systems. The blind separation scheme exploits a charrelation matrix for simple computation and effective extraction of information from observation signal samples. The system model of DS-CDMA signals is modeled as a blind separation framework. The unknown user information and spreading sequence of DS-CDMA systems can be estimated only from the sampled observation signals. Theoretical analysis and simulation results show that the improved performance of the proposed algorithm in comparison with the existing conventional algorithms used in DS-CDMA systems. Especially, the proposed scheme is suitable for when the number of observation samples is less and the signal to noise ratio (SNR is low.

A Charrelation Matrix-Based Blind Adaptive Detector for DS-CDMA Systems.

Science.gov (United States)

Luo, Zhongqiang; Zhu, Lidong

2015-08-14

In this paper, a blind adaptive detector is proposed for blind separation of user signals and blind estimation of spreading sequences in DS-CDMA systems. The blind separation scheme exploits a charrelation matrix for simple computation and effective extraction of information from observation signal samples. The system model of DS-CDMA signals is modeled as a blind separation framework. The unknown user information and spreading sequence of DS-CDMA systems can be estimated only from the sampled observation signals. Theoretical analysis and simulation results show that the improved performance of the proposed algorithm in comparison with the existing conventional algorithms used in DS-CDMA systems. Especially, the proposed scheme is suitable for when the number of observation samples is less and the signal to noise ratio (SNR) is low.

Imprint of primordial non-Gaussianity on dark matter halo profiles

Energy Technology Data Exchange (ETDEWEB)

Dizgah, Azadeh Moradinezhad; Dodelson, Scott; Riotto, Antonio

2013-09-01

We study the impact of primordial non-Gaussianity on the density profile of dark matter halos by using the semi-analytical model introduced recently by Dalal {\\it et al.} which relates the peaks of the initial linear density field to the final density profile of dark matter halos. Models with primordial non-Gaussianity typically produce an initial density field that differs from that produced in Gaussian models. We use the path-integral formulation of excursion set theory to calculate the non-Gaussian corrections to the peak profile and derive the statistics of the peaks of non-Gaussian density field. In the context of the semi-analytic model for halo profiles, currently allowed values for primordial non-Gaussianity would increase the shapes of the inner dark matter profiles, but only at the sub-percent level except in the very innermost regions.

On the dependence structure of Gaussian queues

NARCIS (Netherlands)

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

NARCIS (Netherlands)

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

Diffraction of a Gaussian beam in a three-dimensional smoothly inhomogeneous medium: an eikonal-based complex geometrical-optics approach.

Science.gov (United States)

Berczynski, Pawel; Bliokh, Konstantin Yu; Kravtsov, Yuri A; Stateczny, Andrzej

2006-06-01

We present an ab initio account of the paraxial complex geometrical optics (CGO) in application to scalar Gaussian beam propagation and diffraction in a 3D smoothly inhomogeneous medium. The paraxial CGO deals with quadratic expansion of the complex eikonal and reduces the wave problem to the solution of ordinary differential equations of the Riccati type. This substantially simplifies the description of Gaussian beam diffraction as compared with full-wave or parabolic (quasi-optics) equations. For a Gaussian beam propagating in a homogeneous medium or along the symmetry axis in a lenslike medium, the CGO equations possess analytical solutions; otherwise, they can be readily solved numerically. As a nontrivial example we consider Gaussian beam propagation and diffraction along a helical ray in an axially symmetric waveguide medium. It is shown that the major axis of the beam's elliptical cross section grows unboundedly; it is oriented predominantly in the azimuthal (binormal) direction and does not obey the parallel-transport law.

Robust and Effective Component-based Banknote Recognition for the Blind.

Science.gov (United States)

Hasanuzzaman, Faiz M; Yang, Xiaodong; Tian, Yingli

2012-11-01

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

Density Transition Based Self-Focusing of cosh-Gaussian Laser Beam in Plasma with Linear Absorption

International Nuclear Information System (INIS)

Kant, Niti; Wani, Manzoor Ahmad

2015-01-01

Density transition based self-focusing of cosh-Gaussian laser beam in plasma with linear absorption has been studied. The field distribution in the plasma is expressed in terms of beam width parameter, decentered parameter, and linear absorption coefficient. The differential equation for the beam width parameter is solved by following Wentzel–Kramers–Brillouin (WKB) and paraxial approximation through parabolic wave equation approach. The behaviour of beam width parameter with dimensionless distance of propagation is studied at optimum values of plasma density, decentered parameter and with different absorption levels in the medium. The results reveal that these parameters can affect the self-focusing significantly. (paper)

Interference of Multi-Mode Gaussian States and "non Appearance" of Quantum Correlations

Science.gov (United States)

Olivares, Stefano

2012-01-01

We theoretically investigate bilinear, mode-mixing interactions involving two modes of uncorrelated multi-mode Gaussian states. In particular, we introduce the notion of "locally the same states" (LSS) and prove that two uncorrelated LSS modes are invariant under the mode mixing, i.e. the interaction does not lead to the birth of correlations between the outgoing modes. We also study the interference of orthogonally polarized Gaussian states by means of an interferometric scheme based on a beam splitter, rotators of polarization and polarization filters.

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...

Non-Gaussian nature of glassy dynamics by cage to cage motion

International Nuclear Information System (INIS)

Vorselaars, Bart; Lyulin, Alexey V.; Michels, M. A. J.; Karatasos, K.

2007-01-01

A model based on a single Brownian particle moving in a periodic effective field is used to understand the non-Gaussian dynamics in glassy systems of cage escape and subsequent recaging, often thought to be caused by a heterogeneous glass structure. The results are compared to molecular-dynamics simulations of systems with varying complexity: quasi-two-dimensional colloidlike particles, atactic polystyrene, and a dendritic glass. The model nicely describes generic features of all three topologically different systems, in particular around the maximum of the non-Gaussian parameter. This maximum is a measure for the average distance between cages

A Bayesian optimal design for degradation tests based on the inverse Gaussian process

Energy Technology Data Exchange (ETDEWEB)

Peng, Weiwen; Liu, Yu; Li, Yan Feng; Zhu, Shun Peng; Huang, Hong Zhong [University of Electronic Science and Technology of China, Chengdu (China)

2014-10-15

The inverse Gaussian process is recently introduced as an attractive and flexible stochastic process for degradation modeling. This process has been demonstrated as a valuable complement for models that are developed on the basis of the Wiener and gamma processes. We investigate the optimal design of the degradation tests on the basis of the inverse Gaussian process. In addition to an optimal design with pre-estimated planning values of model parameters, we also address the issue of uncertainty in the planning values by using the Bayesian method. An average pre-posterior variance of reliability is used as the optimization criterion. A trade-off between sample size and number of degradation observations is investigated in the degradation test planning. The effects of priors on the optimal designs and on the value of prior information are also investigated and quantified. The degradation test planning of a GaAs Laser device is performed to demonstrate the proposed method.

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

Exploring super-gaussianity towards robust information-theoretical time delay estimation

DEFF Research Database (Denmark)

Petsatodis, Theodoros; Talantzis, Fotios; Boukis, Christos

2013-01-01

the effect upon TDE when modeling the source signal with different speech-based distributions. An information theoretical TDE method indirectly encapsulating higher order statistics (HOS) formed the basis of this work. The underlying assumption of Gaussian distributed source has been replaced...

Implementation and application of the gaussian decomposition software for NaI gamma-ray spectrometry data

International Nuclear Information System (INIS)

Zeng Lihui; Wang Nanping; Tian Gui

2012-01-01

In order to extract the information of peaks in different energy from the data of overlapping peaks in environmental gamma spectrometer, a spectrum data Gaussian decomposition software was designed based on least-square Gaussian fitting method. The interface of this software is friendly, it can complete the decomposition of overlapping peaks in gamma spectrometer quickly by the way of man-machines interactive. The result of field measured data decomposed by this software indicates that the Gaussian decomposition software can efficiently extract 137 Cs spectra from over lapping peaks, which has significance to estimate the human nuclide contamination in the environment. (authors)

Implementation and application of the gaussian decomposition software for NaI gamma-ray spectrometry data

International Nuclear Information System (INIS)

Zeng Lihui; Wang Nanping Tian Gui

2011-01-01

In order to extract the information of peaks in different energy from the data of overlapping peaks in environmental gamma spectrometer, a spectrum data Gaussian decomposition soft is designed based on least- square Gaussian fitting method. The interface of this software is friendly, it can complete the decomposition of overlapping peaks in gamma spectrometer quickly by the way of man-machines interactive. The result that applied gamma spectrometry to data analysis in the field measurement indicates that the Gaussian decomposition soft can efficiently extract 137 Cs from overlapping peaks which has significance to assess the human nuclide contamination of environment. (authors)

A frozen Gaussian approximation-based multi-level particle swarm optimization for seismic inversion

Energy Technology Data Exchange (ETDEWEB)

Li, Jinglai, E-mail: jinglaili@sjtu.edu.cn [Institute of Natural Sciences, Department of Mathematics, and MOE Key Laboratory of Scientific and Engineering Computing, Shanghai Jiao Tong University, Shanghai 200240 (China); Lin, Guang, E-mail: lin491@purdue.edu [Department of Mathematics, School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907 (United States); Computational Sciences and Mathematics Division, Pacific Northwest National Laboratory, Richland, WA 99352 (United States); Yang, Xu, E-mail: xuyang@math.ucsb.edu [Department of Mathematics, University of California, Santa Barbara, CA 93106 (United States)

2015-09-01

In this paper, we propose a frozen Gaussian approximation (FGA)-based multi-level particle swarm optimization (MLPSO) method for seismic inversion of high-frequency wave data. The method addresses two challenges in it: First, the optimization problem is highly non-convex, which makes hard for gradient-based methods to reach global minima. This is tackled by MLPSO which can escape from undesired local minima. Second, the character of high-frequency of seismic waves requires a large number of grid points in direct computational methods, and thus renders an extremely high computational demand on the simulation of each sample in MLPSO. We overcome this difficulty by three steps: First, we use FGA to compute high-frequency wave propagation based on asymptotic analysis on phase plane; Then we design a constrained full waveform inversion problem to prevent the optimization search getting into regions of velocity where FGA is not accurate; Last, we solve the constrained optimization problem by MLPSO that employs FGA solvers with different fidelity. The performance of the proposed method is demonstrated by a two-dimensional full-waveform inversion example of the smoothed Marmousi model.

[Research on fast implementation method of image Gaussian RBF interpolation based on CUDA].

Science.gov (United States)

Chen, Hao; Yu, Haizhong

2014-04-01

Image interpolation is often required during medical image processing and analysis. Although interpolation method based on Gaussian radial basis function (GRBF) has high precision, the long calculation time still limits its application in field of image interpolation. To overcome this problem, a method of two-dimensional and three-dimensional medical image GRBF interpolation based on computing unified device architecture (CUDA) is proposed in this paper. According to single instruction multiple threads (SIMT) executive model of CUDA, various optimizing measures such as coalesced access and shared memory are adopted in this study. To eliminate the edge distortion of image interpolation, natural suture algorithm is utilized in overlapping regions while adopting data space strategy of separating 2D images into blocks or dividing 3D images into sub-volumes. Keeping a high interpolation precision, the 2D and 3D medical image GRBF interpolation achieved great acceleration in each basic computing step. The experiments showed that the operative efficiency of image GRBF interpolation based on CUDA platform was obviously improved compared with CPU calculation. The present method is of a considerable reference value in the application field of image interpolation.

CFHTLenS: a Gaussian likelihood is a sufficient approximation for a cosmological analysis of third-order cosmic shear statistics

Science.gov (United States)

Simon, P.; Semboloni, E.; van Waerbeke, L.; Hoekstra, H.; Erben, T.; Fu, L.; Harnois-Déraps, J.; Heymans, C.; Hildebrandt, H.; Kilbinger, M.; Kitching, T. D.; Miller, L.; Schrabback, T.

2015-05-01

We study the correlations of the shear signal between triplets of sources in the Canada-France-Hawaii Telescope Lensing Survey (CFHTLenS) to probe cosmological parameters via the matter bispectrum. In contrast to previous studies, we adopt a non-Gaussian model of the data likelihood which is supported by our simulations of the survey. We find that for state-of-the-art surveys, similar to CFHTLenS, a Gaussian likelihood analysis is a reasonable approximation, albeit small differences in the parameter constraints are already visible. For future surveys we expect that a Gaussian model becomes inaccurate. Our algorithm for a refined non-Gaussian analysis and data compression is then of great utility especially because it is not much more elaborate if simulated data are available. Applying this algorithm to the third-order correlations of shear alone in a blind analysis, we find a good agreement with the standard cosmological model: Σ _8=σ _8(Ω _m/0.27)^{0.64}=0.79^{+0.08}_{-0.11} for a flat Λ cold dark matter cosmology with h = 0.7 ± 0.04 (68 per cent credible interval). Nevertheless our models provide only moderately good fits as indicated by χ2/dof = 2.9, including a 20 per cent rms uncertainty in the predicted signal amplitude. The models cannot explain a signal drop on scales around 15 arcmin, which may be caused by systematics. It is unclear whether the discrepancy can be fully explained by residual point spread function systematics of which we find evidence at least on scales of a few arcmin. Therefore we need a better understanding of higher order correlations of cosmic shear and their systematics to confidently apply them as cosmological probes.

Efficiency of the human observer for detecting a Gaussian signal at a known location in non-Gaussian distributed lumpy backgrounds.

Science.gov (United States)

Park, Subok; Gallas, Bradon D; Badano, Aldo; Petrick, Nicholas A; Myers, Kyle J

2007-04-01

A previous study [J. Opt. Soc. Am. A22, 3 (2005)] has shown that human efficiency for detecting a Gaussian signal at a known location in non-Gaussian distributed lumpy backgrounds is approximately 4%. This human efficiency is much less than the reported 40% efficiency that has been documented for Gaussian-distributed lumpy backgrounds [J. Opt. Soc. Am. A16, 694 (1999) and J. Opt. Soc. Am. A18, 473 (2001)]. We conducted a psychophysical study with a number of changes, specifically in display-device calibration and data scaling, from the design of the aforementioned study. Human efficiency relative to the ideal observer was found again to be approximately 5%. Our variance analysis indicates that neither scaling nor display made a statistically significant difference in human performance for the task. We conclude that the non-Gaussian distributed lumpy background is a major factor in our low human-efficiency results.

An improved image non-blind image deblurring method based on FoEs

Science.gov (United States)

Zhu, Qidan; Sun, Lei

2013-03-01

Traditional non-blind image deblurring algorithms always use maximum a posterior(MAP). MAP estimates involving natural image priors can reduce the ripples effectively in contrast to maximum likelihood(ML). However, they have been found lacking in terms of restoration performance. Based on this issue, we utilize MAP with KL penalty to replace traditional MAP. We develop an image reconstruction algorithm that minimizes the KL divergence between the reference distribution and the prior distribution. The approximate KL penalty can restrain over-smooth caused by MAP. We use three groups of images and Harris corner detection to prove our method. The experimental results show that our algorithm of non-blind image restoration can effectively reduce the ringing effect and exhibit the state-of-the-art deblurring results.

Accelerated Dimension-Independent Adaptive Metropolis

KAUST Repository

Chen, Yuxin

2016-10-27

This work describes improvements by algorithmic and architectural means to black-box Bayesian inference over high-dimensional parameter spaces. The well-known adaptive Metropolis (AM) algorithm [H. Haario, E. Saksman, and J. Tamminen, Bernoulli, (2001), pp. 223--242] is extended herein to scale asymptotically uniformly with respect to the underlying parameter dimension for Gaussian targets, by respecting the variance of the target. The resulting algorithm, referred to as the dimension-independent adaptive Metropolis (DIAM) algorithm, also shows improved performance with respect to adaptive Metropolis on non-Gaussian targets. This algorithm is further improved, and the possibility of probing high-dimensional (with dimension $d \\\\geq 1000$) targets is enabled, via GPU-accelerated numerical libraries and periodically synchronized concurrent chains (justified a posteriori). Asymptotically in dimension, this GPU implementation exhibits a factor of four improvement versus a competitive CPU-based Intel MKL (math kernel library) parallel version alone. Strong scaling to concurrent chains is exhibited, through a combination of longer time per sample batch (weak scaling) with fewer necessary samples to convergence. The algorithm performance is illustrated on several Gaussian and non-Gaussian target examples, in which the dimension may be in excess of one thousand.

Feedback-Capacity of Degraded Gaussian Vector BC using Directed Information and Concave Envelopes

OpenAIRE

Ramachandran, Viswanathan

2017-01-01

It is known that the capacity region of a two user physically degraded discrete memoryless (DM) broadcastchannel (BC) is not enlarged by feedback. An identical result holds true for a physically degraded Gaussian BC,established later using a variant of the Entropy Power Inequality (EPI). In this paper, we extend the latter resultto a physically degraded Gaussian Vector BC (PD-GVBC). However, the extension is not EPI based, but employs arecent result on the factorization of concave envelopes. ...

Monthly streamflow forecasting based on hidden Markov model and Gaussian Mixture Regression

Science.gov (United States)

Liu, Yongqi; Ye, Lei; Qin, Hui; Hong, Xiaofeng; Ye, Jiajun; Yin, Xingli

2018-06-01

Reliable streamflow forecasts can be highly valuable for water resources planning and management. In this study, we combined a hidden Markov model (HMM) and Gaussian Mixture Regression (GMR) for probabilistic monthly streamflow forecasting. The HMM is initialized using a kernelized K-medoids clustering method, and the Baum-Welch algorithm is then executed to learn the model parameters. GMR derives a conditional probability distribution for the predictand given covariate information, including the antecedent flow at a local station and two surrounding stations. The performance of HMM-GMR was verified based on the mean square error and continuous ranked probability score skill scores. The reliability of the forecasts was assessed by examining the uniformity of the probability integral transform values. The results show that HMM-GMR obtained reasonably high skill scores and the uncertainty spread was appropriate. Different HMM states were assumed to be different climate conditions, which would lead to different types of observed values. We demonstrated that the HMM-GMR approach can handle multimodal and heteroscedastic data.

Symmetries and integrability of a fourth-order Euler-Bernoulli beam equation

International Nuclear Information System (INIS)

Bokhari, Ashfaque H.; Zaman, F. D.; Mahomed, F. M.

2010-01-01

The complete symmetry group classification of the fourth-order Euler-Bernoulli ordinary differential equation, where the elastic modulus and the area moment of inertia are constants and the applied load is a function of the normal displacement, is obtained. We perform the Lie and Noether symmetry analysis of this problem. In the Lie analysis, the principal Lie algebra which is one dimensional extends in four cases, viz. the linear, exponential, general power law, and a negative fractional power law. It is further shown that two cases arise in the Noether classification with respect to the standard Lagrangian. That is, the linear case for which the Noether algebra dimension is one less than the Lie algebra dimension as well as the negative fractional power law. In the latter case the Noether algebra is three dimensional and is isomorphic to the Lie algebra which is sl(2,R). This exceptional case, although admitting the nonsolvable algebra sl(2,R), remarkably allows for a two-parameter family of exact solutions via the Noether integrals. The Lie reduction gives a second-order ordinary differential equation which has nonlocal symmetry.

A Detailed Derivation of Gaussian Orbital-Based Matrix Elements in Electron Structure Calculations

Science.gov (United States)

Petersson, T.; Hellsing, B.

2010-01-01

A detailed derivation of analytic solutions is presented for overlap, kinetic, nuclear attraction and electron repulsion integrals involving Cartesian Gaussian-type orbitals. It is demonstrated how s-type orbitals can be used to evaluate integrals with higher angular momentum via the properties of Hermite polynomials and differentiation with…

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.

Gaussian statistics for palaeomagnetic vectors

Science.gov (United States)

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

Science.gov (United States)

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

Visible-blind and solar-blind ultraviolet photodiodes based on (In{sub x}Ga{sub 1−x}){sub 2}O{sub 3}

Energy Technology Data Exchange (ETDEWEB)

Zhang, Zhipeng; Wenckstern, Holger von; Lenzner, Jörg; Lorenz, Michael; Grundmann, Marius [Institut für Experimentelle Physik II, Fakultät für Physik und Geowissenschaften, Universität Leipzig, Linnéstraße 5, 04103 Leipzig (Germany)

2016-03-21

UV and deep-UV selective photodiodes from visible-blind to solar-blind were realized based on a Si-doped (In{sub x}Ga{sub 1–x}){sub 2}O{sub 3} thin film with a monotonic lateral variation of 0.0035 < x < 0.83. Such layer was deposited by employing a continuous composition spread approach relying on the ablation of a single segmented target in pulsed-laser deposition. The photo response signal is provided from a metal-semiconductor-metal structure upon backside illumination. The absorption onset was tuned from 4.83 to 3.22 eV for increasing x. Higher responsivities were observed for photodiodes fabricated from indium-rich part of the sample, for which an internal gain mechanism could be identified.

Consistency relations for sharp inflationary non-Gaussian features

Energy Technology Data Exchange (ETDEWEB)

Mooij, Sander; Palma, Gonzalo A.; Panotopoulos, Grigoris [Departamento de Física, Facultad de Ciencias Físicas y Matemáticas, Universidad de Chile, Blanco Encalada 2008, Santiago (Chile); Soto, Alex, E-mail: sander.mooij@ing.uchile.cl, E-mail: gpalmaquilod@ing.uchile.cl, E-mail: gpanotop@ing.uchile.cl, E-mail: gatogeno@gmail.com [Departamento de Física, Facultad de Ciencias, Universidad de Chile, Las Palmeras 3425, Ñuñoa, Santiago (Chile)

2016-09-01

If cosmic inflation suffered tiny time-dependent deviations from the slow-roll regime, these would induce the existence of small scale-dependent features imprinted in the primordial spectra, with their shapes and sizes revealing information about the physics that produced them. Small sharp features could be suppressed at the level of the two-point correlation function, making them undetectable in the power spectrum, but could be amplified at the level of the three-point correlation function, offering us a window of opportunity to uncover them in the non-Gaussian bispectrum. In this article, we show that sharp features may be analyzed using only data coming from the three point correlation function parametrizing primordial non-Gaussianity. More precisely, we show that if features appear in a particular non-Gaussian triangle configuration (e.g. equilateral, folded, squeezed), these must reappear in every other configuration according to a specific relation allowing us to correlate features across the non-Gaussian bispectrum. As a result, we offer a method to study scale-dependent features generated during inflation that depends only on data coming from measurements of non-Gaussianity, allowing us to omit data from the power spectrum.

Consistency relations for sharp inflationary non-Gaussian features

International Nuclear Information System (INIS)

Mooij, Sander; Palma, Gonzalo A.; Panotopoulos, Grigoris; Soto, Alex

2016-01-01

If cosmic inflation suffered tiny time-dependent deviations from the slow-roll regime, these would induce the existence of small scale-dependent features imprinted in the primordial spectra, with their shapes and sizes revealing information about the physics that produced them. Small sharp features could be suppressed at the level of the two-point correlation function, making them undetectable in the power spectrum, but could be amplified at the level of the three-point correlation function, offering us a window of opportunity to uncover them in the non-Gaussian bispectrum. In this article, we show that sharp features may be analyzed using only data coming from the three point correlation function parametrizing primordial non-Gaussianity. More precisely, we show that if features appear in a particular non-Gaussian triangle configuration (e.g. equilateral, folded, squeezed), these must reappear in every other configuration according to a specific relation allowing us to correlate features across the non-Gaussian bispectrum. As a result, we offer a method to study scale-dependent features generated during inflation that depends only on data coming from measurements of non-Gaussianity, allowing us to omit data from the power spectrum.

High-dimensional orbital angular momentum entanglement concentration based on Laguerre–Gaussian mode selection

International Nuclear Information System (INIS)

Zhang, Wuhong; Su, Ming; Wu, Ziwen; Lu, Meng; Huang, Bingwei; Chen, Lixiang

2013-01-01

Twisted photons enable the definition of a Hilbert space beyond two dimensions by orbital angular momentum (OAM) eigenstates. Here we propose a feasible entanglement concentration experiment, to enhance the quality of high-dimensional entanglement shared by twisted photon pairs. Our approach is started from the full characterization of entangled spiral bandwidth, and is then based on the careful selection of the Laguerre–Gaussian (LG) modes with specific radial and azimuthal indices p and ℓ. In particular, we demonstrate the possibility of high-dimensional entanglement concentration residing in the OAM subspace of up to 21 dimensions. By means of LabVIEW simulations with spatial light modulators, we show that the Shannon dimensionality could be employed to quantify the quality of the present concentration. Our scheme holds promise in quantum information applications defined in high-dimensional Hilbert space. (letter)

Sharing the Licensed Spectrum of Full-Duplex Systems Using Improper Gaussian Signaling

KAUST Repository

Gaafar, Mohamed

2015-12-01

Sharing the spectrum with in-band full-duplex (FD) primary users (PU) is a challenging and interesting problem in the underlay cognitive radio (CR) systems. The self-interference introduced at the primary network may dramatically impede the secondary user (SU) opportunity to access the spectrum. In this work, we attempt to tackle this problem through the use of the so-called improper Gaussian signaling. Such a signaling technique has demonstrated its superiority in improving the overall performance in interference limited networks. Particularly, we assume a system with a SU pair working in half-duplex mode that uses improper Gaussian signaling while the FD PU pair implements the regular proper Gaussian signaling techniques. First, we derive a closed form expression for the SU outage probability and an upper bound for the PU outage probability. Then, we optimize the SU signal parameters to minimize its outage probability while maintaining the required PU quality-of-service based on the average channel state information. Finally, we provide some numerical results that validate the tightness of the PU outage probability bound and demonstrate the advantage of employing the improper Gaussian signaling to the SU in order to access the spectrum of the FD PU.

Prediction error variance and expected response to selection, when selection is based on the best predictor - for Gaussian and threshold characters, traits following a Poisson mixed model and survival traits

DEFF Research Database (Denmark)

Andersen, Anders Holst; Korsgaard, Inge Riis; Jensen, Just

2002-01-01

In this paper, we consider selection based on the best predictor of animal additive genetic values in Gaussian linear mixed models, threshold models, Poisson mixed models, and log normal frailty models for survival data (including models with time-dependent covariates with associated fixed...... or random effects). In the different models, expressions are given (when these can be found - otherwise unbiased estimates are given) for prediction error variance, accuracy of selection and expected response to selection on the additive genetic scale and on the observed scale. The expressions given for non...... Gaussian traits are generalisations of the well-known formulas for Gaussian traits - and reflect, for Poisson mixed models and frailty models for survival data, the hierarchal structure of the models. In general the ratio of the additive genetic variance to the total variance in the Gaussian part...

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

An Efficient Seam Elimination Method for UAV Images Based on Wallis Dodging and Gaussian Distance Weight Enhancement.

Science.gov (United States)

Tian, Jinyan; Li, Xiaojuan; Duan, Fuzhou; Wang, Junqian; Ou, Yang

2016-05-10

The rapid development of Unmanned Aerial Vehicle (UAV) remote sensing conforms to the increasing demand for the low-altitude very high resolution (VHR) image data. However, high processing speed of massive UAV data has become an indispensable prerequisite for its applications in various industry sectors. In this paper, we developed an effective and efficient seam elimination approach for UAV images based on Wallis dodging and Gaussian distance weight enhancement (WD-GDWE). The method encompasses two major steps: first, Wallis dodging was introduced to adjust the difference of brightness between the two matched images, and the parameters in the algorithm were derived in this study. Second, a Gaussian distance weight distribution method was proposed to fuse the two matched images in the overlap region based on the theory of the First Law of Geography, which can share the partial dislocation in the seam to the whole overlap region with an effect of smooth transition. This method was validated at a study site located in Hanwang (Sichuan, China) which was a seriously damaged area in the 12 May 2008 enchuan Earthquake. Then, a performance comparison between WD-GDWE and the other five classical seam elimination algorithms in the aspect of efficiency and effectiveness was conducted. Results showed that WD-GDWE is not only efficient, but also has a satisfactory effectiveness. This method is promising in advancing the applications in UAV industry especially in emergency situations.

XDGMM: eXtreme Deconvolution Gaussian Mixture Modeling

Science.gov (United States)

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.

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).

Sufficient Condition for Monotonicity in Constructing the Distribution Function With Bernoulli Scheme

Directory of Open Access Journals (Sweden)

Vedenyapin Aleksandr Dmitrievich

2015-11-01

Full Text Available This paper is the construction of the distribution function using the Bernoulli scheme, and is also designed to correct some of the mistakes that were made in the article [2]. Namely, a function built in [2] need not be monotonous, and some formulas need to be adjusted. The idea of building as well as in [2], is based on the model of Cox-Ross-Rubinstein "binary market". The essence of the model was to divide time into N steps, and assuming that the price of an asset at each step can move either up to a certain value with probability p, or down also by some certain value with probability q = 1 - p. Prices in step N can take only a finite number of values. "Success" or "failure" was the changing price for some fixed value in the model of Cox-Ross-Rubinstein. Here as a "success" or "failure" at every step we consider the affiliation of changing the index value to the section [r, S] either to the interval [I, r. Further a function P(r was introduced, which at any step gives us the probability of "success". The maximum index value increase for the all period of time [T, 2T] will be equal nS, and the maximum possible reduction will be equal nI. Then let x ∈ [nI, nS]. This segment will reflect every possible total variation that we can get at the end of a period of time [T, 2T]. The further introduced inequality k ≥ (x - nI/(S - I gives us the minimum number of successes that needed for total changing could be in the section [x, nS] if was n - k reductions with the index value to I. Then was introduced the function r(x, kmin which is defined on the interval (nI, nS] and provided us some assurance that the total index changing could be in the section [x, nS] if successful interval is [r(x, kmin, S] and the amount of success is satisfying to our inequality. The probability of k "successes" and n - k "failures" is calculated according to the formula of Bernoulli, where the probability of "success" is determined by the function P(r, and r is determined

Privacy Preserving Similarity Based Text Retrieval through Blind Storage

Directory of Open Access Journals (Sweden)

Pinki Kumari

2016-09-01

Full Text Available Cloud computing is improving rapidly due to their more advantage and more data owners give interest to outsource their data into cloud storage for centralize their data. As huge files stored in the cloud storage, there is need to implement the keyword based search process to data user. At the same time to protect the privacy of data, encryption techniques are used for sensitive data, that encryption is done before outsourcing data to cloud server. But it is critical to search results in encryption data. In this system we propose similarity text retrieval from the blind storage blocks with encryption format. This system provides more security because of blind storage system. In blind storage system data is stored randomly on cloud storage.  In Existing Data Owner cannot encrypt the document data as it was done only at server end. Everyone can access the data as there was no private key concept applied to maintained privacy of the data. But In our proposed system, Data Owner can encrypt the data himself using RSA algorithm.  RSA is a public key-cryptosystem and it is widely used for sensitive data storage over Internet. In our system we use Text mining process for identifying the index files of user documents. Before encryption we also use NLP (Nature Language Processing technique to identify the keyword synonyms of data owner document. Here text mining process examines text word by word and collect literal meaning beyond the words group that composes the sentence. Those words are examined in API of word net so that only equivalent words can be identified for index file use. Our proposed system provides more secure and authorized way of recover the text in cloud storage with access control. Finally, our experimental result shows that our system is better than existing.

Occupant satisfaction with two blind control strategies

DEFF Research Database (Denmark)

Karlsen, Line Røseth; Heiselberg, Per Kvols; Bryn, Ida

2015-01-01

Highlights •Occupant satisfaction with two blind control strategies has been studied. •Control based on cut-off position of slats was more popular than closed slats. •Results from the study are helpful in development of control strategies for blinds. •The results give indications of how blinds...

Gaussian Filtering with Tapered Oil-Filled Photonic Bandgap Fibers

DEFF Research Database (Denmark)

Brunetti, Anna Chiara; Scolari, Lara; Weirich, Johannes

2008-01-01

A tunable Gaussian filter based on a tapered oil-filled photonic crystal fiber is demonstrated. The filter is centered at X=1364nm with a bandwidth (FWHM) of 237nm. Tunability is achieved by changing the temperature of the filter. A shift of 210nm of the central wavelength has been observed...

Blind source separation dependent component analysis

CERN Document Server

Xiang, Yong; Yang, Zuyuan

2015-01-01

This book provides readers a complete and self-contained set of knowledge about dependent source separation, including the latest development in this field. The book gives an overview on blind source separation where three promising blind separation techniques that can tackle mutually correlated sources are presented. The book further focuses on the non-negativity based methods, the time-frequency analysis based methods, and the pre-coding based methods, respectively.

Feature Matching for SAR and Optical Images Based on Gaussian-Gamma-shaped Edge Strength Map

Directory of Open Access Journals (Sweden)

CHEN Min

2016-03-01

Full Text Available A matching method for SAR and optical images, robust to pixel noise and nonlinear grayscale differences, is presented. Firstly, a rough correction to eliminate rotation and scale change between images is performed. Secondly, features robust to speckle noise of SAR image are detected by improving the original phase congruency based method. Then, feature descriptors are constructed on the Gaussian-Gamma-shaped edge strength map according to the histogram of oriented gradient pattern. Finally, descriptor similarity and geometrical relationship are combined to constrain the matching processing.The experimental results demonstrate that the proposed method provides significant improvement in correct matches number and image registration accuracy compared with other traditional methods.

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.

Science.gov (United States)

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.

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.

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.).

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.

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

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

Enhancement of force patterns classification based on Gaussian distributions.

Science.gov (United States)

Ertelt, Thomas; Solomonovs, Ilja; Gronwald, Thomas

2018-01-23

Description of the patterns of ground reaction force is a standard method in areas such as medicine, biomechanics and robotics. The fundamental parameter is the time course of the force, which is classified visually in particular in the field of clinical diagnostics. Here, the knowledge and experience of the diagnostician is relevant for its assessment. For an objective and valid discrimination of the ground reaction force pattern, a generic method, especially in the medical field, is absolutely necessary to describe the qualities of the time-course. The aim of the presented method was to combine the approaches of two existing procedures from the fields of machine learning and the Gauss approximation in order to take advantages of both methods for the classification of ground reaction force patterns. The current limitations of both methods could be eliminated by an overarching method. Twenty-nine male athletes from different sports were examined. Each participant was given the task of performing a one-legged stopping maneuver on a force plate from the maximum possible starting speed. The individual time course of the ground reaction force of each subject was registered and approximated on the basis of eight Gaussian distributions. The descriptive coefficients were then classified using Bayesian regulated neural networks. The different sports served as the distinguishing feature. Although the athletes were all given the same task, all sports referred to a different quality in the time course of ground reaction force. Meanwhile within each sport, the athletes were homogeneous. With an overall prediction (R = 0.938) all subjects/sports were classified correctly with 94.29% accuracy. The combination of the two methods: the mathematical description of the time course of ground reaction forces on the basis of Gaussian distributions and their classification by means of Bayesian regulated neural networks, seems an adequate and promising method to discriminate the

Comparison of non-Gaussian and Gaussian diffusion models of diffusion weighted imaging of rectal cancer at 3.0 T MRI.

Science.gov (United States)

Zhang, Guangwen; Wang, Shuangshuang; Wen, Didi; Zhang, Jing; Wei, Xiaocheng; Ma, Wanling; Zhao, Weiwei; Wang, Mian; Wu, Guosheng; Zhang, Jinsong

2016-12-09

Water molecular diffusion in vivo tissue is much more complicated. We aimed to compare non-Gaussian diffusion models of diffusion-weighted imaging (DWI) including intra-voxel incoherent motion (IVIM), stretched-exponential model (SEM) and Gaussian diffusion model at 3.0 T MRI in patients with rectal cancer, and to determine the optimal model for investigating the water diffusion properties and characterization of rectal carcinoma. Fifty-nine consecutive patients with pathologically confirmed rectal adenocarcinoma underwent DWI with 16 b-values at a 3.0 T MRI system. DWI signals were fitted to the mono-exponential and non-Gaussian diffusion models (IVIM-mono, IVIM-bi and SEM) on primary tumor and adjacent normal rectal tissue. Parameters of standard apparent diffusion coefficient (ADC), slow- and fast-ADC, fraction of fast ADC (f), α value and distributed diffusion coefficient (DDC) were generated and compared between the tumor and normal tissues. The SEM exhibited the best fitting results of actual DWI signal in rectal cancer and the normal rectal wall (R 2  = 0.998, 0.999 respectively). The DDC achieved relatively high area under the curve (AUC = 0.980) in differentiating tumor from normal rectal wall. Non-Gaussian diffusion models could assess tissue properties more accurately than the ADC derived Gaussian diffusion model. SEM may be used as a potential optimal model for characterization of rectal cancer.

Continuous-variable entanglement distillation of non-Gaussian mixed states

International Nuclear Information System (INIS)

Dong Ruifang; Lassen, Mikael; Heersink, Joel; Marquardt, Christoph; Leuchs, Gerd; Filip, Radim; Andersen, Ulrik L.

2010-01-01

Many different quantum-information communication protocols such as teleportation, dense coding, and entanglement-based quantum key distribution are based on the faithful transmission of entanglement between distant location in an optical network. The distribution of entanglement in such a network is, however, hampered by loss and noise that is inherent in all practical quantum channels. Thus, to enable faithful transmission one must resort to the protocol of entanglement distillation. In this paper we present a detailed theoretical analysis and an experimental realization of continuous variable entanglement distillation in a channel that is inflicted by different kinds of non-Gaussian noise. The continuous variable entangled states are generated by exploiting the third order nonlinearity in optical fibers, and the states are sent through a free-space laboratory channel in which the losses are altered to simulate a free-space atmospheric channel with varying losses. We use linear optical components, homodyne measurements, and classical communication to distill the entanglement, and we find that by using this method the entanglement can be probabilistically increased for some specific non-Gaussian noise channels.

Unbiased free energy estimates in fast nonequilibrium transformations using Gaussian mixtures

International Nuclear Information System (INIS)

Procacci, Piero

2015-01-01

In this paper, we present an improved method for obtaining unbiased estimates of the free energy difference between two thermodynamic states using the work distribution measured in nonequilibrium driven experiments connecting these states. The method is based on the assumption that any observed work distribution is given by a mixture of Gaussian distributions, whose normal components are identical in either direction of the nonequilibrium process, with weights regulated by the Crooks theorem. Using the prototypical example for the driven unfolding/folding of deca-alanine, we show that the predicted behavior of the forward and reverse work distributions, assuming a combination of only two Gaussian components with Crooks derived weights, explains surprisingly well the striking asymmetry in the observed distributions at fast pulling speeds. The proposed methodology opens the way for a perfectly parallel implementation of Jarzynski-based free energy calculations in complex systems

Log Gaussian Cox processes on the sphere