Inseparability of photon-added Gaussian states
Li Hongrong; Li Fuli; Zhu Shiyao
2007-06-15
The inseparability of photon-added Gaussian states which are generated from two-mode Gaussian states by adding photons is investigated. According to the established inseparability conditions [New J. Phys. 7, 211 (2005); Phys. Rev. Lett. 96, 050503 (2006)], we find that even if a two-mode Gaussian state is separable, the photon-added Gaussian state becomes entangled when the purity of the Gaussian state is larger than a certain value. The lower bound of entanglement of symmetric photon-added Gaussian states is derived. The result shows that entanglement of the photon-added Gaussian states is involved with high-order moment correlations. We find that fidelity of teleporting coherent states cannot be raised by employing the photon-added Gaussian states as a quantum channel of teleportation.
A Gaussian measure of quantum phase noise
NASA Technical Reports Server (NTRS)
Schleich, Wolfgang P.; Dowling, Jonathan P.
1992-01-01
We study the width of the semiclassical phase distribution of a quantum state in its dependence on the average number of photons (m) in this state. As a measure of phase noise, we choose the width, delta phi, of the best Gaussian approximation to the dominant peak of this probability curve. For a coherent state, this width decreases with the square root of (m), whereas for a truncated phase state it decreases linearly with increasing (m). For an optimal phase state, delta phi decreases exponentially but so does the area caught underneath the peak: all the probability is stored in the broad wings of the distribution.
The properties of the anti-tumor model with coupling non-Gaussian noise and Gaussian colored noise
NASA Astrophysics Data System (ADS)
Guo, Qin; Sun, Zhongkui; Xu, Wei
2016-05-01
The anti-tumor model with correlation between multiplicative non-Gaussian noise and additive Gaussian-colored noise has been investigated in this paper. The behaviors of the stationary probability distribution demonstrate that the multiplicative non-Gaussian noise plays a dual role in the development of tumor and an appropriate additive Gaussian colored noise can lead to a minimum of the mean value of tumor cell population. The mean first passage time is calculated to quantify the effects of noises on the transition time of tumors between the stable states. An increase in both the non-Gaussian noise intensity and the departure from the Gaussian noise can accelerate the transition from the disease state to the healthy state. On the contrary, an increase in cross-correlated degree will slow down the transition. Moreover, the correlation time can enhance the stability of the disease state.
Fractional Gaussian noise, functional MRI and Alzheimer's disease.
Maxim, Voichiţa; Sendur, Levent; Fadili, Jalal; Suckling, John; Gould, Rebecca; Howard, Rob; Bullmore, Ed
2005-03-01
Fractional Gaussian noise (fGn) provides a parsimonious model for stationary increments of a self-similar process parameterised by the Hurst exponent, H, and variance, sigma2. Fractional Gaussian noise with H < 0.5 demonstrates negatively autocorrelated or antipersistent behaviour; fGn with H > 0.5 demonstrates 1/f, long memory or persistent behaviour; and the special case of fGn with H = 0.5 corresponds to classical Gaussian white noise. We comparatively evaluate four possible estimators of fGn parameters, one method implemented in the time domain and three in the wavelet domain. We show that a wavelet-based maximum likelihood (ML) estimator yields the most efficient estimates of H and sigma2 in simulated fGn with 0 < H < 1. Applying this estimator to fMRI data acquired in the "resting" state from healthy young and older volunteers, we show empirically that fGn provides an accommodating model for diverse species of fMRI noise, assuming adequate preprocessing to correct effects of head movement, and that voxels with H > 0.5 tend to be concentrated in cortex whereas voxels with H < 0.5 are more frequently located in ventricles and sulcal CSF. The wavelet-ML estimator can be generalised to estimate the parameter vector beta for general linear modelling (GLM) of a physiological response to experimental stimulation and we demonstrate nominal type I error control in multiple testing of beta, divided by its standard error, in simulated and biological data under the null hypothesis beta = 0. We illustrate these methods principally by showing that there are significant differences between patients with early Alzheimer's disease (AD) and age-matched comparison subjects in the persistence of fGn in the medial and lateral temporal lobes, insula, dorsal cingulate/medial premotor cortex, and left pre- and postcentral gyrus: patients with AD had greater persistence of resting fMRI noise (larger H) in these regions. Comparable abnormalities in the AD patients were also identified
Characterization of non-Gaussianity in gravitational wave detector noise
NASA Astrophysics Data System (ADS)
Yamamoto, Takahiro; Hayama, Kazuhiro; Mano, Shuhei; Itoh, Yousuke; Kanda, Nobuyuki
2016-04-01
The first detection of a gravitational wave (GW) has been achieved by two detectors of the advanced LIGO. Routine detections of GW events from various GW sources are expected in the coming decades. Although the first signal was statistically significant, we expect to see numerous low signal-to-noise ratio (SNR) events with which we may be able to learn various aspects of the Universe that have yet to be unveiled. On the other hand, instrumental glitches due to nonstationarity and/or a non-Gaussian tail of detector noise distribution prevent us from confidently identifying true but low SNR GW signals out of instrumental noise. Thus, to make the best use of data from GW detectors, it is important to establish a method to safely distinguish true GW signals from false signals due to instrumental noises. For this purpose, we urgently need to understand characteristics of detector noises, since the nonstationarity and non-Gaussianity inherent in detector outputs are known to increase false detections of signals. Focusing on identifying the non-Gaussian noise components, this paper introduces a new measure for characterizing the non-Gaussian noise components using the parameter ν which characterizes the weight of tail in a Student-t distribution. A confidence interval is reported on the extent to which detector noise deviates from Gaussianity. Our method revealed stationary and transient deterioration of Gaussianity in LIGO S5 data.
Information Content in Uniformly Discretized Gaussian Noise:. Optimal Compression Rates
NASA Astrophysics Data System (ADS)
Romeo, August; Gaztañaga, Enrique; Barriga, Jose; Elizalde, Emilio
We approach the theoretical problem of compressing a signal dominated by Gaussian noise. We present expressions for the compression ratio which can be reached, under the light of Shannon's noiseless coding theorem, for a linearly quantized stochastic Gaussian signal (noise). The compression ratio decreases logarithmically with the amplitude of the frequency spectrum P(f) of the noise. Entropy values and compression rates are shown to depend on the shape of this power spectrum, given different normalizations. The cases of white noise (w.n.), fnp power-law noise (including 1/f noise), (w.n.+1/f) noise, and piecewise (w.n.+1/f | w.n.+1/f2) noise are discussed, while quantitative behaviors and useful approximations are provided.
Effects of non-Gaussian noise near supercritical Hopf bifurcation
NASA Astrophysics Data System (ADS)
Zhang, Ruiting; Hou, Zhonghuai; Xin, Houwen
2011-01-01
We have studied the effects of non-Gaussian colored noise in a chemical oscillation system, the well-known Brusselator model, in the parameter region close to the supercritical Hopf bifurcation. With the variation of the parameter q, which quantifies the deviation from Gaussian character, the signal-to-noise ratio of noise induced oscillation exhibits a bell-shaped change, indicating the presence of resonant activity. The cooperative effects of q and the correlation time τ on the performance of noise induced oscillation are also investigated. Interestingly, resonance-like behavior can be induced by either q or τ when the other parameter is properly fixed. Stochastic normal form theory is used to analyze these nontrivial effects and the simulation results are well reproduced. This work provides us comprehensive understanding of how non-Gaussian noise influences the dynamics in chemical oscillation systems.
Continuous-variable quantum key distribution with Gaussian source noise
Shen Yujie; Peng Xiang; Yang Jian; Guo Hong
2011-05-15
Source noise affects the security of continuous-variable quantum key distribution (CV QKD) and is difficult to analyze. We propose a model to characterize Gaussian source noise through introducing a neutral party (Fred) who induces the noise with a general unitary transformation. Without knowing Fred's exact state, we derive the security bounds for both reverse and direct reconciliations and show that the bound for reverse reconciliation is tight.
Making tensor factorizations robust to non-gaussian noise.
Chi, Eric C.; 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).
Ballistic diffusion induced by non-Gaussian noise
NASA Astrophysics Data System (ADS)
Qin, Li; Li, Qiang
2013-03-01
In this letter, we have analyzed the diffusive behavior of a Brownian particle subject to both internal Gaussian thermal and external non-Gaussian noise sources. We discuss two time correlation functions C(t) of the non-Gaussian stochastic process, and find that they depend on the parameter q, indicating the departure of the non-Gaussian noise from Gaussian behavior: for q <= 1, C(t) is fitted very well by the first-order exponentially decaying curve and approaches zero in the long-time limit, whereas for q > 1, C(t) can be approximated by a second-order exponentially decaying function and converges to a non-zero constant. Due to the properties of C(t), the particle exhibits a normal diffusion for q <= 1, while for q > 1 the non-Gaussian noise induces a ballistic diffusion, i.e., the long-time mean square displacement of the free particle reads <[x(t) -
Comparison of Bistable Systems and Matched Filters in Non-Gaussian Noise
NASA Astrophysics Data System (ADS)
Zhang, Xinming; Yan, Jianfeng; Duan, Fabing
2016-10-01
In this paper, we report that for a weak signal buried in the heavy-tailed noise, the bistable system can outperform the matched filter, yielding a higher output signal-to-noise ratio (SNR) or a lower probability of error. Moreover, by adding mutually independent internal noise components to an array of bistable systems, the output SNR or the probability of error can be further improved via the mechanism of stochastic resonance (SR). These comparison results demonstrate the potential capability of bistable systems for detecting weak signals in non-Gaussian noise environments.
Design and implementation of an optical Gaussian noise generator
NASA Astrophysics Data System (ADS)
Za~O, Leonardo; Loss, Gustavo; Coelho, Rosângela
2009-08-01
A design of a fast and accurate optical Gaussian noise generator is proposed and demonstrated. The noise sample generation is based on the Box-Muller algorithm. The functions implementation was performed on a high-speed Altera Stratix EP1S25 field-programmable gate array (FPGA) development kit. It enabled the generation of 150 million 16-bit noise samples per second. The Gaussian noise generator required only 7.4% of the FPGA logic elements, 1.2% of the RAM memory, 0.04% of the ROM memory, and a laser source. The optical pulses were generated by a laser source externally modulated by the data bit samples using the frequency-shift keying technique. The accuracy of the noise samples was evaluated for different sequences size and confidence intervals. The noise sample pattern was validated by the Bhattacharyya distance (Bd) and the autocorrelation function. The results showed that the proposed design of the optical Gaussian noise generator is very promising to evaluate the performance of optical communications channels with very low bit-error-rate values.
Qubit Noise Spectroscopy for Non-Gaussian Dephasing Environments.
Norris, Leigh M; Paz-Silva, Gerardo A; Viola, Lorenza
2016-04-15
We introduce open-loop quantum control protocols for characterizing the spectral properties of non-Gaussian noise, applicable to both classical and quantum dephasing environments. By engineering a multidimensional frequency comb via repetition of suitably designed pulse sequences, the desired high-order spectra may be related to observable properties of the qubit probe. We prove that access to a high time resolution is key to achieving spectral reconstruction over an extended bandwidth, overcoming the limitations of existing schemes. Non-Gaussian spectroscopy is demonstrated for a classical noise model describing quadratic dephasing at an optimal point, as well as a quantum spin-boson model out of equilibrium. In both cases, we obtain spectral reconstructions that accurately predict the qubit dynamics in the non-Gaussian regime.
Stochastic Schroedinger equations with general complex Gaussian noises
Bassi, Angelo
2003-06-01
Within the framework of non-Markovian stochastic Schroedinger equations, we generalize the results of [W. T. Strunz, Phys. Lett. A 224, 25 (1996)] to the case of general complex Gaussian noises; we analyze the two important cases of purely real and purely imaginary stochastic processes.
Cochlear toughening, protection, and potentiation of noise-induced trauma by non-Gaussian noise
NASA Astrophysics Data System (ADS)
Hamernik, Roger P.; Qiu, Wei; Davis, Bob
2003-02-01
An interrupted noise exposure of sufficient intensity, presented on a daily repeating cycle, produces a threshold shift (TS) following the first day of exposure. TSs measured on subsequent days of the exposure sequence have been shown to decrease relative to the initial TS. This reduction of TS, despite the continuing daily exposure regime, has been called a cochlear toughening effect and the exposures referred to as toughening exposures. Four groups of chinchillas were exposed to one of four different noises presented on an interrupted (6 h/day for 20 days) or noninterrupted (24 h/day for 5 days) schedule. The exposures had equivalent total energy, an overall level of 100 dB(A) SPL, and approximately the same flat, broadband long-term spectrum. The noises differed primarily in their temporal structures; two were Gaussian and two were non-Gausssian, nonstationary. Brainstem auditory evoked potentials were used to estimate hearing thresholds and surface preparation histology was used to determine sensory cell loss. The experimental results presented here show that: (1) Exposures to interrupted high-level, non-Gaussian signals produce a toughening effect comparable to that produced by an equivalent interrupted Gaussian noise. (2) Toughening, whether produced by Gaussian or non-Gaussian noise, results in reduced trauma compared to the equivalent uninterrupted noise, and (3) that both continuous and interrupted non-Gaussian exposures produce more trauma than do energy and spectrally equivalent Gaussian noises. Over the course of the 20-day exposure, the pattern of TS following each day's exposure could exhibit a variety of configurations. These results do not support the equal energy hypothesis as a unifying principal for estimating the potential of a noise exposure to produce hearing loss.
Absolute judgment for one- and two-dimensional stimuli embedded in Gaussian noise
NASA Technical Reports Server (NTRS)
Kvalseth, T. O.
1977-01-01
This study examines the effect on human performance of adding Gaussian noise or disturbance to the stimuli in absolute judgment tasks involving both one- and two-dimensional stimuli. For each selected stimulus value (both an X-value and a Y-value were generated in the two-dimensional case), 10 values (or 10 pairs of values in the two-dimensional case) were generated from a zero-mean Gaussian variate, added to the selected stimulus value and then served as the coordinate values for the 10 points that were displayed sequentially on a CRT. The results show that human performance, in terms of the information transmitted and rms error as functions of stimulus uncertainty, was significantly reduced as the noise variance increased.
Gaussian white noise analysis and its application to Feynman path integral
NASA Astrophysics Data System (ADS)
Suryawan, Herry Pribawanto
2016-02-01
In applied science, Gaussian white noise (the time derivative of Brownian motion) is often chosen as a mathematical idealization of phenomena involving sudden and extremely large fluctuations. It is also possible to define and study Gaussian white noise in a mathematically rigorous framework. In this survey paper we review the Gaussian white noise as an object in an infinite dimensional topological vector space. A brief construction of Gaussian white noise space and Gaussian white noise distributions will be presented. Gaussian white noise analysis provides a framework which offers various generalization of concept known from finite dimensional analysis to the infinite dimensional case, among them are differential operators, Fourier transform, and distribution theory. We will also present some recent developments and results on the application of Gaussian white noise theory to Feynman's path integral approach for quantum mechanics.
Response of MDOF strongly nonlinear systems to fractional Gaussian noises.
Deng, Mao-Lin; Zhu, Wei-Qiu
2016-08-01
In the present paper, multi-degree-of-freedom strongly nonlinear systems are modeled as quasi-Hamiltonian systems and the stochastic averaging method for quasi-Hamiltonian systems (including quasi-non-integrable, completely integrable and non-resonant, completely integrable and resonant, partially integrable and non-resonant, and partially integrable and resonant Hamiltonian systems) driven by fractional Gaussian noise is introduced. The averaged fractional stochastic differential equations (SDEs) are derived. The simulation results for some examples show that the averaged SDEs can be used to predict the response of the original systems and the simulation time for the averaged SDEs is less than that for the original systems. PMID:27586630
Fractional Brownian motion, fractional Gaussian noise, and Tsallis permutation entropy
NASA Astrophysics Data System (ADS)
Zunino, L.; Pérez, D. G.; Kowalski, A.; Martín, M. T.; Garavaglia, M.; Plastino, A.; Rosso, O. A.
2008-10-01
In this work, we analyze two important stochastic processes, the fractional Brownian motion and fractional Gaussian noise, within the framework of the Tsallis permutation entropy. This entropic measure, evaluated after using the Bandt & Pompe method to extract the associated probability distribution, is shown to be a powerful tool to characterize fractal stochastic processes. It allows for a better discrimination of the processes than the Shannon counterpart for appropriate ranges of values of the entropic index. Moreover, we find the optimum value of this entropic index for the stochastic processes under study.
NASA Astrophysics Data System (ADS)
Guo, Yongfeng; Shen, Yajun; Tan, Jianguo
2016-09-01
The phenomenon of stochastic resonance (SR) in a piecewise nonlinear model driven by a periodic signal and correlated noises for the cases of a multiplicative non-Gaussian noise and an additive Gaussian white noise is investigated. Applying the path integral approach, the unified colored noise approximation and the two-state model theory, the analytical expression of the signal-to-noise ratio (SNR) is derived. It is found that conventional stochastic resonance exists in this system. From numerical computations we obtain that: (i) As a function of the non-Gaussian noise intensity, the SNR is increased when the non-Gaussian noise deviation parameter q is increased. (ii) As a function of the Gaussian noise intensity, the SNR is decreased when q is increased. This demonstrates that the effect of the non-Gaussian noise on SNR is different from that of the Gaussian noise in this system. Moreover, we further discuss the effect of the correlation time of the non-Gaussian noise, cross-correlation strength, the amplitude and frequency of the periodic signal on SR.
Toward the detection of gravitational waves under non-Gaussian noises I. Locally optimal statistic.
Yokoyama, Jun'ichi
2014-01-01
After reviewing the standard hypothesis test and the matched filter technique to identify gravitational waves under Gaussian noises, we introduce two methods to deal with non-Gaussian stationary noises. We formulate the likelihood ratio function under weakly non-Gaussian noises through the Edgeworth expansion and strongly non-Gaussian noises in terms of a new method we call Gaussian mapping where the observed marginal distribution and the two-body correlation function are fully taken into account. We then apply these two approaches to Student's t-distribution which has a larger tails than Gaussian. It is shown that while both methods work well in the case the non-Gaussianity is small, only the latter method works well for highly non-Gaussian case. PMID:25504231
Toward the detection of gravitational waves under non-Gaussian noises I. Locally optimal statistic.
Yokoyama, Jun'ichi
2014-01-01
After reviewing the standard hypothesis test and the matched filter technique to identify gravitational waves under Gaussian noises, we introduce two methods to deal with non-Gaussian stationary noises. We formulate the likelihood ratio function under weakly non-Gaussian noises through the Edgeworth expansion and strongly non-Gaussian noises in terms of a new method we call Gaussian mapping where the observed marginal distribution and the two-body correlation function are fully taken into account. We then apply these two approaches to Student's t-distribution which has a larger tails than Gaussian. It is shown that while both methods work well in the case the non-Gaussianity is small, only the latter method works well for highly non-Gaussian case.
Coherence resonance in the two-dimensional neural map driven by non-Gaussian colored noise
NASA Astrophysics Data System (ADS)
Li, Dongxi; Hu, Bing; Wang, Jia; Jing, Yingchuan; Hou, Fangmei
2016-01-01
Based on the two-dimensional (2D) neural map, we investigate the impacts of non-Gaussian colored noise on the firing activity of discrete system. Taking the coherence parameter R to measure the regularity of firing behavior, it is demonstrated that coherence parameter R has a pronounced minimum value with the noise intensity and the correlation time of non-Gaussian colored noise, which is the so-called phenomenon of coherence resonance (CR). Besides, the firing activity is not sensitive to the non-Gaussian parameter which determines the departure from the Gaussian distribution when the correlation time is large enough.
Quantum error correction of continuous-variable states against Gaussian noise
Ralph, T. C.
2011-08-15
We describe a continuous-variable error correction protocol that can correct the Gaussian noise induced by linear loss on Gaussian states. The protocol can be implemented using linear optics and photon counting. We explore the theoretical bounds of the protocol as well as the expected performance given current knowledge and technology.
On stochastic differential equations driven by the renormalized square of the Gaussian white noise
NASA Astrophysics Data System (ADS)
Ben Ammou, Bilel Kacem; Lanconelli, Alberto
2015-11-01
We investigate the properties of the Wick square of Gaussian white noises through a new method to perform nonlinear operations on Hida distributions. This method lays in between the Wick product interpretation and the usual definition of nonlinear functions. We prove an Itô-type formula and solve stochastic differential equations driven by the renormalized square of the Gaussian white noise. Our approach works with standard assumptions on the coefficients of the equations, global Lipschitz continuity, and produces existence and uniqueness results in the space where the noise lives. The linear case is studied in details and positivity of the solution is proved.
NASA Astrophysics Data System (ADS)
Ganguly, Jayanta; Ghosh, Manas
2014-06-01
We investigate the profiles of diagonal components of frequency-dependent linear (αxx and αyy) optical response of repulsive impurity doped quantum dots. The dopant impurity potential chosen assumes Gaussian form. The study principally puts emphasis on investigating the role of noise on the polarizability components. In view of this we have exploited Gaussian white noise containing additive and multiplicative characteristics (in Stratonovich sense). The frequency-dependent polarizabilities are studied by exposing the doped dot to a periodically oscillating external electric field of given intensity. The oscillation frequency, confinement potentials, dopant location, and above all, the noise characteristics tune the linear polarizability components in a subtle manner. Whereas the additive noise fails to have any impact on the polarizabilities, the multiplicative noise influences them delicately and gives rise to additional interesting features.
NASA Astrophysics Data System (ADS)
Kasai, Seiya; Tadokoro, Yukihiro; Ichiki, Akihisa
2013-12-01
We design nonlinear functions for the transmission of a small signal with non-Gaussian noise and perform experiments to characterize their responses. Using statistical design theory [A. Ichiki and Y. Tadokoro, Phys. Rev. E 87, 012124 (2013), 10.1103/PhysRevE.87.012124], a static nonlinear function is estimated from the probability density function of the given noise in order to maximize the signal-to-noise ratio of the output. Using an electronic system that implements the optimized nonlinear function, we confirm the recovery of a small signal from a signal with non-Gaussian noise. In our experiment, the non-Gaussian noise is a mixture of Gaussian noises. A similar technique is also applied to the optimization of the threshold value of the function. We find that, for non-Gaussian noise, the response of the optimized nonlinear systems is better than that of the linear system.
A feedback control strategy for the airfoil system under non-Gaussian colored noise excitation.
Huang, Yong; Tao, Gang
2014-09-01
The stability of a binary airfoil with feedback control under stochastic disturbances, a non-Gaussian colored noise, is studied in this paper. First, based on some approximated theories and methods the non-Gaussian colored noise is simplified to an Ornstein-Uhlenbeck process. Furthermore, via the stochastic averaging method and the logarithmic polar transformation, one dimensional diffusion process can be obtained. At last by applying the boundary conditions, the largest Lyapunov exponent which can determine the almost-sure stability of the system and the effective region of control parameters is calculated.
A feedback control strategy for the airfoil system under non-Gaussian colored noise excitation
Huang, Yong E-mail: taogang@njust.edu.cn; Tao, Gang E-mail: taogang@njust.edu.cn
2014-09-01
The stability of a binary airfoil with feedback control under stochastic disturbances, a non-Gaussian colored noise, is studied in this paper. First, based on some approximated theories and methods the non-Gaussian colored noise is simplified to an Ornstein-Uhlenbeck process. Furthermore, via the stochastic averaging method and the logarithmic polar transformation, one dimensional diffusion process can be obtained. At last by applying the boundary conditions, the largest Lyapunov exponent which can determine the almost-sure stability of the system and the effective region of control parameters is calculated.
Ultimate capacity of linear time-invariant bosonic channels with additive Gaussian noise
NASA Astrophysics Data System (ADS)
Roy Bardhan, Bhaskar; Shapiro, Jeffrey H.
2016-03-01
Fiber-optic communications are moving to coherent detection in order to increase their spectral efficiency, i.e., their channel capacity per unit bandwidth. At power levels below the threshold for significant nonlinear effects, the channel model for such operation a linear time-invariant filter followed by additive Gaussian noise is one whose channel capacity is well known from Shannon's noisy channel coding theorem. The fiber channel, however, is really a bosonic channel, meaning that its ultimate classical information capacity must be determined from quantum-mechanical analysis, viz. from the Holevo-Schumacher-Westmoreland (HSW) theorem. Based on recent results establishing the HSW capacity of a linear (lossy or amplifying) channel with additive Gaussian noise, we provide a general continuous-time result, namely the HSW capacity of a linear time-invariant (LTI) bosonic channel with additive Gaussian noise arising from a thermal environment. In particular, we treat quasi-monochromatic communication under an average power constraint through a channel comprised of a stable LTI filter that may be attenuating at all frequencies or amplifying at some frequencies and attenuating at others. Phase-insensitive additive Gaussian noise-associated with the continuous-time Langevin noise operator needed to preserve free-field commutator brackets is included at the filter output. We compare the resulting spectral efficiencies with corresponding results for heterodyne and homodyne detection over the same channel to assess the increased spectral efficiency that might be realized with optimum quantum reception.
Maass, W; Sontag, E D
1999-04-01
We consider recurrent analog neural nets where the output of each gate is subject to gaussian noise or any other common noise distribution that is nonzero on a sufficiently large part of the state-space. We show that many regular languages cannot be recognized by networks of this type, and we give a precise characterization of languages that can be recognized. This result implies severe constraints on possibilities for constructing recurrent analog neural nets that are robust against realistic types of analog noise. On the other hand, we present a method for constructing feedforward analog neural nets that are robust with regard to analog noise of this type.
Lifting primordial non-Gaussianity above the noise
NASA Astrophysics Data System (ADS)
Welling, Yvette; van der Woude, Drian; Pajer, Enrico
2016-08-01
Primordial non-Gaussianity (PNG) in Large Scale Structures is obfuscated by the many additional sources of non-linearity. Within the Effective Field Theory approach to Standard Perturbation Theory, we show that matter non-linearities in the bispectrum can be modeled sufficiently well to strengthen current bounds with near future surveys, such as Euclid. We find that the EFT corrections are crucial to this improvement in sensitivity. Yet, our understanding of non-linearities is still insufficient to reach important theoretical benchmarks for equilateral PNG, while, for local PNG, our forecast is more optimistic. We consistently account for the theoretical error intrinsic to the perturbative approach and discuss the details of its implementation in Fisher forecasts.
NASA Technical Reports Server (NTRS)
Cheung, K. M.; Vilnrotter, V.
1996-01-01
A closed-form expression for the capacity of an array of correlated Gaussian channels is derived. It is shown that when signal and noise are independent, the array of observables can be replaced with a single observable without diminishing the capacity of the array channel. Examples are provided to illustrate the dependence of channel capacity on noise correlation for two- and three-channel arrays.
NASA Astrophysics Data System (ADS)
Basin, M.; Maldonado, J. J.; Zendejo, O.
2016-07-01
This paper proposes new mean-square filter and parameter estimator design for linear stochastic systems with unknown parameters over linear observations, where unknown parameters are considered as combinations of Gaussian and Poisson white noises. The problem is treated by reducing the original problem to a filtering problem for an extended state vector that includes parameters as additional states, modelled as combinations of independent Gaussian and Poisson processes. The solution to this filtering problem is based on the mean-square filtering equations for incompletely polynomial states confused with Gaussian and Poisson noises over linear observations. The resulting mean-square filter serves as an identifier for the unknown parameters. Finally, a simulation example shows effectiveness of the proposed mean-square filter and parameter estimator.
Additive non-Gaussian noise attacks on the scalar Costa scheme (SCS)
NASA Astrophysics Data System (ADS)
Tzschoppe, Roman; Bauml, Robert; Fischer, Robert; Huber, Johannes; Kaup, Andre
2005-03-01
The additive attack public mutual information game is explicitly solved for one of the simplest quantization based watermarking schemes, the scalar Costa scheme (SCS). It is a zero-sum game played between the embedder and the attacker, and the payoff function is the mutual information. The solution of the game, a subgame perfect nash equilibrium, is found by backward induction. Therefore, the Blahut-Arimoto algorithm is employed for numerically optimizing the mutual information over noise distributions. Although the worst case distribution is in general strongly non-Gaussian, the capacity degradation compared to a suboptimal Gaussian noise attack is quite small. The loss, if the embedder optimizes SCS for a Gaussian attack but the worst case attack is employed, is negligible.
Minimal Model of Stochastic Athermal Systems: Origin of Non-Gaussian Noise
NASA Astrophysics Data System (ADS)
Kanazawa, Kiyoshi; Sano, Tomohiko G.; Sagawa, Takahiro; Hayakawa, Hisao
2015-03-01
For a wide class of stochastic athermal systems, we derive Langevin-like equations driven by non-Gaussian noise, starting from master equations and developing a new asymptotic expansion. We found an explicit condition whereby the non-Gaussian properties of the athermal noise become dominant for tracer particles associated with both thermal and athermal environments. Furthermore, we derive an inverse formula to infer microscopic properties of the athermal bath from the statistics of the tracer particle. We apply our formulation to a granular motor under viscous friction and analytically obtain the angular velocity distribution function. Our theory demonstrates that the non-Gaussian Langevin equation is the minimal model of athermal systems.
Nonlinear Bayesian estimation of BOLD signal under non-Gaussian noise.
Khan, Ali Fahim; Younis, Muhammad Shahzad; Bajwa, Khalid Bashir
2015-01-01
Modeling the blood oxygenation level dependent (BOLD) signal has been a subject of study for over a decade in the neuroimaging community. Inspired from fluid dynamics, the hemodynamic model provides a plausible yet convincing interpretation of the BOLD signal by amalgamating effects of dynamic physiological changes in blood oxygenation, cerebral blood flow and volume. The nonautonomous, nonlinear set of differential equations of the hemodynamic model constitutes the process model while the weighted nonlinear sum of the physiological variables forms the measurement model. Plagued by various noise sources, the time series fMRI measurement data is mostly assumed to be affected by additive Gaussian noise. Though more feasible, the assumption may cause the designed filter to perform poorly if made to work under non-Gaussian environment. In this paper, we present a data assimilation scheme that assumes additive non-Gaussian noise, namely, the e-mixture noise, affecting the measurements. The proposed filter MAGSF and the celebrated EKF are put to test by performing joint optimal Bayesian filtering to estimate both the states and parameters governing the hemodynamic model under non-Gaussian environment. Analyses using both the synthetic and real data reveal superior performance of the MAGSF as compared to EKF. PMID:25691911
Nonlinear Bayesian Estimation of BOLD Signal under Non-Gaussian Noise
Khan, Ali Fahim; Younis, Muhammad Shahzad; Bajwa, Khalid Bashir
2015-01-01
Modeling the blood oxygenation level dependent (BOLD) signal has been a subject of study for over a decade in the neuroimaging community. Inspired from fluid dynamics, the hemodynamic model provides a plausible yet convincing interpretation of the BOLD signal by amalgamating effects of dynamic physiological changes in blood oxygenation, cerebral blood flow and volume. The nonautonomous, nonlinear set of differential equations of the hemodynamic model constitutes the process model while the weighted nonlinear sum of the physiological variables forms the measurement model. Plagued by various noise sources, the time series fMRI measurement data is mostly assumed to be affected by additive Gaussian noise. Though more feasible, the assumption may cause the designed filter to perform poorly if made to work under non-Gaussian environment. In this paper, we present a data assimilation scheme that assumes additive non-Gaussian noise, namely, the e-mixture noise, affecting the measurements. The proposed filter MAGSF and the celebrated EKF are put to test by performing joint optimal Bayesian filtering to estimate both the states and parameters governing the hemodynamic model under non-Gaussian environment. Analyses using both the synthetic and real data reveal superior performance of the MAGSF as compared to EKF. PMID:25691911
NASA Astrophysics Data System (ADS)
Saha, Surajit; Ghosh, Manas
2016-03-01
We perform a broad exploration of profiles of third harmonic generation (THG) susceptibility of impurity doped quantum dots (QDs) in the presence and absence of noise. We have invoked Gaussian white noise in the present study. A Gaussian impurity has been introduced into the QD. Noise has been applied to the system additively and multiplicatively. A perpendicular magnetic field emerges out as a confinement source and a static external electric field has been applied. The THG profiles have been pursued as a function of incident photon energy when several important parameters such as electric field strength, magnetic field strength, confinement energy, dopant location, Al concentration, dopant potential, relaxation time and noise strength assume different values. Moreover, the role of the pathway through which noise is applied (additive/multiplicative) on the THG profiles has also been deciphered. The THG profiles are found to be decorated with interesting observations such as shift of THG peak position and maximization/minimization of THG peak intensity. Presence of noise alters the characteristics of THG profiles and sometimes enhances the THG peak intensity. Furthermore, the mode of application of noise (additive/multiplicative) also regulates the THG profiles in a few occasions in contrasting manners. The observations highlight the possible scope of tuning the THG coefficient of doped QD systems in the presence of noise and bears tremendous technological importance.
NASA Astrophysics Data System (ADS)
Bera, Aindrila; Saha, Surajit; Ganguly, Jayanta; Ghosh, Manas
2016-11-01
We explore diamagnetic susceptibility (DMS) of impurity doped quantum dot (QD) in presence of Gaussian white noise. Noise has been introduced to the system additively and multiplicatively. In view of these profiles of DMS have been pursued with variations of several important quantities e.g. magnetic field strength, confinement frequency, dopant location, dopant potential, and aluminium concentration, both in presence and absence of noise. We have invariably envisaged noise-induced suppression of DMS. Moreover, the extent of suppression noticeably depends on mode of application (additive/multiplicative) of noise. The said mode of application also plays a governing role in the onset of saturation of DMS values. The present study provides a deep insight into the promising role played by noise in controlling effective confinement imposed on the system which bears significant relevance.
Broad-band Gaussian noise is most effective in improving motor performance and is most pleasant
Trenado, Carlos; Mikulić, Areh; Manjarrez, Elias; Mendez-Balbuena, Ignacio; Schulte-Mönting, Jürgen; Huethe, Frank; Hepp-Reymond, Marie-Claude; Kristeva, Rumyana
2014-01-01
Modern attempts to improve human performance focus on stochastic resonance (SR). SR is a phenomenon in non-linear systems characterized by a response increase of the system induced by a particular level of input noise. Recently, we reported that an optimum level of 0–15 Hz Gaussian noise applied to the human index finger improved static isometric force compensation. A possible explanation was a better sensorimotor integration caused by increase in sensitivity of peripheral receptors and/or of internal SR. The present study in 10 subjects compares SR effects in the performance of the same motor task and on pleasantness, by applying three Gaussian noises chosen on the sensitivity of the fingertip receptors (0–15 Hz mostly for Merkel receptors, 250–300 Hz for Pacini corpuscles and 0–300 Hz for all). We document that only the 0–300 Hz noise induced SR effect during the transitory phase of the task. In contrast, the motor performance was improved during the stationary phase for all three noise frequency bandwidths. This improvement was stronger for 0–300 Hz and 250–300 Hz than for 0–15 Hz noise. Further, we found higher degree of pleasantness for 0–300 Hz and 250–300 Hz noise bandwidths than for 0–15 Hz. Thus, we show that the most appropriate Gaussian noise that could be used in haptic gloves is the 0–300 Hz, as it improved motor performance during both stationary and transitory phases. In addition, this noise had the highest degree of pleasantness and thus reveals that the glabrous skin can also forward pleasant sensations. PMID:24550806
Broad-band Gaussian noise is most effective in improving motor performance and is most pleasant.
Trenado, Carlos; Mikulić, Areh; Manjarrez, Elias; Mendez-Balbuena, Ignacio; Schulte-Mönting, Jürgen; Huethe, Frank; Hepp-Reymond, Marie-Claude; Kristeva, Rumyana
2014-01-01
Modern attempts to improve human performance focus on stochastic resonance (SR). SR is a phenomenon in non-linear systems characterized by a response increase of the system induced by a particular level of input noise. Recently, we reported that an optimum level of 0-15 Hz Gaussian noise applied to the human index finger improved static isometric force compensation. A possible explanation was a better sensorimotor integration caused by increase in sensitivity of peripheral receptors and/or of internal SR. The present study in 10 subjects compares SR effects in the performance of the same motor task and on pleasantness, by applying three Gaussian noises chosen on the sensitivity of the fingertip receptors (0-15 Hz mostly for Merkel receptors, 250-300 Hz for Pacini corpuscles and 0-300 Hz for all). We document that only the 0-300 Hz noise induced SR effect during the transitory phase of the task. In contrast, the motor performance was improved during the stationary phase for all three noise frequency bandwidths. This improvement was stronger for 0-300 Hz and 250-300 Hz than for 0-15 Hz noise. Further, we found higher degree of pleasantness for 0-300 Hz and 250-300 Hz noise bandwidths than for 0-15 Hz. Thus, we show that the most appropriate Gaussian noise that could be used in haptic gloves is the 0-300 Hz, as it improved motor performance during both stationary and transitory phases. In addition, this noise had the highest degree of pleasantness and thus reveals that the glabrous skin can also forward pleasant sensations.
A wideband Gaussian noise generator utilizing simultaneously generated pn-sequences.
NASA Technical Reports Server (NTRS)
Hurd, W. J.
1972-01-01
A digital system has been constructed for the generation of wideband Gaussian noise with a spectrum which is flat to within plus or minus 0.5 dB from 0 to 10 MHz. These characteristics are substantially better than those of commercially available analog noise generators, and are required in testing and simulation of wideband communications systems. The noise is generated by the analog summation of thirty essentially independent binary waveforms, clocked at 35 MHz, and low-pass filtered to 10 MHz.
Adaptive subspace detection of extended target in white Gaussian noise using sinc basis
NASA Astrophysics Data System (ADS)
Zhang, Xiao-Wei; Li, Ming; Qu, Jian-She; Yang, Hui
2016-01-01
For the high resolution radar (HRR), the problem of detecting the extended target is considered in this paper. Based on a single observation, a new two-step detection based on sparse representation (TSDSR) method is proposed to detect the extended target in the presence of Gaussian noise with unknown covariance. In the new method, the Sinc dictionary is introduced to sparsely represent the high resolution range profile (HRRP). Meanwhile, adaptive subspace pursuit (ASP) is presented to recover the HRRP embedded in the Gaussian noise and estimate the noise covariance matrix. Based on the Sinc dictionary and the estimated noise covariance matrix, one step subspace detector (OSSD) for the first-order Gaussian (FOG) model without secondary data is adopted to realise the extended target detection. Finally, the proposed TSDSR method is applied to raw HRR data. Experimental results demonstrate that HRRPs of different targets can be sparsely represented very well with the Sinc dictionary. Moreover, the new method can estimate the noise power with tiny errors and have a good detection performance.
Optimal configurations of full-Stokes polarimeter with immunity to both Poisson and Gaussian noise
NASA Astrophysics Data System (ADS)
Mu, Tingkui; Chen, Zeyu; Zhang, Chunmin; Liang, Rongguang
2016-05-01
For a full-Stokes polarimeter (FSP), generally there are two types of noise, signal-dependent Poisson shot noise and signal-independent additive Gaussian noise, which will degrade the signal-to-noise ratio on the measured Stokes parameters. The relation between the immunity to Gaussian noise and the condition of the measurement matrix has been widely studied in the recent literature. In this paper, we present a new merit function and use it to achieve optimal configurations with immunity to both types of noise. The numerical results show that, for the FSP consisting of variable retarders followed by a fixed polarizer, the four measurement channels immune to these two types of noise can be optimally composed by a 102.2° retardance with a pair of azimuths ±71.9° and a 142.1° retardance with a pair of azimuths ±34.95°, or by two quarter-wave plates with four pairs of azimuths (±70.15°, ±87.84°) and (±42.82°, ±19.14°). The tolerances of the retardances or azimuths in the optimized configurations are evaluated for practical manufacturing, assembling and alignment.
NASA Astrophysics Data System (ADS)
Tuzlukov, Vyacheslav
2011-06-01
In this paper, we consider the problem of M-ary signal detection based on the generalized approach to signal processing (GASP) in noise over a single-input multiple-output (SIMO) channel affected by frequency-dispersive Rayleigh distributed fading and corrupted by additive non-Gaussian noise modeled as spherically invariant random process. We derive both the optimum generalized detector (GD) structure based on GASP and a suboptimal reduced-complexity GD applying the low energy coherence approach jointly with the GASP in noise. Both GD structures are independent of the actual noise statistics. We also carry out a performance analysis of both GDs and compare with the conventional receivers. The performance analysis is carried out with reference to the case that the channel is affected by a frequency-selective fading and for a binary frequency-shift keying (BFSK) signaling format. The results obtained through both a Chernoff-bounding technique and Monte Carlo simulations reveal that the adoption of diversity also represents a suitable means to restore performance in the presence of dispersive fading and impulsive non-Gaussian noise. It is also shown that the suboptimal GD incurs a limited loss with respect to the optimum GD and this loss is less in comparison with the conventional receiver.
Analysis of regularized inversion of data corrupted by white Gaussian noise
NASA Astrophysics Data System (ADS)
Kekkonen, Hanne; Lassas, Matti; Siltanen, Samuli
2014-04-01
Tikhonov regularization is studied in the case of linear pseudodifferential operator as the forward map and additive white Gaussian noise as the measurement error. The measurement model for an unknown function u(x) is \\begin{eqnarray*} m(x) = Au(x) + \\delta \\varepsilon (x), \\end{eqnarray*} where δ > 0 is the noise magnitude. If ɛ was an L2-function, Tikhonov regularization gives an estimate \\begin{eqnarray*} T_\\alpha (m) = \\mathop {{arg\\, min}}_{u\\in H^r} \\big \\lbrace \\Vert A u-m\\Vert _{L^2}^2+ \\alpha \\Vert u\\Vert _{H^r}^2 \\big \\rbrace \\end{eqnarray*} for u where α = α(δ) is the regularization parameter. Here penalization of the Sobolev norm \\Vert u\\Vert _{H^r} covers the cases of standard Tikhonov regularization (r = 0) and first derivative penalty (r = 1). Realizations of white Gaussian noise are almost never in L2, but do belong to Hs with probability one if s < 0 is small enough. A modification of Tikhonov regularization theory is presented, covering the case of white Gaussian measurement noise. Furthermore, the convergence of regularized reconstructions to the correct solution as δ → 0 is proven in appropriate function spaces using microlocal analysis. The convergence of the related finite-dimensional problems to the infinite-dimensional problem is also analysed.
Non-stationary noise estimation using dictionary learning and Gaussian mixture models
NASA Astrophysics Data System (ADS)
Hughes, James M.; Rockmore, Daniel N.; Wang, Yang
2014-02-01
Stationarity of the noise distribution is a common assumption in image processing. This assumption greatly simplifies denoising estimators and other model parameters and consequently assuming stationarity is often a matter of convenience rather than an accurate model of noise characteristics. The problematic nature of this assumption is exacerbated in real-world contexts, where noise is often highly non-stationary and can possess time- and space-varying characteristics. Regardless of model complexity, estimating the parameters of noise dis- tributions in digital images is a difficult task, and estimates are often based on heuristic assumptions. Recently, sparse Bayesian dictionary learning methods were shown to produce accurate estimates of the level of additive white Gaussian noise in images with minimal assumptions. We show that a similar model is capable of accu- rately modeling certain kinds of non-stationary noise processes, allowing for space-varying noise in images to be estimated, detected, and removed. We apply this modeling concept to several types of non-stationary noise and demonstrate the model's effectiveness on real-world problems, including denoising and segmentation of images according to noise characteristics, which has applications in image forensics.
Symul, Thomas; Alton, Daniel J.; Lance, Andrew M.; Lam, Ping Koy; Assad, Syed M.; Weedbrook, Christian; Ralph, Timothy C.
2007-09-15
In realistic continuous-variable quantum key distribution protocols, an eavesdropper may exploit the additional Gaussian noise generated during transmission to mask her presence. We present a theoretical framework for a post-selection-based protocol which explicitly takes into account excess Gaussian noise. We derive a quantitative expression of the secret key rates based on the Levitin and Holevo bounds. We experimentally demonstrate that the post-selection-based scheme is still secure against both individual and collective Gaussian attacks in the presence of this excess noise.
Canales-Rodríguez, Erick J; Daducci, Alessandro; Sotiropoulos, Stamatios N; Caruyer, Emmanuel; Aja-Fernández, Santiago; Radua, Joaquim; Yurramendi Mendizabal, Jesús M; Iturria-Medina, Yasser; Melie-García, Lester; Alemán-Gómez, Yasser; Thiran, Jean-Philippe; Sarró, Salvador; Pomarol-Clotet, Edith; Salvador, Raymond
2015-01-01
Spherical deconvolution (SD) methods are widely used to estimate the intra-voxel white-matter fiber orientations from diffusion MRI data. However, while some of these methods assume a zero-mean Gaussian distribution for the underlying noise, its real distribution is known to be non-Gaussian and to depend on many factors such as the number of coils and the methodology used to combine multichannel MRI signals. Indeed, the two prevailing methods for multichannel signal combination lead to noise patterns better described by Rician and noncentral Chi distributions. Here we develop a Robust and Unbiased Model-BAsed Spherical Deconvolution (RUMBA-SD) technique, intended to deal with realistic MRI noise, based on a Richardson-Lucy (RL) algorithm adapted to Rician and noncentral Chi likelihood models. To quantify the benefits of using proper noise models, RUMBA-SD was compared with dRL-SD, a well-established method based on the RL algorithm for Gaussian noise. Another aim of the study was to quantify the impact of including a total variation (TV) spatial regularization term in the estimation framework. To do this, we developed TV spatially-regularized versions of both RUMBA-SD and dRL-SD algorithms. The evaluation was performed by comparing various quality metrics on 132 three-dimensional synthetic phantoms involving different inter-fiber angles and volume fractions, which were contaminated with noise mimicking patterns generated by data processing in multichannel scanners. The results demonstrate that the inclusion of proper likelihood models leads to an increased ability to resolve fiber crossings with smaller inter-fiber angles and to better detect non-dominant fibers. The inclusion of TV regularization dramatically improved the resolution power of both techniques. The above findings were also verified in human brain data.
Canales-Rodríguez, Erick J.; Caruyer, Emmanuel; Aja-Fernández, Santiago; Radua, Joaquim; Yurramendi Mendizabal, Jesús M.; Iturria-Medina, Yasser; Melie-García, Lester; Alemán-Gómez, Yasser; Thiran, Jean-Philippe; Sarró, Salvador; Pomarol-Clotet, Edith; Salvador, Raymond
2015-01-01
Spherical deconvolution (SD) methods are widely used to estimate the intra-voxel white-matter fiber orientations from diffusion MRI data. However, while some of these methods assume a zero-mean Gaussian distribution for the underlying noise, its real distribution is known to be non-Gaussian and to depend on many factors such as the number of coils and the methodology used to combine multichannel MRI signals. Indeed, the two prevailing methods for multichannel signal combination lead to noise patterns better described by Rician and noncentral Chi distributions. Here we develop a Robust and Unbiased Model-BAsed Spherical Deconvolution (RUMBA-SD) technique, intended to deal with realistic MRI noise, based on a Richardson-Lucy (RL) algorithm adapted to Rician and noncentral Chi likelihood models. To quantify the benefits of using proper noise models, RUMBA-SD was compared with dRL-SD, a well-established method based on the RL algorithm for Gaussian noise. Another aim of the study was to quantify the impact of including a total variation (TV) spatial regularization term in the estimation framework. To do this, we developed TV spatially-regularized versions of both RUMBA-SD and dRL-SD algorithms. The evaluation was performed by comparing various quality metrics on 132 three-dimensional synthetic phantoms involving different inter-fiber angles and volume fractions, which were contaminated with noise mimicking patterns generated by data processing in multichannel scanners. The results demonstrate that the inclusion of proper likelihood models leads to an increased ability to resolve fiber crossings with smaller inter-fiber angles and to better detect non-dominant fibers. The inclusion of TV regularization dramatically improved the resolution power of both techniques. The above findings were also verified in human brain data. PMID:26470024
Canales-Rodríguez, Erick J; Daducci, Alessandro; Sotiropoulos, Stamatios N; Caruyer, Emmanuel; Aja-Fernández, Santiago; Radua, Joaquim; Yurramendi Mendizabal, Jesús M; Iturria-Medina, Yasser; Melie-García, Lester; Alemán-Gómez, Yasser; Thiran, Jean-Philippe; Sarró, Salvador; Pomarol-Clotet, Edith; Salvador, Raymond
2015-01-01
Spherical deconvolution (SD) methods are widely used to estimate the intra-voxel white-matter fiber orientations from diffusion MRI data. However, while some of these methods assume a zero-mean Gaussian distribution for the underlying noise, its real distribution is known to be non-Gaussian and to depend on many factors such as the number of coils and the methodology used to combine multichannel MRI signals. Indeed, the two prevailing methods for multichannel signal combination lead to noise patterns better described by Rician and noncentral Chi distributions. Here we develop a Robust and Unbiased Model-BAsed Spherical Deconvolution (RUMBA-SD) technique, intended to deal with realistic MRI noise, based on a Richardson-Lucy (RL) algorithm adapted to Rician and noncentral Chi likelihood models. To quantify the benefits of using proper noise models, RUMBA-SD was compared with dRL-SD, a well-established method based on the RL algorithm for Gaussian noise. Another aim of the study was to quantify the impact of including a total variation (TV) spatial regularization term in the estimation framework. To do this, we developed TV spatially-regularized versions of both RUMBA-SD and dRL-SD algorithms. The evaluation was performed by comparing various quality metrics on 132 three-dimensional synthetic phantoms involving different inter-fiber angles and volume fractions, which were contaminated with noise mimicking patterns generated by data processing in multichannel scanners. The results demonstrate that the inclusion of proper likelihood models leads to an increased ability to resolve fiber crossings with smaller inter-fiber angles and to better detect non-dominant fibers. The inclusion of TV regularization dramatically improved the resolution power of both techniques. The above findings were also verified in human brain data. PMID:26470024
On estimating the phase of periodic waveform in additive Gaussian noise, part 2
NASA Astrophysics Data System (ADS)
Rauch, L. L.
1984-11-01
Motivated by advances in signal processing technology that support more complex algorithms, a new look is taken at the problem of estimating the phase and other parameters of a periodic waveform in additive Gaussian noise. The general problem was introduced and the maximum a posteriori probability criterion with signal space interpretation was used to obtain the structures of optimum and some suboptimum phase estimators for known constant frequency and unknown constant phase with an a priori distribution. Optimal algorithms are obtained for some cases where the frequency is a parameterized function of time with the unknown parameters and phase having a joint a priori distribution. In the last section, the intrinsic and extrinsic geometry of hypersurfaces is introduced to provide insight to the estimation problem for the small noise and large noise cases.
Practical Poissonian-Gaussian noise modeling and fitting for single-image raw-data.
Foi, Alessandro; Trimeche, Mejdi; Katkovnik, Vladimir; Egiazarian, Karen
2008-10-01
We present a simple and usable noise model for the raw-data of digital imaging sensors. This signal-dependent noise model, which gives the pointwise standard-deviation of the noise as a function of the expectation of the pixel raw-data output, is composed of a Poissonian part, modeling the photon sensing, and Gaussian part, for the remaining stationary disturbances in the output data. We further explicitly take into account the clipping of the data (over- and under-exposure), faithfully reproducing the nonlinear response of the sensor. We propose an algorithm for the fully automatic estimation of the model parameters given a single noisy image. Experiments with synthetic images and with real raw-data from various sensors prove the practical applicability of the method and the accuracy of the proposed model. PMID:18784024
NASA Astrophysics Data System (ADS)
Kang-Kang, Wang; Xian-Bin, Liu; Yu, Zhou
2015-08-01
In this paper, the stability and stochastic resonance (SR) phenomenon induced by the multiplicative periodic signal for a metapopulation system driven by the additive Gaussian noise, multiplicative non-Gaussian noise and noise correlation time is investigated. By using the fast descent method, unified colored noise approximation and McNamara and Wiesenfeld’s SR theory, the analytical expressions of the stationary probability distribution function and signal-to-noise ratio (SNR) are derived in the adiabatic limit. Via numerical calculations, each effect of the addictive noise intensity, the multiplicative noise intensity and the correlation time upon the steady state probability distribution function and the SNR is discussed, respectively. It is shown that multiplicative, additive noises and the departure parameter from the Gaussian noise can all destroy the stability of the population system. However, the noise correlation time can consolidate the stability of the system. On the other hand, the correlation time always plays an important role in motivating the SR and enhancing the SNR. Under different parameter conditions of the system, the multiplicative, additive noises and the departure parameter can not only excite SR phenomenon, but also restrain the SR phenomenon, which demonstrates the complexity of different noises upon the nonlinear system.
A median-Gaussian filtering framework for Moiré pattern noise removal from X-ray microscopy image.
Wei, Zhouping; Wang, Jian; Nichol, Helen; Wiebe, Sheldon; Chapman, Dean
2012-02-01
Moiré pattern noise in Scanning Transmission X-ray Microscopy (STXM) imaging introduces significant errors in qualitative and quantitative image analysis. Due to the complex origin of the noise, it is difficult to avoid Moiré pattern noise during the image data acquisition stage. In this paper, we introduce a post-processing method for filtering Moiré pattern noise from STXM images. This method includes a semi-automatic detection of the spectral peaks in the Fourier amplitude spectrum by using a local median filter, and elimination of the spectral noise peaks using a Gaussian notch filter. The proposed median-Gaussian filtering framework shows good results for STXM images with the size of power of two, if such parameters as threshold, sizes of the median and Gaussian filters, and size of the low frequency window, have been properly selected.
Non-Gaussian resistance noise in misfit layer compounds: Bi-Se-Cr
NASA Astrophysics Data System (ADS)
Peng, Lintao; Freedman, Alex; Clarke, Samantha; Freedman, Danna; Grayson, M.
Misfit layer ternary compounds Bi-Se-Cr have been synthesized and structurally and magnetically characterized. However, the nature of the magnetic ordering below the transition temperature remains debatable between ferromagnetic and spin-glass. These misfit layer compounds consist of two alternating chalcogenide layers of CrSe2 and BiSe along the c-axis. Whereas the a-axis is lattice matched, the lattice mismatch along the b-axis introduces non-periodic modulation of atomic position leading to quasi-crystalline order along the b-axis alone. We explore unconventional electrical transport properties in the noise spectrum of these compounds. After thinning down the compounds to nanoscale, Van der Pauw devices are fabricated with standard electron beam lithography process. Large resistance noise was observed at temperature below the Cure temperature. The magnitude of resistance noise is much greater than trivial intrinsic noises like thermal Johnson noise and increases as temperature decreases. The probability density function of the relative noise shows 2-4 peaks among different observations which indicate strong non-Gaussian statistic property suggesting glassy behaviors in this material.
NASA Astrophysics Data System (ADS)
Ganguly, Jayanta; Ghosh, Manas
2014-05-01
We investigate the profiles of diagonal components of frequency-dependent first nonlinear (βxxx and βyyy) optical response of repulsive impurity doped quantum dots. We have assumed a Gaussian function to represent the dopant impurity potential. This study primarily addresses the role of noise on the polarizability components. We have invoked Gaussian white noise consisting of additive and multiplicative characteristics (in Stratonovich sense). The doped system has been subjected to an oscillating electric field of given intensity, and the frequency-dependent first nonlinear polarizabilities are computed. The noise characteristics are manifested in an interesting way in the nonlinear polarizability components. In case of additive noise, the noise strength remains practically ineffective in influencing the optical responses. The situation completely changes with the replacement of additive noise by its multiplicative analog. The replacement enhances the nonlinear optical response dramatically and also causes their maximization at some typical value of noise strength that depends on oscillation frequency.
Ganguly, Jayanta; Ghosh, Manas
2014-05-07
We investigate the profiles of diagonal components of frequency-dependent first nonlinear (β{sub xxx} and β{sub yyy}) optical response of repulsive impurity doped quantum dots. We have assumed a Gaussian function to represent the dopant impurity potential. This study primarily addresses the role of noise on the polarizability components. We have invoked Gaussian white noise consisting of additive and multiplicative characteristics (in Stratonovich sense). The doped system has been subjected to an oscillating electric field of given intensity, and the frequency-dependent first nonlinear polarizabilities are computed. The noise characteristics are manifested in an interesting way in the nonlinear polarizability components. In case of additive noise, the noise strength remains practically ineffective in influencing the optical responses. The situation completely changes with the replacement of additive noise by its multiplicative analog. The replacement enhances the nonlinear optical response dramatically and also causes their maximization at some typical value of noise strength that depends on oscillation frequency.
Optimal inversion of the generalized Anscombe transformation for Poisson-Gaussian noise.
Mäkitalo, Markku; Foi, Alessandro
2013-01-01
Many digital imaging devices operate by successive photon-to-electron, electron-to-voltage, and voltage-to-digit conversions. These processes are subject to various signal-dependent errors, which are typically modeled as Poisson-Gaussian noise. The removal of such noise can be effected indirectly by applying a variance-stabilizing transformation (VST) to the noisy data, denoising the stabilized data with a Gaussian denoising algorithm, and finally applying an inverse VST to the denoised data. The generalized Anscombe transformation (GAT) is often used for variance stabilization, but its unbiased inverse transformation has not been rigorously studied in the past. We introduce the exact unbiased inverse of the GAT and show that it plays an integral part in ensuring accurate denoising results. We demonstrate that this exact inverse leads to state-of-the-art results without any notable increase in the computational complexity compared to the other inverses. We also show that this inverse is optimal in the sense that it can be interpreted as a maximum likelihood inverse. Moreover, we thoroughly analyze the behavior of the proposed inverse, which also enables us to derive a closed-form approximation for it. This paper generalizes our work on the exact unbiased inverse of the Anscombe transformation, which we have presented earlier for the removal of pure Poisson noise.
Optimal inversion of the generalized Anscombe transformation for Poisson-Gaussian noise.
Mäkitalo, Markku; Foi, Alessandro
2013-01-01
Many digital imaging devices operate by successive photon-to-electron, electron-to-voltage, and voltage-to-digit conversions. These processes are subject to various signal-dependent errors, which are typically modeled as Poisson-Gaussian noise. The removal of such noise can be effected indirectly by applying a variance-stabilizing transformation (VST) to the noisy data, denoising the stabilized data with a Gaussian denoising algorithm, and finally applying an inverse VST to the denoised data. The generalized Anscombe transformation (GAT) is often used for variance stabilization, but its unbiased inverse transformation has not been rigorously studied in the past. We introduce the exact unbiased inverse of the GAT and show that it plays an integral part in ensuring accurate denoising results. We demonstrate that this exact inverse leads to state-of-the-art results without any notable increase in the computational complexity compared to the other inverses. We also show that this inverse is optimal in the sense that it can be interpreted as a maximum likelihood inverse. Moreover, we thoroughly analyze the behavior of the proposed inverse, which also enables us to derive a closed-form approximation for it. This paper generalizes our work on the exact unbiased inverse of the Anscombe transformation, which we have presented earlier for the removal of pure Poisson noise. PMID:22692910
Security of coherent-state quantum cryptography in the presence of Gaussian noise
Heid, Matthias; Luetkenhaus, Norbert
2007-08-15
We investigate the security against collective attacks of a continuous variable quantum key distribution scheme in the asymptotic key limit for a realistic setting. The quantum channel connecting the two honest parties is assumed to be lossy and imposes Gaussian noise on the observed quadrature distributions. Secret key rates are given for direct and reverse reconciliation schemes including post-selection in the collective attack scenario. The effect of a nonideal error correction and two-way communication in the classical post-processing step is also taken into account.
NASA Technical Reports Server (NTRS)
Painter, J. H.; Gupta, S. C.
1973-01-01
This paper presents the derivation of the recursive algorithms necessary for real-time digital detection of M-ary known signals that are subject to independent multiplicative and additive Gaussian noises. The motivating application is minimum probability of error detection of digital data-link messages aboard civil aircraft in the earth reflection multipath environment. For each known signal, the detector contains one Kalman filter and one probability computer. The filters estimate the multipath disturbance. The estimates and the received signal drive the probability computers. Outputs of all the computers are compared in amplitude to give the signal decision. The practicality and usefulness of the detector are extensively discussed.
Raghunathan, Shesha; Brun, Todd A.; Goan, Hsi-Sheng
2010-11-15
A promising technique for measuring single electron spins is magnetic resonance force microscopy (MRFM), in which a microcantilever with a permanent magnetic tip is resonantly driven by a single oscillating spin. The most effective experimental technique is the oscillating cantilever-driven adiabatic reversals (OSCAR) protocol, in which the signal takes the form of a frequency shift. If the quality factor of the cantilever is high enough, this signal will be amplified over time to the point where it can be detected by optical or other techniques. An important requirement, however, is that this measurement process occurs on a time scale that is short compared to any noise which disturbs the orientation of the measured spin. We describe a model of spin noise for the MRFM system and show how this noise is transformed to become time dependent in going to the usual rotating frame. We simplify the description of the cantilever-spin system by approximating the cantilever wave function as a Gaussian wave packet and show that the resulting approximation closely matches the full quantum behavior. We then examine the problem of detecting the signal for a cantilever with thermal noise and spin with spin noise, deriving a condition for this to be a useful measurement.
2-D impulse noise suppression by recursive gaussian maximum likelihood estimation.
Chen, Yang; Yang, Jian; Shu, Huazhong; Shi, Luyao; Wu, Jiasong; Luo, Limin; Coatrieux, Jean-Louis; Toumoulin, Christine
2014-01-01
An effective approach termed Recursive Gaussian Maximum Likelihood Estimation (RGMLE) is developed in this paper to suppress 2-D impulse noise. And two algorithms termed RGMLE-C and RGMLE-CS are derived by using spatially-adaptive variances, which are respectively estimated based on certainty and joint certainty & similarity information. To give reliable implementation of RGMLE-C and RGMLE-CS algorithms, a novel recursion stopping strategy is proposed by evaluating the estimation error of uncorrupted pixels. Numerical experiments on different noise densities show that the proposed two algorithms can lead to significantly better results than some typical median type filters. Efficient implementation is also realized via GPU (Graphic Processing Unit)-based parallelization techniques.
NASA Astrophysics Data System (ADS)
Ganguly, Jayanta; Ghosh, Manas
2015-07-01
We investigate the modulation of diagonal components of static linear (αxx, αyy) and first nonlinear (βxxx, βyyy) polarizabilities of quantum dots by Gaussian white noise. Quantum dot is doped with impurity represented by a Gaussian potential and repulsive in nature. The study reveals the importance of mode of application of noise (additive/multiplicative) on the polarizability components. The doped system is further exposed to a static external electric field of given intensity. As important observation we have found that the strength of additive noise becomes unable to influence the polarizability components. However, the multiplicative noise influences them conspicuously and gives rise to additional interesting features. Multiplicative noise even enhances the magnitude of the polarizability components immensely. The present investigation deems importance in view of the fact that noise seriously affects the optical properties of doped quantum dot devices.
NASA Astrophysics Data System (ADS)
Miao, Yan-Gang; Xu, Zhen-Ming
2016-04-01
Considering non-Gaussian smeared matter distributions, we investigate the thermodynamic behaviors of the noncommutative high-dimensional Schwarzschild-Tangherlini anti-de Sitter black hole, and we obtain the condition for the existence of extreme black holes. We indicate that the Gaussian smeared matter distribution, which is a special case of non-Gaussian smeared matter distributions, is not applicable for the six- and higher-dimensional black holes due to the hoop conjecture. In particular, the phase transition is analyzed in detail. Moreover, we point out that the Maxwell equal area law holds for the noncommutative black hole whose Hawking temperature is within a specific range, but fails for one whose the Hawking temperature is beyond this range.
Yang, Sejung; Lee, Byung-Uk
2015-01-01
In certain image acquisitions processes, like in fluorescence microscopy or astronomy, only a limited number of photons can be collected due to various physical constraints. The resulting images suffer from signal dependent noise, which can be modeled as a Poisson distribution, and a low signal-to-noise ratio. However, the majority of research on noise reduction algorithms focuses on signal independent Gaussian noise. In this paper, we model noise as a combination of Poisson and Gaussian probability distributions to construct a more accurate model and adopt the contourlet transform which provides a sparse representation of the directional components in images. We also apply hidden Markov models with a framework that neatly describes the spatial and interscale dependencies which are the properties of transformation coefficients of natural images. In this paper, an effective denoising algorithm for Poisson-Gaussian noise is proposed using the contourlet transform, hidden Markov models and noise estimation in the transform domain. We supplement the algorithm by cycle spinning and Wiener filtering for further improvements. We finally show experimental results with simulations and fluorescence microscopy images which demonstrate the improved performance of the proposed approach. PMID:26352138
Yang, Sejung; Lee, Byung-Uk
2015-01-01
In certain image acquisitions processes, like in fluorescence microscopy or astronomy, only a limited number of photons can be collected due to various physical constraints. The resulting images suffer from signal dependent noise, which can be modeled as a Poisson distribution, and a low signal-to-noise ratio. However, the majority of research on noise reduction algorithms focuses on signal independent Gaussian noise. In this paper, we model noise as a combination of Poisson and Gaussian probability distributions to construct a more accurate model and adopt the contourlet transform which provides a sparse representation of the directional components in images. We also apply hidden Markov models with a framework that neatly describes the spatial and interscale dependencies which are the properties of transformation coefficients of natural images. In this paper, an effective denoising algorithm for Poisson-Gaussian noise is proposed using the contourlet transform, hidden Markov models and noise estimation in the transform domain. We supplement the algorithm by cycle spinning and Wiener filtering for further improvements. We finally show experimental results with simulations and fluorescence microscopy images which demonstrate the improved performance of the proposed approach. PMID:26352138
Smolin, John A; Gambetta, Jay M; Smith, Graeme
2012-02-17
We provide an efficient method for computing the maximum-likelihood mixed quantum state (with density matrix ρ) given a set of measurement outcomes in a complete orthonormal operator basis subject to Gaussian noise. Our method works by first changing basis yielding a candidate density matrix μ which may have nonphysical (negative) eigenvalues, and then finding the nearest physical state under the 2-norm. Our algorithm takes at worst O(d(4)) for the basis change plus O(d(3)) for finding ρ where d is the dimension of the quantum state. In the special case where the measurement basis is strings of Pauli operators, the basis change takes only O(d(3)) as well. The workhorse of the algorithm is a new linear-time method for finding the closest probability distribution (in Euclidean distance) to a set of real numbers summing to one.
Error probabilities in optical PPM receivers with Gaussian mixture densities
NASA Technical Reports Server (NTRS)
Gagliardi, R. M.
1982-01-01
A Gaussian mixture density arises when a discrete variable (e.g., a photodetector count variable) is added to a continuous Gaussian variable (e.g., thermal noise). Making use of some properties of photomultiplier Gaussian mixture distributions, some approximate error probability formulas can be derived. These appear as averages of M-ary orthogonal Gaussian error probabilities. The use of a pure Gaussian assumption is considered, and when properly defined, appears as an accurate upper bound to performance.
NASA Astrophysics Data System (ADS)
Ganguly, Jayanta; Saha, Surajit; Pal, Suvajit; Ghosh, Manas
2016-03-01
We perform a meticulous analysis of profiles of third-order nonlinear optical susceptibility (TONOS) of impurity doped quantum dots (QDs) in the presence and absence of noise. We have invoked Gaussian white noise in the present study and noise has been introduced to the system additively and multiplicatively. The QD is doped with a Gaussian impurity. A magnetic field applied perpendicularly serves as a confinement source and the doped system has been exposed to a static external electric field. The TONOS profiles have been monitored against a continuous variation of incident photon energy when several important parameters such as electric field strength, magnetic field strength, confinement energy, dopant location, Al concentration, dopant potential, relaxation time, anisotropy, and noise strength assume different values. Moreover, the influence of mode of introduction of noise (additive/multiplicative) on the TONOS profiles has also been addressed. The said profiles are found to be consisting of interesting observations such as shift of TONOS peak position and maximization/minimization of TONOS peak intensity. The presence of noise alters the features of TONOS profiles and sometimes enhances the TONOS peak intensity from that of noise-free state. Furthermore, the mode of application of noise also often tailors the TONOS profiles in diverse fashions. The observations accentuate the possibility of tuning the TONOS of doped QD systems in the presence of noise.
Optimum receivers for pattern recognition in the presence of Gaussian noise with unknown statistics.
Towghi, N; Javidi, B
2001-08-01
We develop algorithms to detect a known pattern or a reference signal in the presence of additive, disjoint background, and multiplicative white Gaussian noise with unknown statistics. The presence of three different types of noise processes with unknown statistics presents difficulties in estimating the unknown parameters. The standard methods such as expected-maximization-type algorithms are iterative, and in the framework of hypothesis testing they are time-consuming, because corresponding to each hypothesis one must estimate a set of parameters. Other standard methods such as setting the gradient of the likelihood function with respect to the unknown parameters will lead to a nonlinear system of equations that do not have a closed-form solution and require iterative methods. We develop an approach to overcome these handicaps and derive algorithms to detect a known object. We present new methods to estimate unknown parameters within the framework of hypothesis testing. The methods that we present are direct and provide closed-form estimates of the unknown parameters. Computer simulations are used to show that for the images tested, the receivers that we have designed perform better than existing receivers. PMID:11488488
On stochastic complex beam beam interaction models with Gaussian colored noise
NASA Astrophysics Data System (ADS)
Xu, Yong; Zhang, Huiqing; Xu, Wei
2007-10-01
This paper is to continue our study on complex beam-beam interaction models in particle accelerators with random excitations Y. Xu, W. Xu, G.M. Mahmoud, On a complex beam-beam interaction model with random forcing [Physica A 336 (2004) 347-360]. The random noise is taken as the form of exponentially correlated Gaussian colored noise, and the transition probability density function is obtained in terms of a perturbation expansion of the parameter. Then the method of stochastic averaging based on perturbation technique is used to derive a Fokker-Planck equation for the transition probability density function. The solvability condition and the general transforms using the method of characteristics are proposed to obtain the approximate expressions of probability density function to order ε. Also the exact stationary probability density and the first and second moments of the amplitude are obtained, and one can find when the correlation time equals to zero, the result is identical to that derived from the Stratonovich-Khasminskii theorem for the same model under a broad-band excitation in our previous work.
Modular design and implementation of field-programmable-gate-array-based Gaussian noise generator
NASA Astrophysics Data System (ADS)
Li, Yuan-Ping; Lee, Ta-Sung; Hwang, Jeng-Kuang
2016-05-01
The modular design of a Gaussian noise generator (GNG) based on field-programmable gate array (FPGA) technology was studied. A new range reduction architecture was included in a series of elementary function evaluation modules and was integrated into the GNG system. The approximation and quantisation errors for the square root module with a first polynomial approximation were high; therefore, we used the central limit theorem (CLT) to improve the noise quality. This resulted in an output rate of one sample per clock cycle. We subsequently applied Newton's method for the square root module, thus eliminating the need for the use of the CLT because applying the CLT resulted in an output rate of two samples per clock cycle (>200 million samples per second). Two statistical tests confirmed that our GNG is of high quality. Furthermore, the range reduction, which is used to solve a limited interval of the function approximation algorithms of the System Generator platform using Xilinx FPGAs, appeared to have a higher numerical accuracy, was operated at >350 MHz, and can be suitably applied for any function evaluation.
Hamernik, Roger P; Qiu, Wei; Davis, Bob
2007-10-01
Sixteen groups of chinchillas (N=140) were exposed to various equivalent energy noise paradigms at 100 dB(A) or 103 dB(A) SPL. Eleven groups received an interrupted, intermittent, and time varying (IITV) non-Gaussian exposure quantified by the kurtosis statistic. The IITV exposures, which lasted for 8 hday, 5 daysweek for 3 weeks, were designed to model some of the essential features of an industrial workweek. Five equivalent energy reference groups were exposed to either a Gaussian or non-Gaussian 5 days, 24 hday continuous noise. Evoked potentials were used to estimate hearing thresholds and surface preparations of the organ of Corti quantified the sensory cell population. For IITV exposures at an equivalent energy and kurtosis, the temporal variations in level did not alter trauma and in some cases the IITV exposures produced results similar to those found for the 5 day continuous exposures. Any increase in kurtosis at a fixed energy was accompanied by an increase in noise-induced trauma. These results suggest that the equal energy hypothesis is an acceptable approach to evaluating noise exposures for hearing conservation purposes provided that the kurtosis of the amplitude distribution is taken into consideration. Temporal variations in noise levels seem to have little effect on trauma. PMID:17902860
Sonka, Milan; Abramoff, Michael D.
2013-01-01
In this paper, MMSE estimator is employed for noise-free 3D OCT data recovery in 3D complex wavelet domain. Since the proposed distribution for noise-free data plays a key role in the performance of MMSE estimator, a priori distribution for the pdf of noise-free 3D complex wavelet coefficients is proposed which is able to model the main statistical properties of wavelets. We model the coefficients with a mixture of two bivariate Gaussian pdfs with local parameters which are able to capture the heavy-tailed property and inter- and intrascale dependencies of coefficients. In addition, based on the special structure of OCT images, we use an anisotropic windowing procedure for local parameters estimation that results in visual quality improvement. On this base, several OCT despeckling algorithms are obtained based on using Gaussian/two-sided Rayleigh noise distribution and homomorphic/nonhomomorphic model. In order to evaluate the performance of the proposed algorithm, we use 156 selected ROIs from 650 × 512 × 128 OCT dataset in the presence of wet AMD pathology. Our simulations show that the best MMSE estimator using local bivariate mixture prior is for the nonhomomorphic model in the presence of Gaussian noise which results in an improvement of 7.8 ± 1.7 in CNR. PMID:24222760
Fokker-Planck description for a linear delayed Langevin equation with additive Gaussian noise
NASA Astrophysics Data System (ADS)
Giuggioli, Luca; McKetterick, Thomas John; Kenkre, V. M.; Chase, Matthew
2016-09-01
We construct an equivalent probability description of linear multi-delay Langevin equations subject to additive Gaussian white noise. By exploiting the time-convolutionless transform and a time variable transformation we are able to write a Fokker-Planck equation (FPE) for the 1-time and for the 2-time probability distributions valid irrespective of the regime of stability of the Langevin equations. We solve exactly the derived FPEs and analyze the aging dynamics by studying analytically the conditional probability distribution. We discuss explicitly why the initially conditioned distribution is not sufficient to describe fully out a non-Markov process as both preparation and observation times have bearing on its dynamics. As our analytic procedure can also be applied to linear Langevin equations with memory kernels, we compare the non-Markov dynamics of a one-delay system with that of a generalized Langevin equation with an exponential as well as a power law memory. Application to a generalization of the Green-Kubo formula is also presented.
Fokker–Planck description for a linear delayed Langevin equation with additive Gaussian noise
NASA Astrophysics Data System (ADS)
Giuggioli, Luca; McKetterick, Thomas John; Kenkre, V. M.; Chase, Matthew
2016-09-01
We construct an equivalent probability description of linear multi-delay Langevin equations subject to additive Gaussian white noise. By exploiting the time-convolutionless transform and a time variable transformation we are able to write a Fokker–Planck equation (FPE) for the 1-time and for the 2-time probability distributions valid irrespective of the regime of stability of the Langevin equations. We solve exactly the derived FPEs and analyze the aging dynamics by studying analytically the conditional probability distribution. We discuss explicitly why the initially conditioned distribution is not sufficient to describe fully out a non-Markov process as both preparation and observation times have bearing on its dynamics. As our analytic procedure can also be applied to linear Langevin equations with memory kernels, we compare the non-Markov dynamics of a one-delay system with that of a generalized Langevin equation with an exponential as well as a power law memory. Application to a generalization of the Green–Kubo formula is also presented.
NASA Astrophysics Data System (ADS)
Kishan, Harini; Seelamantula, Chandra Sekhar
2015-09-01
We propose optimal bilateral filtering techniques for Gaussian noise suppression in images. To achieve maximum denoising performance via optimal filter parameter selection, we adopt Stein's unbiased risk estimate (SURE)-an unbiased estimate of the mean-squared error (MSE). Unlike MSE, SURE is independent of the ground truth and can be used in practical scenarios where the ground truth is unavailable. In our recent work, we derived SURE expressions in the context of the bilateral filter and proposed SURE-optimal bilateral filter (SOBF). We selected the optimal parameters of SOBF using the SURE criterion. To further improve the denoising performance of SOBF, we propose variants of SOBF, namely, SURE-optimal multiresolution bilateral filter (SMBF), which involves optimal bilateral filtering in a wavelet framework, and SURE-optimal patch-based bilateral filter (SPBF), where the bilateral filter parameters are optimized on small image patches. Using SURE guarantees automated parameter selection. The multiresolution and localized denoising in SMBF and SPBF, respectively, yield superior denoising performance when compared with the globally optimal SOBF. Experimental validations and comparisons show that the proposed denoisers perform on par with some state-of-the-art denoising techniques.
Capacity of optical communication in loss and noise with general quantum Gaussian receivers
NASA Astrophysics Data System (ADS)
Takeoka, Masahiro; Guha, Saikat
2014-04-01
Laser-light (coherent-state) modulation is sufficient to achieve the ultimate (Holevo) capacity of classical communication over a lossy and noisy optical channel, but requires a receiver that jointly detects long modulated code words with highly nonlinear quantum operations, which are near-impossible to realize using current technology. We analyze the capacity of the lossy-noisy optical channel when the transmitter uses coherent-state modulation but the receiver is restricted to a general quantum-limited Gaussian receiver, i.e., one that may involve arbitrary combinations of Gaussian operations [passive linear optics: beam splitters and phase shifters; second-order nonlinear optics (or active linear optics): squeezers, along with homodyne or heterodyne detection measurements] and any amount of classical feedforward within the receiver. Under these assumptions, we show that the Gaussian receiver that attains the maximum mutual information is either homodyne detection, heterodyne detection, or time sharing between the two, depending upon the received power level. In other words, our result shows that to exceed the theoretical limit of conventional coherent optical communication, one has to incorporate non-Gaussian, i.e., third- or higher-order nonlinear operations in the receiver. Finally we compare our Gaussian receiver limit with experimentally feasible non-Gaussian receivers and show that in the regime of low received photon flux, it is possible to overcome the Gaussian receiver limit by relatively simple non-Gaussian receivers based on photon counting.
NASA Astrophysics Data System (ADS)
Waubke, Holger; Kasess, Christian H.
2016-11-01
Devices that emit structure-borne sound are commonly decoupled by elastic components to shield the environment from acoustical noise and vibrations. The elastic elements often have a hysteretic behavior that is typically neglected. In order to take hysteretic behavior into account, Bouc developed a differential equation for such materials, especially joints made of rubber or equipped with dampers. In this work, the Bouc model is solved by means of the Gaussian closure technique based on the Kolmogorov equation. Kolmogorov developed a method to derive probability density functions for arbitrary explicit first-order vector differential equations under white noise excitation using a partial differential equation of a multivariate conditional probability distribution. Up to now no analytical solution of the Kolmogorov equation in conjunction with the Bouc model exists. Therefore a wide range of approximate solutions, especially the statistical linearization, were developed. Using the Gaussian closure technique that is an approximation to the Kolmogorov equation assuming a multivariate Gaussian distribution an analytic solution is derived in this paper for the Bouc model. For the stationary case the two methods yield equivalent results, however, in contrast to statistical linearization the presented solution allows to calculate the transient behavior explicitly. Further, stationary case leads to an implicit set of equations that can be solved iteratively with a small number of iterations and without instabilities for specific parameter sets.
NASA Astrophysics Data System (ADS)
Coelho, A. S.; Barbosa, F. A. S.; Cassemiro, K. N.; Martinelli, M.; Villar, A. S.; Nussenzveig, P.
2015-07-01
Gaussian quantum states hold special importance in the continuous variable regime. In quantum information science, the understanding and characterization of central resources such as entanglement may strongly rely on the knowledge of the Gaussian or non-Gaussian character of the quantum state. However, the quantum measurement associated with the spectral photocurrent of light modes consists of a mixture of quadrature observables. Within the framework of two recent papers [Phys. Rev. A 88, 052113 (2013), 10.1103/PhysRevA.88.052113 and Phys. Rev. Lett. 111, 200402 (2013), 10.1103/PhysRevLett.111.200402], we address here how the statistics of the spectral photocurrent relates to the character of the Wigner function describing those modes. We show that a Gaussian state can be misidentified as non-Gaussian and vice versa, a conclusion that forces the adoption of tacit a priori assumptions to perform quantum state reconstruction. We experimentally analyze the light beams generated by the optical parametric oscillator operating above threshold to show that the data strongly supports the generation of Gaussian states of the field, validating the use of necessary and sufficient criteria to characterize entanglement in this system.
NASA Astrophysics Data System (ADS)
Kittisuwan, Pichid
2015-03-01
The application of image processing in industry has shown remarkable success over the last decade, for example, in security and telecommunication systems. The denoising of natural image corrupted by Gaussian noise is a classical problem in image processing. So, image denoising is an indispensable step during image processing. This paper is concerned with dual-tree complex wavelet-based image denoising using Bayesian techniques. One of the cruxes of the Bayesian image denoising algorithms is to estimate the statistical parameter of the image. Here, we employ maximum a posteriori (MAP) estimation to calculate local observed variance with generalized Gamma density prior for local observed variance and Laplacian or Gaussian distribution for noisy wavelet coefficients. Evidently, our selection of prior distribution is motivated by efficient and flexible properties of generalized Gamma density. The experimental results show that the proposed method yields good denoising results.
Memory texture as a mechanism of improvement in preference by adding noise
NASA Astrophysics Data System (ADS)
Zhao, Yinzhu; Aoki, Naokazu; Kobayashi, Hiroyuki
2014-02-01
According to color research, people have memory colors for familiar objects, which correlate with high color preference. As a similar concept to this, we propose memory texture as a mechanism of texture preference by adding image noise (1/f noise or white noise) to photographs of seven familiar objects. Our results showed that (1) memory texture differed from real-life texture; (2) no consistency was found between memory texture and real-life texture; (3) correlation existed between memory texture and preferred texture; and (4) the type of image noise which is more appropriate to texture reproduction differed by object.
NASA Technical Reports Server (NTRS)
Blasche, P. R.
1980-01-01
Specific configurations of first and second order all digital phase locked loops are analyzed for both ideal and additive white gaussian noise inputs. In addition, a design for a hardware digital phase locked loop capable of either first or second order operation is presented along with appropriate experimental data obtained from testing of the hardware loop. All parameters chosen for the analysis and the design of the digital phase locked loop are consistent with an application to an Omega navigation receiver although neither the analysis nor the design are limited to this application.
NASA Astrophysics Data System (ADS)
Pratim Ghosh, Arghya; Mandal, Arkajit; Sarkar, Sucharita; Ghosh, Manas
2016-05-01
We examine the influence of position-dependent effective mass (PDEM) on a few nonlinear optical (NLO) properties of impurity doped quantum dots (QDs) in presence and absence of noise. The said properties include total optical absorption coefficient (TOAC), nonlinear optical rectification (NOR), second harmonic generation (SHG) and third harmonic generation (THG). The impurity potential is modeled by a Gaussian function and the noise applied being Gaussian white noise. The profiles of above NLO properties have been pursued as a function of incident photon energy for different values of PDEM. Using PDEM the said profiles exhibit considerable departure from that of fixed effective mass (FEM). Presence of noise almost invariably amplifies the NLO properties with a few exceptions. A change in the mode of application of noise also sometimes affects the above profiles. The investigation furnishes us with a detailed picture of the subtle interplay between noise and PDEM through which the said NLO properties of doped QD systems can be tailored.
NASA Astrophysics Data System (ADS)
Yong-Ge, Yang; Wei, Xu; Ya-Hui, Sun; Xu-Dong, Gu
2016-02-01
This paper aims to investigate the stochastic response of the van der Pol (VDP) oscillator with two kinds of fractional derivatives under Gaussian white noise excitation. First, the fractional VDP oscillator is replaced by an equivalent VDP oscillator without fractional derivative terms by using the generalized harmonic balance technique. Then, the stochastic averaging method is applied to the equivalent VDP oscillator to obtain the analytical solution. Finally, the analytical solutions are validated by numerical results from the Monte Carlo simulation of the original fractional VDP oscillator. The numerical results not only demonstrate the accuracy of the proposed approach but also show that the fractional order, the fractional coefficient and the intensity of Gaussian white noise play important roles in the responses of the fractional VDP oscillator. An interesting phenomenon we found is that the effects of the fractional order of two kinds of fractional derivative items on the fractional stochastic systems are totally contrary. Project supported by the National Natural Science Foundation of China (Grant Nos. 11472212, 11532011, and 11502201).
NASA Technical Reports Server (NTRS)
Simon, M. K.; Huth, G. K.; Polydoros, A.
1982-01-01
Bandwidth-conserving modulation techniques, which trade average power for bandwidth in a favorable exchange, have recently found widespread application in digital radio and satellite communication systems. Quadrature amplitude-shift-keying (QASK) is a particular type of the considered techniques. QASK makes use of multilevel signals to amplitude modulate the in-phase and quadrature components of a carrier. Frequency hopping (FH) is used to protect a conventional communication system from radio frequency interference (RFI) or jamming. Differentially coherent detection provides a possible solution to the effect of phase discontinuities introduced by FH. The application of such a detection technique to QASK signals is discussed. A receiver structure is proposed and its symbol error probability performance for an additive white Gaussian noise (AWGN) background is investigated.
Power ratio estimation in incoherent backscatter lidar: direct detection with Gaussian noise.
Rye, B J
1989-09-01
Properties of small sample estimators for the return signal power ratio or log ratio in direct detection incoherent backscatter lidar systems are analyzed. As for heterodyne receivers it is usually preferable to form an estimator from the logarithmic difference of the sample averages rather than their ratio. Calculated values of bias and noise figures are confirmed using simulated data based on constant signal models and compared with the estimates obtained from nonlinear Kalman filters. The latter generally provide the least bias at high noise levels at the cost of greater computational complexity.
NASA Astrophysics Data System (ADS)
Ramon, Guy
2015-10-01
The effects of a collection of classical two-level charge fluctuators on the coherence of a dynamically decoupled qubit are studied. Distinct dynamics is found at different qubit working positions. Exact analytical formulas are derived at pure dephasing and approximate solutions are found at the general working position, for weakly and strongly coupled fluctuators. Analysis of these solutions, combined with numerical simulations of the multiple random telegraph processes, reveal the scaling of the noise with the number of fluctuators and the number of control pulses, as well as dependence on other parameters of the qubit-fluctuators system. These results can be used to determine potential microscopic models for the charge environment by performing noise spectroscopy.
NASA Astrophysics Data System (ADS)
Xiao, Yanwen; Xu, Wei; Wang, Liang
2016-03-01
This paper focuses on the study of the stochastic Van der Pol vibro-impact system with fractional derivative damping under Gaussian white noise excitation. The equations of the original system are simplified by non-smooth transformation. For the simplified equation, the stochastic averaging approach is applied to solve it. Then, the fractional derivative damping term is facilitated by a numerical scheme, therewith the fourth-order Runge-Kutta method is used to obtain the numerical results. And the numerical simulation results fit the analytical solutions. Therefore, the proposed analytical means to study this system are proved to be feasible. In this context, the effects on the response stationary probability density functions (PDFs) caused by noise excitation, restitution condition, and fractional derivative damping are considered, in addition the stochastic P-bifurcation is also explored in this paper through varying the value of the coefficient of fractional derivative damping and the restitution coefficient. These system parameters not only influence the response PDFs of this system but also can cause the stochastic P-bifurcation.
Xiao, Yanwen; Xu, Wei; Wang, Liang
2016-03-01
This paper focuses on the study of the stochastic Van der Pol vibro-impact system with fractional derivative damping under Gaussian white noise excitation. The equations of the original system are simplified by non-smooth transformation. For the simplified equation, the stochastic averaging approach is applied to solve it. Then, the fractional derivative damping term is facilitated by a numerical scheme, therewith the fourth-order Runge-Kutta method is used to obtain the numerical results. And the numerical simulation results fit the analytical solutions. Therefore, the proposed analytical means to study this system are proved to be feasible. In this context, the effects on the response stationary probability density functions (PDFs) caused by noise excitation, restitution condition, and fractional derivative damping are considered, in addition the stochastic P-bifurcation is also explored in this paper through varying the value of the coefficient of fractional derivative damping and the restitution coefficient. These system parameters not only influence the response PDFs of this system but also can cause the stochastic P-bifurcation. PMID:27036188
NASA Astrophysics Data System (ADS)
Saha, Surajit; Ganguly, Jayanta; Pal, Suvajit; Ghosh, Manas
2016-08-01
We study the modulation of electro-optic effect (EOE) of impurity doped QD under the influence of geometrical anisotropy and position-dependent effective mass (PDEM) in presence of Gaussian white noise. Always a comparison has been made between fixed effective mass (FEM) and PDEM to understand the role of the latter. In addition, the role of mode of application of noise (additive/multiplicative) has also been analyzed. The EOE profiles are found to be enriched with shift of peak position and maximization of peak intensity. The observations reveal sensitive interplay between noise and anisotropy/PDEM to fine-tune the features of EOE profiles.
Performance of peaky template matching under additive white Gaussian noise and uniform quantization
NASA Astrophysics Data System (ADS)
Horvath, Matthew S.; Rigling, Brian D.
2015-05-01
Peaky template matching (PTM) is a special case of a general algorithm known as multinomial pattern matching originally developed for automatic target recognition of synthetic aperture radar data. The algorithm is a model- based approach that first quantizes pixel values into Nq = 2 discrete values yielding generative Beta-Bernoulli models as class-conditional templates. Here, we consider the case of classification of target chips in AWGN and develop approximations to image-to-template classification performance as a function of the noise power. We focus specifically on the case of a uniform quantization" scheme, where a fixed number of the largest pixels are quantized high as opposed to using a fixed threshold. This quantization method reduces sensitivity to the scaling of pixel intensities and quantization in general reduces sensitivity to various nuisance parameters difficult to account for a priori. Our performance expressions are verified using forward-looking infrared imagery from the Army Research Laboratory Comanche dataset.
NASA Astrophysics Data System (ADS)
Chaudhury, Srabanti; Cherayil, Binny J.
2006-09-01
The distribution of waiting times, f(t ), between successive turnovers in the catalytic action of single molecules of the enzyme β-galactosidase has recently been determined in closed form by Chaudhury and Cherayil [J. Chem. Phys. 125, 024904 (2006)] using a one-dimensional generalized Langevin equation (GLE) formalism in combination with Kramers' flux-over-population approach to barrier crossing dynamics. The present paper provides an alternative derivation of f(t ) that eschews this approach, which is strictly applicable only under conditions of local equilibrium. In this alternative derivation, a double well potential is incorporated into the GLE, along with a colored noise term representing protein conformational fluctuations, and the resulting equation transformed approximately to a Smoluchowski-type equation. f(t ) is identified with the first passage time distribution for a particle to reach the barrier top starting from an equilibrium distribution of initial points, and is determined from the solution of the above equation using local boundary conditions. The use of such boundary conditions is necessitated by the absence of definite information about the precise nature of the boundary conditions applicable to stochastic processes governed by non-Markovian dynamics. f(t ) calculated in this way is found to have the same analytic structure as the distribution calculated by the flux-over-population method.
Rhazi, Dilal; Atalla, Noureddine
2014-02-01
The evaluation of the acoustic performance of noise control treatments is of great importance in many engineering applications, e.g., aircraft, automotive, and building acoustics applications. Numerical methods such as finite- and boundary elements allow for the study of complex structures with added noise control treatment. However, these methods are computationally expensive when used for complex structures. At an early stage of the acoustic trim design process, many industries look for simple and easy to use tools that provide sufficient physical insight that can help to formulate design criteria. The paper presents a simple and tractable approach for the acoustic design of noise control treatments. It presents and compares two transfer matrix-based methods to investigate the vibroacoustic behavior of noise control treatments. The first is based on a modal approach, while the second is based on wave-number space decomposition. In addition to the classical rain-on-the-roof and diffuse acoustic field excitations, the paper also addresses turbulent boundary layer and point source (monopole) excitations. Various examples are presented and compared to a finite element calculation to validate the methodology and to confirm its relevance along with its limitations. PMID:25234878
NASA Astrophysics Data System (ADS)
Martellini, Lionel; Regimbau, Tania
2015-11-01
Under standard assumptions including stationary and serially uncorrelated Gaussian gravitational wave stochastic background signal and noise distributions, as well as homogenous detector sensitivities, the standard cross-correlation detection statistic is known to be optimal in the sense of minimizing the probability of a false dismissal at a fixed value of the probability of a false alarm. The focus of this paper is to analyze the comparative efficiency of this statistic, vs a simple alternative statistic obtained by cross-correlating the squared measurements, in situations that deviate from such standard assumptions. We find that differences in detector sensitivities have a large impact on the comparative efficiency of the cross-correlation detection statistic, which is dominated by the alternative statistic when these differences reach 1 order of magnitude. This effect holds even when both the signal and noise distributions are Gaussian. While the presence of non-Gaussian signals has no material impact for reasonable parameter values, the relative inefficiency of the cross-correlation statistic is less prominent for fat-tailed noise distributions, but it is magnified in case noise distributions have skewness parameters of opposite signs. Our results suggest that introducing an alternative detection statistic can lead to noticeable sensitivity gains when noise distributions are possibly non-Gaussian and/or when detector sensitivities exhibit substantial differences, a situation that is expected to hold in joint detections from Advanced LIGO and Advanced Virgo, in particular in the early phases of development of the detectors, or in joint detections from Advanced LIGO and the Einstein Telescope.
NASA Astrophysics Data System (ADS)
Almeida, Telmo P.; Drummond, Miguel V.; Pavlović, Natasa B.; André, Paulo S.; Nogueira, Rogério N.
2014-08-01
Of all the non-linear fiber propagation models proposed over the years, the Gaussian Noise (GN) model is growing in popularity due to its simplicity and yet reliability when it comes to predict performance of uncompensated coherent transmission (UT) systems that rely on state-of-the art digital-signal processing (DSP) for dispersion compensation. However, many of the systems currently deployed rely on optical CD compensation. Overhauling or upgrading these systems with the most recent DSP is not always feasible. In this context, it is important to broad the range of the GNmodel to dispersion managed (DM) systems, so both scenarios can benefit from a low complexity, fast and reliable performance prediction tool. In this paper, we validate the first results comparing the performance in both accuracy and simulation time of the GN model simulating a realistic DM scenario that relies on periodical spans of non-dispersion shifted fiber (NDSF) to perform the dispersion compensation. The same realistic scenarios were modeled with commercial software and the GN model. The objective was to predict the optimal launch power for different link lengths, central wavelengths and channel spacing values. Preliminary results obtained with the GN model are in good agreement with the ones from the commercial software for several link distances tested up to 2400 Km.
The Parkes front-end controller and noise-adding radiometer
NASA Technical Reports Server (NTRS)
Brunzie, T. J.
1990-01-01
A new front-end controller (FEC) was installed on the 64-m antenna in Parkes, Australia, to support the 1989 Voyager 2 Neptune encounter. The FEC was added to automate operation of the front-end microwave hardware as part of the Deep Space Network's Parkes-Canberra Telemetry Array. Much of the front-end hardware was refurbished and reimplemented from a front-end system installed in 1985 by the European Space Agency for the Uranus encounter; however, the FEC and its associated noise-adding radiometer (NAR) were new Jet Propulsion Laboratory (JPL) designs. Project requirements and other factors led to the development of capabilities not found in standard Deep Space Network (DSN) controllers and radiometers. The Parkes FEC/NAR performed satisfactorily throughout the Neptune encounter and was removed in October 1989.
NASA Astrophysics Data System (ADS)
Arz, Jean-Pierre
The starting point of this Ph.D. is the industrial issue submitted to the ETS by the company Bombardier Recreational Products (BRP) of the noise reduction of the tracked drive mechanism of snowmobiles. The overall goal of is to develop a method to predict the impact noise reduction obtained by the adding of an elastomeric layer specimen of small thickness between the impacting body and the impacted structure which is a complex structure (i.e. a structure whose geometry is complex and whose composition involves several materials). To reach this overall goal, three specific goals have been fixed: (1) characterize the behavior under impact of different small thickness elastomeric layers; (2) predict the impact force generated when an elastomeric layer is added on a complex vibrating structure; and (3) validate experimentally the whole method by applying it to the impact noise reduction of a bar of the snowmobile track. To reach the first specific goal (characterize the behavior under impact of different small thickness elastomeric layers), a specific experimental characterization method has been developed. Firstly, an experimental device has been realized to submit the elastomeric layer specimens to the reproducible impact conditions of an impact hammer. The measurement of the penetration depth of the hammer into the elastomeric layer is achieved by recording its motion with a high-speed camera and by detecting its position by further analysis on the individual images. Secondly, the experimental curves obtained are analyzed to point out their main characteristics and choose an appropriate impact model. Thirdly, the contact force parameters are estimated from the experimental results and from the impact model. Using this method, eight impacted elastomeric specimens have been characterized. The results show that a more precise characterization than hardness is obtained. To reach the second specific goal (predict the impact force generated when an elastomeric layer is
Himemoto, Yoshiaki; Hiramatsu, Takashi; Taruya, Atsushi; Kudoh, Hideaki
2007-01-15
We discuss a robust data analysis method to detect a stochastic background of gravitational waves in the presence of non-Gaussian noise. In contrast to the standard cross-correlation (SCC) statistic frequently used in the stochastic background searches, we consider a generalized cross-correlation (GCC) statistic, which is nearly optimal even in the presence of non-Gaussian noise. The detection efficiency of the GCC statistic is investigated analytically, particularly focusing on the statistical relation between the false-alarm and the false-dismissal probabilities, and the minimum detectable amplitude of gravitational-wave signals. We derive simple analytic formulas for these statistical quantities. The robustness of the GCC statistic is clarified based on these formulas, and one finds that the detection efficiency of the GCC statistic roughly corresponds to the one of the SCC statistic neglecting the contribution of non-Gaussian tails. This remarkable property is checked by performing the Monte Carlo simulations and successful agreement between analytic and simulation results was found.
Voronenko, Sergej O; Stannat, Wilhelm; Lindner, Benjamin
2015-12-01
We study a population of spiking neurons which are subject to independent noise processes and a strong common time-dependent input. We show that the response of output spikes to independent noise shapes information transmission of such populations even when information transmission properties of single neurons are left unchanged. In particular, we consider two Poisson models in which independent noise either (i) adds and deletes spikes (AD model) or (ii) shifts spike times (STS model). We show that in both models suprathreshold stochastic resonance (SSR) can be observed, where the information transmitted by a neural population is increased with addition of independent noise. In the AD model, the presence of the SSR effect is robust and independent of the population size or the noise spectral statistics. In the STS model, the information transmission properties of the population are determined by the spectral statistics of the noise, leading to a strongly increased effect of SSR in some regimes, or an absence of SSR in others. Furthermore, we observe a high-pass filtering of information in the STS model that is absent in the AD model. We quantify information transmission by means of the lower bound on the mutual information rate and the spectral coherence function. To this end, we derive the signal-output cross-spectrum, the output power spectrum, and the cross-spectrum of two spike trains for both models analytically. PMID:26458900
NASA Astrophysics Data System (ADS)
Kleine, Achim
Models were developed to investigate the tracking behavior of combined Costas/AFC (Automatic Frequency Control) feedback loops under Rayleigh/Rician fading conditions with additive Gaussian noise jamming. A general linearized tracking model was developed for land-mobile channels. The model can be used for the nonlinearized case with sinusoidal phase detection characteristic using a standard solution of the Fokker-Planck equation. A tracking analysis for Costas/AFC loops with coherent automatic gain control, and an accuracy analysis for interferometers equipped with Costas/AFC loops are treated as examples. The tracking model is the most inaccurate in the case of quasistationary channels.
NASA Technical Reports Server (NTRS)
Lokshin, A.; Olsen, E. T.
1984-01-01
The scalloping problem for the case of Gaussian noise statistics is analyzed. The optimal weighting strategy for linearly combining two observations in adjacent beam areas is derived, and the sensitivity and scalloping for this weighting strategy are compared with those realized using a single observation or using equal weighting of two observations. The variation of the probability for detecting ETI signals with scan separation are calculated for the various weighting strategies, assuming that the transmitters are of equal strength and are uniformly distributed throughout space.
C. Lo D. D. Turner R. O. Knuteson
2006-01-31
This technical report provide a short description of the application of the principle component analysis techniques to remove uncorrelated random noise from ground-based high spectral resolution infrared radiance observations collected by the atmospheric emitted radiance interferometers (AERIs) deployed by the Atmospheric Radiation Measurement (ARM) Program. A general overview of the technique, the input, and output datastreams of the newly generated value-added product, and the data quality checks used are provided. A more complete discussion of the theory and results is given in Turner et al. (2006).
Added noise due to the effect of an upstream wake on a propeller
NASA Astrophysics Data System (ADS)
Takallu, M. A.; Spence, P. L.; Block, P. J. W.
1987-10-01
An analytical/computational study has been conducted to predict the effect of an upstream wing or pylon on the noise of an operating propeller. The wing trailing edge was placed at variable distances (0.1 and 0.3 chord) upstream of a scaled model propeller (SR-2). The wake was modeled using a similarity formulation. The instantaneous pressure distribution on the propeller blades during the passage through the wake was formulated in terms of a time-dependent variation of each blade section's angle of attack and in terms of the shed vortices from the blade trailing edge. It was found that the final expressions for the unsteady loads considerably altered the radiated noise pattern. Predicted noise for various observer positions, rotational speeds, and propeller/pylon distances were computed and are presented in terms of the pressure time history, harmonics of the Fourier analysis, and overall sound pressure levels (OASPL). The addition of the tangential stress due to skin friction was found to have a damping effect on the acoustic pressure time history and the resulting spectrum of the generated noise. It is shown that the positioning of a pylon upstream of a propeller indeed increases the overall noise.
How much image noise can be added in cardiac x-ray imaging without loss in perceived image quality?
NASA Astrophysics Data System (ADS)
Gislason-Lee, Amber J.; Kumcu, Asli; Kengyelics, Stephen M.; Rhodes, Laura A.; Davies, Andrew G.
2015-03-01
Dynamic X-ray imaging systems are used for interventional cardiac procedures to treat coronary heart disease. X-ray settings are controlled automatically by specially-designed X-ray dose control mechanisms whose role is to ensure an adequate level of image quality is maintained with an acceptable radiation dose to the patient. Current commonplace dose control designs quantify image quality by performing a simple technical measurement directly from the image. However, the utility of cardiac X-ray images is in their interpretation by a cardiologist during an interventional procedure, rather than in a technical measurement. With the long term goal of devising a clinically-relevant image quality metric for an intelligent dose control system, we aim to investigate the relationship of image noise with clinical professionals' perception of dynamic image sequences. Computer-generated noise was added, in incremental amounts, to angiograms of five different patients selected to represent the range of adult cardiac patient sizes. A two alternative forced choice staircase experiment was used to determine the amount of noise which can be added to a patient image sequences without changing image quality as perceived by clinical professionals. Twenty-five viewing sessions (five for each patient) were completed by thirteen observers. Results demonstrated scope to increase the noise of cardiac X-ray images by up to 21% +/- 8% before it is noticeable by clinical professionals. This indicates a potential for 21% radiation dose reduction since X-ray image noise and radiation dose are directly related; this would be beneficial to both patients and personnel.
NASA Astrophysics Data System (ADS)
Azad, Nasser L.; Mozaffari, Ahmad
2015-12-01
The main scope of the current study is to develop a systematic stochastic model to capture the undesired uncertainty and random noises on the key parameters affecting the catalyst temperature over the coldstart operation of automotive engine systems. In the recent years, a number of articles have been published which aim at the modeling and analysis of automotive engines' behavior during coldstart operations by using regression modeling methods. Regarding highly nonlinear and uncertain nature of the coldstart operation, calibration of the engine system's variables, for instance the catalyst temperature, is deemed to be an intricate task, and it is unlikely to develop an exact physics-based nonlinear model. This encourages automotive engineers to take advantage of knowledge-based modeling tools and regression approaches. However, there exist rare reports which propose an efficient tool for coping with the uncertainty associated with the collected database. Here, the authors introduce a random noise to experimentally derived data and simulate an uncertain database as a representative of the engine system's behavior over coldstart operations. Then, by using a Gaussian process regression machine (GPRM), a reliable model is used for the sake of analysis of the engine's behavior. The simulation results attest the efficacy of GPRM for the considered case study. The research outcomes confirm that it is possible to develop a practical calibration tool which can be reliably used for modeling the catalyst temperature.
NASA Astrophysics Data System (ADS)
YAMAGUCHI, S.; OIMATSU, K.; SAEKI, T.
2001-03-01
For the purpose of designing the underwater transmission system using audible sound directly projected by underwater loudspeaker to prevent a diving accident and/or to give a working instruction, it is important to estimate the transmission loss from a wall not only for pure tones but also for wideband signals such as voice and noise. On the other hand, the sound pressure waves of voice signal and noise usually exhibit the non-stationary property with temporal changes of various statistical moments. Furthermore, the sound propagation environment between the sound source and the observation point shows non-stationarity, caused by temporal changes of the sound propagation path, etc. From the above practical viewpoints, in this paper, an approximate expression for the probability density function of transmitted sound power fluctuation is theoretically derived, in the case when a Gaussian-type non-stationary random sound pressure wave with fluctuation of variance is passed through a single wall in underwater. The validity and the usefulness of the theoretical method is confirmed by experiments conducted in a water tank.
Mohamed Salleh, Faridah Hani; Arif, Shereena Mohd; Zainudin, Suhaila; Firdaus-Raih, Mohd
2015-12-01
A gene regulatory network (GRN) is a large and complex network consisting of interacting elements that, over time, affect each other's state. The dynamics of complex gene regulatory processes are difficult to understand using intuitive approaches alone. To overcome this problem, we propose an algorithm for inferring the regulatory interactions from knock-out data using a Gaussian model combines with Pearson Correlation Coefficient (PCC). There are several problems relating to GRN construction that have been outlined in this paper. We demonstrated the ability of our proposed method to (1) predict the presence of regulatory interactions between genes, (2) their directionality and (3) their states (activation or suppression). The algorithm was applied to network sizes of 10 and 50 genes from DREAM3 datasets and network sizes of 10 from DREAM4 datasets. The predicted networks were evaluated based on AUROC and AUPR. We discovered that high false positive values were generated by our GRN prediction methods because the indirect regulations have been wrongly predicted as true relationships. We achieved satisfactory results as the majority of sub-networks achieved AUROC values above 0.5.
A low-noise wide-dynamic-range UV detector with pixel-level A/D conversion
NASA Astrophysics Data System (ADS)
Zhang, Wenjing; Bao, Xichang; Wang, Ling; Li, Chao; Yuan, Yonggang; Li, Xiangyang
2009-07-01
This paper presents a low-power low-noise wide-dynamic-range GaN-based UV detector with pixel-level A/D conversion. The detector comprised an array of 50×50μm2 pixels with a multi-channel bit serial (MCBS) ADC in each pixel. Each pixel contains a UV photo-detector, a 1-bit comparator and a 3-T memory cell. The A/D conversion is performed simultaneously for all pixels. The digital data is read out from the pixel array in manner of a random access digital memory. Since there are many ADCs operating simultaneously, power consumption for each ADC must be minimized. To satisfy the low power consumption, A power-down circuit is introduced in. The minimal value for ADC resolution and the frame rate are 10bits and 100f/s respectively. A high GBW comparator is designed to satisfy this demand. In order to suppress the FPN and 1/f noise a digital correlated double sampling (CDS) is adopted in this application.
NASA Astrophysics Data System (ADS)
Ranjha, Bilal; Zhou, Zhou; Kavehrad, Mohsen
2014-08-01
We have compared the bit error rate (BER) performance of precoding-based asymmetrically clipped optical orthogonal frequency division multiplexing (ACO-OFDM) and pulse amplitude modulated discrete multitone (PAM-DMT) optical wireless (OW) systems in additive white Gaussian noise (AWGN) and indoor multipath frequency selective channel. Simulation and analytical results show that precoding schemes such as discrete Fourier transform, discrete cosine transform, and Zadoff-Chu sequences do not affect the performance of the OW systems in the AWGN channel while they do reduce the peak-to-average power ratio (PAPR) of the OFDM output signal. However, in a multipath indoor channel, using zero forcing frequency domain equalization precoding-based systems give better BER performance than their conventional counterparts. With additional clipping to further reduce the PAPR, precoding-based systems also show better BER performance compared to nonprecoded systems when clipped relative to the peak of nonprecoded systems. Therefore, precoding-based ACO-OFDM and PAM-DMT systems offer better BER performance, zero signaling overhead, and low PAPR compared to conventional systems.
Breaking Gaussian incompatibility on continuous variable quantum systems
Heinosaari, Teiko; Kiukas, Jukka; Schultz, Jussi
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.
Rost, Gwyneth C.; McMurray, Bob
2013-01-01
It is well attested that 14-month olds have difficulty learning similar sounding words (e.g. bih/dih), despite their excellent phonetic discrimination abilities. In contrast, Rost and McMurray (2009) recently demonstrated that 14-month olds’ minimal pair learning can be improved by the presentation of words by multiple talkers. This study investigates which components of the variability found in multi-talker input improved infants’ processing, assessing both the phonologically contrastive aspects of the speech stream and phonologically irrelevant indexical and suprasegmental aspects. In the first two experiments, speaker was held constant while cues to word-initial voicing were systematically manipulated. Infants failed in both cases. The third experiment introduced variability in speaker, but voicing cues were invariant within each category. Infants in this condition learned the words. We conclude that aspects of the speech signal that have been typically thought of as noise are in fact valuable information – signal – for the young word learner. PMID:24358016
Noise-Assisted Concurrent Multipath Traffic Distribution in Ad Hoc Networks
Murata, Masayuki
2013-01-01
The concept of biologically inspired networking has been introduced to tackle unpredictable and unstable situations in computer networks, especially in wireless ad hoc networks where network conditions are continuously changing, resulting in the need of robustness and adaptability of control methods. Unfortunately, existing methods often rely heavily on the detailed knowledge of each network component and the preconfigured, that is, fine-tuned, parameters. In this paper, we utilize a new concept, called attractor perturbation (AP), which enables controlling the network performance using only end-to-end information. Based on AP, we propose a concurrent multipath traffic distribution method, which aims at lowering the average end-to-end delay by only adjusting the transmission rate on each path. We demonstrate through simulations that, by utilizing the attractor perturbation relationship, the proposed method achieves a lower average end-to-end delay compared to other methods which do not take fluctuations into account. PMID:24319375
Noise-assisted concurrent multipath traffic distribution in ad hoc networks.
Asvarujanon, Narun; Leibnitz, Kenji; Wakamiya, Naoki; Murata, Masayuki
2013-01-01
The concept of biologically inspired networking has been introduced to tackle unpredictable and unstable situations in computer networks, especially in wireless ad hoc networks where network conditions are continuously changing, resulting in the need of robustness and adaptability of control methods. Unfortunately, existing methods often rely heavily on the detailed knowledge of each network component and the preconfigured, that is, fine-tuned, parameters. In this paper, we utilize a new concept, called attractor perturbation (AP), which enables controlling the network performance using only end-to-end information. Based on AP, we propose a concurrent multipath traffic distribution method, which aims at lowering the average end-to-end delay by only adjusting the transmission rate on each path. We demonstrate through simulations that, by utilizing the attractor perturbation relationship, the proposed method achieves a lower average end-to-end delay compared to other methods which do not take fluctuations into account.
Yan, Haixia; Qin, Kairong; Wang, Yiqin; Li, Fufeng; Run, Fengying; Hong, Yujian; Hao, Jiming
2011-02-01
The ensemble empirical mode decomposition (EEMD) can be used to overcome the mode mixing problem of empirical mode decomposition (EMD) effectively. The EEMD method and Hilbert-Huang Transform (HHT) can be used to analyze pulse signals of Traditional Chinese Medicine (TCM). The amplitudes of the added white noise were about 0.1 and 0.2 time standard deviation of the investigated signal respectively. The difference of average frequency and average energy of every mode between normal pulse, slippery pulse, wiry pulse and wiry-slippery pulse were demonstrated based on different amplitudes of the added white noise. The results showed that it is more in line with clinical practice when the amplitude of the added white noise is about 0.2 time standard deviation of the investigated signal.
Autonomous Gaussian Decomposition
NASA Astrophysics Data System (ADS)
Lindner, Robert R.; Vera-Ciro, Carlos; Murray, Claire E.; Stanimirović, Snežana; Babler, Brian; Heiles, Carl; Hennebelle, Patrick; Goss, W. M.; Dickey, John
2015-04-01
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
Lindner, Robert R.; Vera-Ciro, Carlos; Murray, Claire E.; Stanimirović, Snežana; Babler, Brian; Heiles, Carl; Hennebelle, Patrick; Dickey, John
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.
NASA Astrophysics Data System (ADS)
Saha, Surajit; Pal, Suvajit; Ganguly, Jayanta; Ghosh, Manas
2016-03-01
We inspect the influence of position-dependent effective mass (PDEM) on the third-order nonlinear optical susceptibility (TONOS) of impurity doped quantum dots (QDs) in the presence and absence of noise. The TONOS profiles have been followed as a function of incident photon energy for different values of PDEM. Using PDEM the said profile considerably deviates from that of fixed effective mass (FEM). However, a switch from one mode of application of noise to another primarily alters the TONOS peak intensity. The observations highlight the possibility of tuning the TONOS profiles of doped QD systems exploiting noise and PDEM.
NASA Astrophysics Data System (ADS)
Gao, Ting; Duan, Jinqiao
2016-10-01
Complex systems are sometimes subject to non-Gaussian α-stable Lévy fluctuations. A new method is devised to estimate the uncertain parameter α and other system parameters, using observations on mean exit time or escape probability for the system evolution. It is based on solving an inverse problem for a deterministic, nonlocal partial differential equation via numerical optimization. The existing methods for estimating parameters require observations on system state sample paths for long time periods or probability densities at large spatial ranges. The method proposed here, instead, requires observations on mean exit time or escape probability only for an arbitrarily small spatial domain. This new method is beneficial to systems for which mean exit time or escape probability is feasible to observe.
NASA Astrophysics Data System (ADS)
Kayahan, Huseyin; Ceylan, Ömer; Yazici, Melik; Gurbuz, Yasar
2014-06-01
This paper presents a digital ROIC for staring type arrays with extending counting method to realize very low quantization noise while achieving a very high charge handling capacity. Current state of the art has shown that digital readouts with pulse frequency method can achieve charge handling capacities higher than 3Ge- with quantization noise higher than 1000e-. Even if the integration capacitance is reduced, it cannot be lower than 1-3 fF due to the parasitic capacitance of the comparator. For achieving a very low quantization noise of 161 electrons in a power efficient way, a new method based on measuring the time to measure the remaining charge on the integration capacitor is proposed. With this approach SNR of low flux pixels are significantly increased while large flux pixels can store electrons as high as 2.33Ge-. A prototype array of 32×32 pixels with 30μm pitch is implemented in 90nm CMOS process technology for verification. Measurement results are given for complete readout.
Noise-enhanced convolutional neural networks.
Audhkhasi, Kartik; Osoba, Osonde; Kosko, Bart
2016-06-01
Injecting carefully chosen noise can speed convergence in the backpropagation training of a convolutional neural network (CNN). The Noisy CNN algorithm speeds training on average because the backpropagation algorithm is a special case of the generalized expectation-maximization (EM) algorithm and because such carefully chosen noise always speeds up the EM algorithm on average. The CNN framework gives a practical way to learn and recognize images because backpropagation scales with training data. It has only linear time complexity in the number of training samples. The Noisy CNN algorithm finds a special separating hyperplane in the network's noise space. The hyperplane arises from the likelihood-based positivity condition that noise-boosts the EM algorithm. The hyperplane cuts through a uniform-noise hypercube or Gaussian ball in the noise space depending on the type of noise used. Noise chosen from above the hyperplane speeds training on average. Noise chosen from below slows it on average. The algorithm can inject noise anywhere in the multilayered network. Adding noise to the output neurons reduced the average per-iteration training-set cross entropy by 39% on a standard MNIST image test set of handwritten digits. It also reduced the average per-iteration training-set classification error by 47%. Adding noise to the hidden layers can also reduce these performance measures. The noise benefit is most pronounced for smaller data sets because the largest EM hill-climbing gains tend to occur in the first few iterations. This noise effect can assist random sampling from large data sets because it allows a smaller random sample to give the same or better performance than a noiseless sample gives.
NASA Astrophysics Data System (ADS)
Zhao, Fan; Zhao, Jian; Zhao, Wenda; Qu, Feng
2016-05-01
Infrared images are characterized by low signal-to-noise ratio and low contrast. Therefore, the edge details are easily immerged in the background and noise, making it much difficult to achieve infrared image edge detail enhancement and denoising. This article proposes a novel method of Gaussian mixture model-based gradient field reconstruction, which enhances image edge details while suppressing noise. First, by analyzing the gradient histogram of noisy infrared image, Gaussian mixture model is adopted to simulate the distribution of the gradient histogram, and divides the image information into three parts corresponding to faint details, noise and the edges of clear targets, respectively. Then, the piecewise function is constructed based on the characteristics of the image to increase gradients of faint details and suppress gradients of noise. Finally, anisotropic diffusion constraint is added while visualizing enhanced image from the transformed gradient field to further suppress noise. The experimental results show that the method possesses unique advantage of effectively enhancing infrared image edge details and suppressing noise as well, compared with the existing methods. In addition, it can be used to effectively enhance other types of images such as the visible and medical images.
Gaussian Decomposition of Laser Altimeter Waveforms
NASA Technical Reports Server (NTRS)
Hofton, Michelle A.; Minster, J. Bernard; Blair, J. Bryan
1999-01-01
We develop a method to decompose a laser altimeter return waveform into its Gaussian components assuming that the position of each Gaussian within the waveform can be used to calculate the mean elevation of a specific reflecting surface within the laser footprint. We estimate the number of Gaussian components from the number of inflection points of a smoothed copy of the laser waveform, and obtain initial estimates of the Gaussian half-widths and positions from the positions of its consecutive inflection points. Initial amplitude estimates are obtained using a non-negative least-squares method. To reduce the likelihood of fitting the background noise within the waveform and to minimize the number of Gaussians needed in the approximation, we rank the "importance" of each Gaussian in the decomposition using its initial half-width and amplitude estimates. The initial parameter estimates of all Gaussians ranked "important" are optimized using the Levenburg-Marquardt method. If the sum of the Gaussians does not approximate the return waveform to a prescribed accuracy, then additional Gaussians are included in the optimization procedure. The Gaussian decomposition method is demonstrated on data collected by the airborne Laser Vegetation Imaging Sensor (LVIS) in October 1997 over the Sequoia National Forest, California.
A two-step A/D conversion and column self-calibration technique for low noise CMOS image sensors.
Bae, Jaeyoung; Kim, Daeyun; Ham, Seokheon; Chae, Youngcheol; Song, Minkyu
2014-01-01
In this paper, a 120 frames per second (fps) low noise CMOS Image Sensor (CIS) based on a Two-Step Single Slope ADC (TS SS ADC) and column self-calibration technique is proposed. The TS SS ADC is suitable for high speed video systems because its conversion speed is much faster (by more than 10 times) than that of the Single Slope ADC (SS ADC). However, there exist some mismatching errors between the coarse block and the fine block due to the 2-step operation of the TS SS ADC. In general, this makes it difficult to implement the TS SS ADC beyond a 10-bit resolution. In order to improve such errors, a new 4-input comparator is discussed and a high resolution TS SS ADC is proposed. Further, a feedback circuit that enables column self-calibration to reduce the Fixed Pattern Noise (FPN) is also described. The proposed chip has been fabricated with 0.13 μm Samsung CIS technology and the chip satisfies the VGA resolution. The pixel is based on the 4-TR Active Pixel Sensor (APS). The high frame rate of 120 fps is achieved at the VGA resolution. The measured FPN is 0.38 LSB, and measured dynamic range is about 64.6 dB.
A two-step A/D conversion and column self-calibration technique for low noise CMOS image sensors.
Bae, Jaeyoung; Kim, Daeyun; Ham, Seokheon; Chae, Youngcheol; Song, Minkyu
2014-01-01
In this paper, a 120 frames per second (fps) low noise CMOS Image Sensor (CIS) based on a Two-Step Single Slope ADC (TS SS ADC) and column self-calibration technique is proposed. The TS SS ADC is suitable for high speed video systems because its conversion speed is much faster (by more than 10 times) than that of the Single Slope ADC (SS ADC). However, there exist some mismatching errors between the coarse block and the fine block due to the 2-step operation of the TS SS ADC. In general, this makes it difficult to implement the TS SS ADC beyond a 10-bit resolution. In order to improve such errors, a new 4-input comparator is discussed and a high resolution TS SS ADC is proposed. Further, a feedback circuit that enables column self-calibration to reduce the Fixed Pattern Noise (FPN) is also described. The proposed chip has been fabricated with 0.13 μm Samsung CIS technology and the chip satisfies the VGA resolution. The pixel is based on the 4-TR Active Pixel Sensor (APS). The high frame rate of 120 fps is achieved at the VGA resolution. The measured FPN is 0.38 LSB, and measured dynamic range is about 64.6 dB. PMID:24999716
Multiplicative noise removal using variable splitting and constrained optimization.
Bioucas-Dias, José M; Figueiredo, Mário A T
2010-07-01
Multiplicative noise (also known as speckle noise) models are central to the study of coherent imaging systems, such as synthetic aperture radar and sonar, and ultrasound and laser imaging. These models introduce two additional layers of difficulties with respect to the standard Gaussian additive noise scenario: (1) the noise is multiplied by (rather than added to) the original image; (2) the noise is not Gaussian, with Rayleigh and Gamma being commonly used densities. These two features of multiplicative noise models preclude the direct application of most state-of-the-art algorithms, which are designed for solving unconstrained optimization problems where the objective has two terms: a quadratic data term (log-likelihood), reflecting the additive and Gaussian nature of the noise, plus a convex (possibly nonsmooth) regularizer (e.g., a total variation or wavelet-based regularizer/prior). In this paper, we address these difficulties by: (1) converting the multiplicative model into an additive one by taking logarithms, as proposed by some other authors; (2) using variable splitting to obtain an equivalent constrained problem; and (3) dealing with this optimization problem using the augmented Lagrangian framework. A set of experiments shows that the proposed method, which we name MIDAL (multiplicative image denoising by augmented Lagrangian), yields state-of-the-art results both in terms of speed and denoising performance.
A Comparison of IIR and Wavelet Filtering for Noise Reduction of the ECG
Sørensen, JS; Johannesen, L; Grove, USL; Lundhus, K; Couderc, J-P; Graff, C
2011-01-01
This study compares the ability to preserve information and reduce noise contaminants on the ECG for five wavelet filters and three IIR filters. Two 3-lead Holter ECGs were used. White Gaussian Noise was added to the first ECG in increments of 10% coverage. The second ECG contained alternating muscle transients and noise-free segments. Computation times and SNR improvements for different noise coverages were calculated and compared. RMS errors were calculated from noise-free segments on the ECG with transient muscle noise. Wavelet filters improved SNR more than IIR filters when the signal coverage was more than 50% noise. In contrast, the computation times were shorter for IIR filters (6 s) than for wavelet filters (88 s). On the ECG with transient muscle noise there was a trade-off in performance between wavelet and IIR filtering. In a clinical setting where the amount of noise is unknown, using IIR filters appears to be preferred for consistent performance. PMID:22068831
Conditional and unconditional Gaussian quantum dynamics
NASA Astrophysics Data System (ADS)
Genoni, Marco G.; Lami, Ludovico; Serafini, Alessio
2016-07-01
This article focuses on the general theory of open quantum systems in the Gaussian regime and explores a number of diverse ramifications and consequences of the theory. We shall first introduce the Gaussian framework in its full generality, including a classification of Gaussian (also known as 'general-dyne') quantum measurements. In doing so, we will give a compact proof for the parametrisation of the most general Gaussian completely positive map, which we believe to be missing in the existing literature. We will then move on to consider the linear coupling with a white noise bath, and derive the diffusion equations that describe the evolution of Gaussian states under such circumstances. Starting from these equations, we outline a constructive method to derive general master equations that apply outside the Gaussian regime. Next, we include the general-dyne monitoring of the environmental degrees of freedom and recover the Riccati equation for the conditional evolution of Gaussian states. Our derivation relies exclusively on the standard quantum mechanical update of the system state, through the evaluation of Gaussian overlaps. The parametrisation of the conditional dynamics we obtain is novel and, at variance with existing alternatives, directly ties in to physical detection schemes. We conclude our study with two examples of conditional dynamics that can be dealt with conveniently through our formalism, demonstrating how monitoring can suppress the noise in optical parametric processes as well as stabilise systems subject to diffusive scattering.
Optimal application of Morrison's iterative noise removal for deconvolution. Appendices
NASA Technical Reports Server (NTRS)
Ioup, George E.; Ioup, Juliette W.
1987-01-01
Morrison's iterative method of noise removal, or Morrison's smoothing, is applied in a simulation to noise-added data sets of various noise levels to determine its optimum use. Morrison's smoothing is applied for noise removal alone, and for noise removal prior to deconvolution. For the latter, an accurate method is analyzed to provide confidence in the optimization. The method consists of convolving the data with an inverse filter calculated by taking the inverse discrete Fourier transform of the reciprocal of the transform of the response of the system. Various length filters are calculated for the narrow and wide Gaussian response functions used. Deconvolution of non-noisy data is performed, and the error in each deconvolution calculated. Plots are produced of error versus filter length; and from these plots the most accurate length filters determined. The statistical methodologies employed in the optimizations of Morrison's method are similar. A typical peak-type input is selected and convolved with the two response functions to produce the data sets to be analyzed. Both constant and ordinate-dependent Gaussian distributed noise is added to the data, where the noise levels of the data are characterized by their signal-to-noise ratios. The error measures employed in the optimizations are the L1 and L2 norms. Results of the optimizations for both Gaussians, both noise types, and both norms include figures of optimum iteration number and error improvement versus signal-to-noise ratio, and tables of results. The statistical variation of all quantities considered is also given.
NASA Astrophysics Data System (ADS)
Baker, C. R.; Chao, I. F.
1990-10-01
The additive infinite-dimensional Gaussian channel subject to jamming is modeled as a two-person zero-sum game with mutual information as the payoff function. The jammer's noise is added to the ambient Gaussian noise. The coder's signal energy is subject to a constraint is necessary in order that the capacity without feedback be finite. It is shown that use of this same RKHS constraint on the jammer's process is too strong; the jammer would then not be able to reduce capacity, regardless of the amount of jamming energy available. The constraint on the jammer is thus on the total jamming energy, without regard to its distribution relative to that of the ambient noise energy. The existence of a saddle value for the problem does not follow from the von Neuman minimax theorem in the original problem formulation. However, a solution is shown to exist. A saddle point, saddle value, and the jammer's minimax strategy are determined. The solution is a function of the problem parameters: the constraint on the coder, the constraint on the jammer, and the covariance of the ambient Gaussian noise.
Gaussian entanglement distribution via satellite
NASA Astrophysics Data System (ADS)
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.
Evaluation of a novel method of noise reduction using computer-simulated mammograms.
Tischenko, Oleg; Hoeschen, Christoph; Dance, David R; Hunt, Roger A; Maidment, Andrew D A; Bakic, Predrag R
2005-01-01
A novel method of noise reduction has been tested for mammography using computer-simulated images for which the truth is known exactly. This method is based on comparing two images. The images are compared at different scales, using a cross-correlation function as a measure of similarity to define the image modifications in the wavelet domain. The computer-simulated images were calculated for noise-free primary radiation using a quasi-realistic voxel phantom. Two images corresponding to slightly different geometry were produced. Gaussian noise was added with certain properties to simulate quantum noise. The added noise could be reduced by >70% using the proposed method without any noticeable corruption of the structures. It is possible to save 50% dose in mammography by producing two images (each 25% of the dose for a standard mammogram). Additionally, a reduction of the anatomical noise and, therefore, better detection rates of breast cancer in mammography are possible.
REJUVENATING POWER SPECTRA. II. THE GAUSSIANIZED GALAXY DENSITY FIELD
Neyrinck, Mark C.; Szalay, Alexander S.; Szapudi, Istvan
2011-04-20
We find that, even in the presence of discreteness noise, a Gaussianizing transform (producing a more Gaussian one-point distribution) reduces nonlinearities in the power spectra of cosmological matter and galaxy density fields, in many cases drastically. Although Gaussianization does increase the effective shot noise, it also increases the power spectrum's fidelity to the linear power spectrum on scales where the shot noise is negligible. Gaussianizing also increases the Fisher information in the power spectrum in all cases and resolutions, although the gains are smaller in redshift space than in real space. We also find that the gain in cumulative Fisher information from Gaussianizing peaks at a particular grid resolution depends on the sampling level.
Gaussian entanglement of formation
Wolf, M.M.; Giedke, G.; Krueger, O.; Werner, R. F.; Cirac, J.I.
2004-05-01
We introduce a Gaussian version of the entanglement of formation adapted to bipartite Gaussian states by considering decompositions into pure Gaussian states only. We show that this quantity is an entanglement monotone under Gaussian operations and provide a simplified computation for states of arbitrary many modes. For the case of one mode per site the remaining variational problem can be solved analytically. If the considered state is in addition symmetric with respect to interchanging the two modes, we prove additivity of the considered entanglement measure. Moreover, in this case and considering only a single copy, our entanglement measure coincides with the true entanglement of formation.
Reduction of internal noise in auditory perceptual learning.
Jones, Pete R; Moore, David R; Amitay, Sygal; Shub, Daniel E
2013-02-01
This paper examines what mechanisms underlie auditory perceptual learning. Fifteen normal hearing adults performed two-alternative, forced choice, pure tone frequency discrimination for four sessions. External variability was introduced by adding a zero-mean Gaussian random variable to the frequency of each tone. Measures of internal noise, encoding efficiency, bias, and inattentiveness were derived using four methods (model fit, classification boundary, psychometric function, and double-pass consistency). The four methods gave convergent estimates of internal noise, which was found to decrease from 4.52 to 2.93 Hz with practice. No group-mean changes in encoding efficiency, bias, or inattentiveness were observed. It is concluded that learned improvements in frequency discrimination primarily reflect a reduction in internal noise. Data from highly experienced listeners and neural networks performing the same task are also reported. These results also indicated that auditory learning represents internal noise reduction, potentially through the re-weighting of frequency-specific channels.
Schäfer, Joachim; Karpov, Evgueni; Cerf, Nicolas J.
2014-12-04
We seek for a realistic implementation of multimode Gaussian entangled states that can realize the optimal encoding for quantum bosonic Gaussian channels with memory. For a Gaussian channel with classical additive Markovian correlated noise and a lossy channel with non-Markovian correlated noise, we demonstrate the usefulness using Gaussian matrix-product states (GMPS). These states can be generated sequentially, and may, in principle, approximate well any Gaussian state. We show that we can achieve up to 99.9% of the classical Gaussian capacity with GMPS requiring squeezing parameters that are reachable with current technology. This may offer a way towards an experimental realization.
Noise Elimination Study for a Single Station Magnetotelluric Data
NASA Astrophysics Data System (ADS)
Şengül, Ebru; Uǧur Ulugergerli, Emin; Göktaş, Hilal
2010-05-01
Five components of the natural electromagnetic field relating to underground conductivity distribution on Earth are measured as a time series in the Magnetotelluric (MT) method. E (Ex, Ey) and H (Hx, Hy, Hz) components of the electromagnetic field suffers from noise contamination. The noise, in general, can be classified as random and systematic noise. Random noise disrupts the pattern of data such as sudden signal peaks and/or step structures called impulsive effect. This type of noise usually is dominant in some parts of the time series. The sources of random noise vary; some of the sources are instrumental problems and atmospheric events. On the other hand, systematic noise occurs at certain frequencies and is added to the data. Industrial activities cause such type of the noise and can corrupt all the data set. The estimation of the impedance tensor from single-station MT data is subject to this study. The proposed method uses statistical approaches focused on the noise elimination techniques. Noise elimination from MT time series is very important particularly to achieve repeatable impedance values using single station MT data. The conventional impedance estimation technique requires solution of a linear equation system (E = ZH) based on Gaussian statistical model which requires the noise of electric channels should obey Gaussian distribution and magnetic channels should be noise free. In fact, measured data never provides this ideal condition. Therefore, noise elimination techniques are very important step in data processing works in MT method. Random noise such as spikes makes deviations in impedance values, resistivity and phase curves. Random noise should be eliminated to correct of these deviations in the data. For this purpose firstly, all data are divided into time windows. Each window consists of 512 values. After that, spikes are removed and missing data are regenerated by using interpolation technique for each window in time domain. Then, data are
Optimal estimation of non-Gaussianity
Babich, Daniel
2005-08-15
We systematically analyze the primordial non-Gaussianity estimator used by the Wilkinson Microwave Anisotropy Probe (WMAP) science team with the basic ideas of estimation theory in order to see if the limited cosmic microwave background (CMB) data is being optimally utilized. The WMAP estimator is based on the implicit assumption that the CMB bispectrum, the harmonic transform of the three-point correlation function, contains all of the primordial non-Gaussianity information in a CMB map. We first demonstrate that the signal-to-noise (S/N) of an estimator based on CMB three-point correlation functions is significantly larger than the S/N of any estimator based on higher-order correlation functions; justifying our choice to focus on the three-point correlation function. We then conclude that the estimator based on the three-point correlation function, which was used by WMAP, is optimal, meaning it saturates the Cramer-Rao inequality when the underlying CMB map is nearly Gaussian. We quantify this restriction by demonstrating that the suboptimal character of our estimator is proportional to the square of the fiducial non-Gaussianity, which is already constrained to be extremely small, so we can consider the WMAP estimator to be optimal in practice. Our conclusions do not depend on the form of the primordial bispectrum, only on the observationally established weak levels of primordial non-Gaussianity.
Enhanced corticomuscular coherence by external stochastic noise
Trenado, Carlos; Mendez-Balbuena, Ignacio; Manjarrez, Elias; Huethe, Frank; Schulte-Mönting, Jürgen; Feige, Bernd; Hepp-Reymond, Marie-Claude; Kristeva, Rumyana
2014-01-01
Noise can have beneficial effects as shown by the stochastic resonance (SR) phenomenon which is characterized by performance improvement when an optimal noise is added. Modern attempts to improve human performance utilize this phenomenon. The purpose of the present study was to investigate whether performance improvement by addition of optimum noise (ON) is related to increased cortical motor spectral power (SP) and increased corticomuscular coherence. Eight subjects performed a visuomotor task requiring to compensate with the right index finger a static force (SF) generated by a manipulandum on which Gaussian noise was applied. The finger position was displayed on-line on a monitor as a small white dot which the subjects had to maintain in the center of a green bigger circle. Electroencephalogram from the contralateral motor area, electromyogram from active muscles and finger position were recorded. The performance was measured by the mean absolute deviation (MAD) of the white dot from the zero position. ON compared to the zero noise condition induced an improvement in motor accuracy together with an enhancement of cortical motor SP and corticomuscular coherence in beta-range. These data suggest that the improved sensorimotor performance via SR is consistent with an increase in the cortical motor SP and in the corticomuscular coherence. PMID:24904365
Enhanced corticomuscular coherence by external stochastic noise.
Trenado, Carlos; Mendez-Balbuena, Ignacio; Manjarrez, Elias; Huethe, Frank; Schulte-Mönting, Jürgen; Feige, Bernd; Hepp-Reymond, Marie-Claude; Kristeva, Rumyana
2014-01-01
Noise can have beneficial effects as shown by the stochastic resonance (SR) phenomenon which is characterized by performance improvement when an optimal noise is added. Modern attempts to improve human performance utilize this phenomenon. The purpose of the present study was to investigate whether performance improvement by addition of optimum noise (ON) is related to increased cortical motor spectral power (SP) and increased corticomuscular coherence. Eight subjects performed a visuomotor task requiring to compensate with the right index finger a static force (SF) generated by a manipulandum on which Gaussian noise was applied. The finger position was displayed on-line on a monitor as a small white dot which the subjects had to maintain in the center of a green bigger circle. Electroencephalogram from the contralateral motor area, electromyogram from active muscles and finger position were recorded. The performance was measured by the mean absolute deviation (MAD) of the white dot from the zero position. ON compared to the zero noise condition induced an improvement in motor accuracy together with an enhancement of cortical motor SP and corticomuscular coherence in beta-range. These data suggest that the improved sensorimotor performance via SR is consistent with an increase in the cortical motor SP and in the corticomuscular coherence.
NASA Astrophysics Data System (ADS)
Koyama, Kazuya; Pettinari, Guido Walter; Mizuno, Shuntaro; Fidler, Christian
2014-06-01
In this paper, we study cosmic microwave background (CMB) constraints on primordial non-Gaussianity in Dirac-Born-Infeld (DBI) galileon models in which an induced gravity term is added to the DBI action. In this model, the non-Gaussianity of orthogonal shape can be generated. We provide a relation between theoretical parameters and orthogonal/equilateral nonlinear parameters using the Fisher matrix approach for the CMB bispectrum. In doing so, we include the effect of the CMB transfer functions and experimental noise properties by employing the recently developed second order non-Gaussianity code. The relation is also shown in the language of effective theory so that it can be applied to general single-field models. Using the bispectrum Fisher matrix and the central values for equilateral and orthogonal non-Gaussianities found by the Planck temperature survey, we provide forecasts on the theoretical parameters of the DBI galileon model. We consider the upcoming Planck polarization data and the proposed post-Planck experiments Cosmic Origins Explore (COrE) and Polarized Radiation Imaging and Spectroscopy Mission (PRISM). We find that Planck polarization measurements may provide a hint for a non-canonical sound speed at the 68% confidence level. COrE and PRISM will not only confirm a non-canonical sound speed but also exclude the conventional DBI inflation model at more than the 95% and 99% confidence level respectively, assuming that the central values will not change. This indicates that improving constraints on non-Gaussianity further by future CMB experiments is invaluable to constrain the physics of the early universe.
A note on the wideband Gaussian broadcast channel
NASA Technical Reports Server (NTRS)
Mceliece, R. J.; Posner, E. C.; Swanson, L.
1986-01-01
It is well known that for the Gaussian broadcast channel, timeshared coding is not as efficient as more sophisticated broadcast coding strategies. However, the relative advantage of broadcast coding over timeshared coding is shown to be small if the signal-to-noise ratios of both receivers are small. One surprising consequence of this is that for the wideband Gaussian broadcast channel, which is defined, broadcast coding offers no advantage over timeshared coding at all, and so timeshared coding is optimal.
Byrnes, Christian T.; Nurmi, Sami; Tasinato, Gianmassimo; Wands, David E-mail: s.nurmi@thphys.uni-heidelberg.de E-mail: david.wands@port.ac.uk
2012-03-01
We propose a method to probe higher-order correlators of the primordial density field through the inhomogeneity of local non-Gaussian parameters, such as f{sub NL}, measured within smaller patches of the sky. Correlators between n-point functions measured in one patch of the sky and k-point functions measured in another patch depend upon the (n+k)-point functions over the entire sky. The inhomogeneity of non-Gaussian parameters may be a feasible way to detect or constrain higher- order correlators in local models of non-Gaussianity, as well as to distinguish between single and multiple-source scenarios for generating the primordial density perturbation, and more generally to probe the details of inflationary physics.
Quantum steering of Gaussian states via non-Gaussian measurements
NASA Astrophysics Data System (ADS)
Ji, Se-Wan; Lee, Jaehak; Park, Jiyong; Nha, Hyunchul
2016-07-01
Quantum steering—a strong correlation to be verified even when one party or its measuring device is fully untrusted—not only provides a profound insight into quantum physics but also offers a crucial basis for practical applications. For continuous-variable (CV) systems, Gaussian states among others have been extensively studied, however, mostly confined to Gaussian measurements. While the fulfilment of Gaussian criterion is sufficient to detect CV steering, whether it is also necessary for Gaussian states is a question of fundamental importance in many contexts. This critically questions the validity of characterizations established only under Gaussian measurements like the quantification of steering and the monogamy relations. Here, we introduce a formalism based on local uncertainty relations of non-Gaussian measurements, which is shown to manifest quantum steering of some Gaussian states that Gaussian criterion fails to detect. To this aim, we look into Gaussian states of practical relevance, i.e. two-mode squeezed states under a lossy and an amplifying Gaussian channel. Our finding significantly modifies the characteristics of Gaussian-state steering so far established such as monogamy relations and one-way steering under Gaussian measurements, thus opening a new direction for critical studies beyond Gaussian regime.
Quantum steering of Gaussian states via non-Gaussian measurements
Ji, Se-Wan; Lee, Jaehak; Park, Jiyong; Nha, Hyunchul
2016-01-01
Quantum steering—a strong correlation to be verified even when one party or its measuring device is fully untrusted—not only provides a profound insight into quantum physics but also offers a crucial basis for practical applications. For continuous-variable (CV) systems, Gaussian states among others have been extensively studied, however, mostly confined to Gaussian measurements. While the fulfilment of Gaussian criterion is sufficient to detect CV steering, whether it is also necessary for Gaussian states is a question of fundamental importance in many contexts. This critically questions the validity of characterizations established only under Gaussian measurements like the quantification of steering and the monogamy relations. Here, we introduce a formalism based on local uncertainty relations of non-Gaussian measurements, which is shown to manifest quantum steering of some Gaussian states that Gaussian criterion fails to detect. To this aim, we look into Gaussian states of practical relevance, i.e. two-mode squeezed states under a lossy and an amplifying Gaussian channel. Our finding significantly modifies the characteristics of Gaussian-state steering so far established such as monogamy relations and one-way steering under Gaussian measurements, thus opening a new direction for critical studies beyond Gaussian regime. PMID:27411853
Non-Gaussian quantum states generation and robust quantum non-Gaussianity via squeezing field
NASA Astrophysics Data System (ADS)
Tang, Xu-Bing; Gao, Fang; Wang, Yao-Xiong; Kuang, Sen; Shuang, Feng
2015-03-01
Recent studies show that quantum non-Gaussian states or using non-Gaussian operations can improve entanglement distillation, quantum swapping, teleportation, and cloning. In this work, employing a strategy of non-Gaussian operations (namely subtracting and adding a single photon), we propose a scheme to generate non-Gaussian quantum states named single-photon-added and -subtracted coherent (SPASC) superposition states by implementing Bell measurements, and then investigate the corresponding nonclassical features. By squeezed the input field, we demonstrate that robustness of non-Gaussianity can be improved. Controllable phase space distribution offers the possibility to approximately generate a displaced coherent superposition states (DCSS). The fidelity can reach up to F ≥ 0.98 and F ≥ 0.90 for size of amplitude z = 1.53 and 2.36, respectively. Project supported by the National Natural Science Foundation of China (Grant Nos. 61203061 and 61074052), the Outstanding Young Talent Foundation of Anhui Province, China (Grant No. 2012SQRL040), and the Natural Science Foundation of Anhui Province, China (Grant No. KJ2012Z035).
Gaussian discriminating strength
NASA Astrophysics Data System (ADS)
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.
NASA Astrophysics Data System (ADS)
Trofimov, M. Yu.; Zakharenko, A. D.; Kozitskiy, S. B.
2016-10-01
A mode parabolic equation in the ray centered coordinates for 3D underwater sound propagation is developed. The Gaussian beam tracing in this case is constructed. The test calculations are carried out for the ASA wedge benchmark and proved an excellent agreement with the source images method in the case of cross-slope propagation. But in the cases of wave propagation at some angles to the cross-slope direction an account of mode interaction becomes necessary.
Flauger, Raphael; Pajer, Enrico E-mail: ep295@cornell.edu
2011-01-01
We provide a derivation from first principles of the primordial bispectrum of scalar perturbations produced during inflation driven by a canonically normalized scalar field whose potential exhibits small sinusoidal modulations. A potential of this type has been derived in a class of string theory models of inflation based on axion monodromy. We use this model as a concrete example, but we present our derivations and results for a general slow-roll potential with superimposed modulations. We show analytically that a resonance between the oscillations of the background and the oscillations of the fluctuations is responsible for the production of an observably large non-Gaussian signal. We provide an explicit expression for the shape of this resonant non-Gaussianity. We show that there is essentially no overlap between this shape and the local, equilateral, and orthogonal shapes, and we stress that resonant non-Gaussianity is not captured by the simplest version of the effective field theory of inflation. We hope our analytic expression will be useful to further observationally constrain this class of models.
The conditional entropy power inequality for Gaussian quantum states
Koenig, Robert
2015-02-15
We propose a generalization of the quantum entropy power inequality involving conditional entropies. For the special case of Gaussian states, we give a proof based on perturbation theory for symplectic spectra. We discuss some implications for entanglement-assisted classical communication over additive bosonic noise channels.
Distributions of Conductance and Shot Noise and Associated Phase Transitions
Vivo, Pierpaolo; Majumdar, Satya N.; Bohigas, Oriol
2008-11-21
For a chaotic cavity with two identical leads each supporting N channels, we compute analytically, for large N, the full distribution of the conductance and the shot noise power and show that in both cases there is a central Gaussian region flanked on both sides by non-Gaussian tails. The distribution is weakly singular at the junction of Gaussian and non-Gaussian regimes, a direct consequence of two phase transitions in an associated Coulomb gas problem.
NASA Astrophysics Data System (ADS)
Chizhevsky, V. N.
2015-09-01
This paper is a report of the experimental evidence of suppression of vibrational higher-order harmonics in a bistable vertical-cavity surface-emitting laser driven by two harmonic signals with very different frequencies in the phenomenon of vibrational resonance when an optimal amount of white, Gaussian noise is applied. A quantitative characterization of the suppression is given on the basis of the coefficient of nonlinear distortions. The behavior of the coefficient of nonlinear distortions is studied in wide ranges of the added noise intensity, the dc current, and the amplitude of the harmonic signals. The experimental results are compared with a numerical simulation of a Langevin model showing good agreement.
NASA Astrophysics Data System (ADS)
Luo, Yusheng; Du, Z. W.; Yang, Y. J.; Chen, P.; Tian, Q.; Shang, X. L.; Liu, Z. C.; Yao, X. Q.; Wang, J. Z.; Wang, X. H.; Cheng, Y.; Peng, J.; Shen, A. G.; Hu, J. M.
2013-04-01
Early and differential diagnosis of Alzheimer’s disease (AD) has puzzled many clinicians. In this work, laser Raman spectroscopy (LRS) was developed to diagnose AD from platelet samples from AD transgenic mice and non-transgenic controls of different ages. An adaptive Gaussian process (GP) classification algorithm was used to re-establish the classification models of early AD, advanced AD and the control group with just two features and the capacity for noise reduction. Compared with the previous multilayer perceptron network method, the GP showed much better classification performance with the same feature set. Besides, spectra of platelets isolated from AD and Parkinson’s disease (PD) mice were also discriminated. Spectral data from 4 month AD (n = 39) and 12 month AD (n = 104) platelets, as well as control data (n = 135), were collected. Prospective application of the algorithm to the data set resulted in a sensitivity of 80%, a specificity of about 100% and a Matthews correlation coefficient of 0.81. Samples from PD (n = 120) platelets were also collected for differentiation from 12 month AD. The results suggest that platelet LRS detection analysis with the GP appears to be an easier and more accurate method than current ones for early and differential diagnosis of AD.
Optical Johnson noise thermometry
NASA Technical Reports Server (NTRS)
Shepard, R. L.; Blalock, T. V.; Maxey, L. C.; Roberts, M. J.; Simpson, M. L.
1989-01-01
A concept is being explored that an optical analog of the electrical Johnson noise may be used to measure temperature independently of emissivity. The concept is that a laser beam may be modulated on reflection from a hot surface by interaction of the laser photons with the thermally agitated conduction electrons or the lattice phonons, thereby adding noise to the reflected laser beam. If the reflectance noise can be detected and quantified in a background of other noise in the optical and signal processing systems, the reflectance noise may provide a noncontact measurement of the absolute surface temperature and may be independent of the surface's emissivity.
Lee, E.P.
1982-11-03
The growth rate of the hose instability is derived for a beam with Gaussian radial profile, using the spread mass model of phase mix damping. It is found that the maximum growth rate of a convecting wave packet is 49% larger than that derived for a beam with the Bennett profile, and the inverse group velocity (dz/d tau) is also increased by about this amount. A general discussion of spread mass models is presented along with an explanation of the regurgitation phenomena seen in their numerical treatment.
Investigating binocular summation in human vision using complementary fused external noise
NASA Astrophysics Data System (ADS)
Howell, Christopher L.; Olson, Jeffrey T.
2016-05-01
The impact noise has on the processing of visual information at various stages within the human visual system (HVS) is still an open research area. To gain additional insight, twelve experiments were administered to human observers using sine wave targets to determine their contrast thresholds. A single frame of additive white Gaussian noise (AWGN) and its complement were used to investigate the effect of noise on the summation of visual information within the HVS. A standard contrast threshold experiment served as the baseline for comparisons. In the standard experiment, a range of sine wave targets are shown to the observers and their ability to detect the targets at varying contrast levels were recorded. The remaining experiments added some form of noise (noise image or its complement) and/or an additional sine wave target separated between one to three octaves to the test target. All of these experiments were tested using either a single monitor for viewing the targets or with a dual monitor presentation method for comparison. In the dual monitor experiments, a ninety degree mirror was used to direct each target to a different eye, allowing for the information to be fused binocularly. The experiments in this study present different approaches for delivering external noise to the HVS, and should allow for an improved understanding regarding how noise enters the HVS and what impact noise has on the processing of visual information.
Effects of spatial resolution and noise on gamma analysis for IMRT QA
Huang, Jessie Y.; Pulliam, Kiley B.; McKenzie, Elizabeth M.; Followill, David S.; Kry, Stephen F.
2014-01-01
We investigated the sensitivity of the gamma index to two factors: the spatial resolution and the noise level in the measured dose distribution. We also examined how the choice of reference distribution and analysis software affect the sensitivity of gamma analysis to these two factors for quality assurance (QA) of intensity-modulated radiation therapy (IMRT) treatment plans. For ten clinical IMRT plans, the dose delivered to a transverse dose plane was measured with EDR2 radiographic film. To evaluate the effects of spatial resolution, each irradiated film was digitized using three different resolutions (71, 142, and 285 dpi). To evaluate the effects of image noise, 1% and 2% local Gaussian noise was added to the film images. Gamma analysis was performed using 2%/2 mm and 3%/3 mm acceptance criteria and two commercial software packages, OmniPro I’mRT and DoseLab Pro. Dose comparisons were performed with the treatment planning system (TPS)-calculated dose as the reference, and then repeated with the film as the reference to evaluate how the choice of reference distribution affects the results of dose comparisons. When the TPS-calculated dose was designated as the reference distribution, the percentage of pixels with passing gamma values increased with both increasing resolution and noise. For 3%/3 mm acceptance criteria, increasing the film image resolution by a factor of two and by a factor of four caused a median increase of 0.9% and 2.6%, respectively, in the percentage of pixels passing. Increasing the noise level in the film image resulted in a median increase in percentage of pixels passing of 5.5% for 1% added local Gaussian noise and 5.8% for 2% added noise. In contrast, when the film was designated as the reference distribution, the percentage of pixels passing decreased with increased film noise, while increased resolution had no significant effect on passing rates. Furthermore, the sensitivity of gamma analysis to noise and resolution differed between
Moving target detection algorithm based on Gaussian mixture model
NASA Astrophysics Data System (ADS)
Wang, Zhihua; Kai, Du; Zhang, Xiandong
2013-07-01
In real-time video surveillance system, background noise and disturbance for the detection of moving objects will have a significant impact. The traditional Gaussian mixture model;GMM&;has strong adaptive various complex background ability, but slow convergence speed and vulnerable to illumination change influence. the paper proposes an improved moving target detection algorithm based on Gaussian mixture model which increase the convergence rate of foreground to the background model transformation and introducing the concept of the changing factors, through the three frame differential method solved light mutation problem. The results show that this algorithm can improve the accuracy of the moving object detection, and has good stability and real-time.
Gaussian matrix-product states for coding in bosonic communication channels
NASA Astrophysics Data System (ADS)
Schäfer, Joachim; Karpov, Evgueni; Cerf, Nicolas J.
2012-01-01
The communication capacity of Gaussian bosonic channels with memory has recently attracted much interest. Here, we investigate a method to prepare the multimode entangled input symbol states for encoding classical information into these channels. In particular, we study the usefulness of a Gaussian matrix-product state (GMPS) as an input symbol state, which can be sequentially generated although it remains heavily entangled for an arbitrary number of modes. We show that the GMPS can achieve more than 99.9% of the Gaussian capacity for Gaussian bosonic memory channels with a Markovian or non-Markovian correlated noise model in a large range of noise correlation strengths. Furthermore, we present a noise class for which the GMPS is the exact optimal input symbol state of the corresponding channel. Since GMPS are ground states of particular quadratic Hamiltonians, our results suggest a possible link between the theory of quantum communication channels and quantum many-body physics.
NASA Astrophysics Data System (ADS)
Rossi, Matteo A. C.; Paris, Matteo G. A.
2016-01-01
We address the interaction of single- and two-qubit systems with an external transverse fluctuating field and analyze in detail the dynamical decoherence induced by Gaussian noise and random telegraph noise (RTN). Upon exploiting the exact RTN solution of the time-dependent von Neumann equation, we analyze in detail the behavior of quantum correlations and prove the non-Markovianity of the dynamical map in the full parameter range, i.e., for either fast or slow noise. The dynamics induced by Gaussian noise is studied numerically and compared to the RTN solution, showing the existence of (state dependent) regions of the parameter space where the two noises lead to very similar dynamics. We show that the effects of RTN noise and of Gaussian noise are different, i.e., the spectrum alone is not enough to summarize the noise effects, but the dynamics under the effect of one kind of noise may be simulated with high fidelity by the other one.
Rossi, Matteo A C; Paris, Matteo G A
2016-01-14
We address the interaction of single- and two-qubit systems with an external transverse fluctuating field and analyze in detail the dynamical decoherence induced by Gaussian noise and random telegraph noise (RTN). Upon exploiting the exact RTN solution of the time-dependent von Neumann equation, we analyze in detail the behavior of quantum correlations and prove the non-Markovianity of the dynamical map in the full parameter range, i.e., for either fast or slow noise. The dynamics induced by Gaussian noise is studied numerically and compared to the RTN solution, showing the existence of (state dependent) regions of the parameter space where the two noises lead to very similar dynamics. We show that the effects of RTN noise and of Gaussian noise are different, i.e., the spectrum alone is not enough to summarize the noise effects, but the dynamics under the effect of one kind of noise may be simulated with high fidelity by the other one. PMID:26772560
Restoring the encoding properties of a stochastic neuron model by an exogenous noise
Paffi, Alessandra; Camera, Francesca; Apollonio, Francesca; d'Inzeo, Guglielmo; Liberti, Micaela
2015-01-01
Here we evaluate the possibility of improving the encoding properties of an impaired neuronal system by superimposing an exogenous noise to an external electric stimulation signal. The approach is based on the use of mathematical neuron models consisting of stochastic HH-like circuit, where the impairment of the endogenous presynaptic inputs is described as a subthreshold injected current and the exogenous stimulation signal is a sinusoidal voltage perturbation across the membrane. Our results indicate that a correlated Gaussian noise, added to the sinusoidal signal can significantly increase the encoding properties of the impaired system, through the Stochastic Resonance (SR) phenomenon. These results suggest that an exogenous noise, suitably tailored, could improve the efficacy of those stimulation techniques used in neuronal systems, where the presynaptic sensory neurons are impaired and have to be artificially bypassed. PMID:25999845
NASA Astrophysics Data System (ADS)
Anabalón, Andrés; Astefanesei, Dumitru; Choque, David
2016-11-01
We construct exact hairy AdS soliton solutions in Einstein-dilaton gravity theory. We examine their thermodynamic properties and discuss the role of these solutions for the existence of first order phase transitions for hairy black holes. The negative energy density associated to hairy AdS solitons can be interpreted as the Casimir energy that is generated in the dual filed theory when the fermions are antiperiodic on the compact coordinate.
Normal form decomposition for Gaussian-to-Gaussian superoperators
De Palma, Giacomo; Mari, Andrea; Giovannetti, Vittorio; Holevo, Alexander S.
2015-05-15
In this paper, we explore the set of linear maps sending the set of quantum Gaussian states into itself. These maps are in general not positive, a feature which can be exploited as a test to check whether a given quantum state belongs to the convex hull of Gaussian states (if one of the considered maps sends it into a non-positive operator, the above state is certified not to belong to the set). Generalizing a result known to be valid under the assumption of complete positivity, we provide a characterization of these Gaussian-to-Gaussian (not necessarily positive) superoperators in terms of their action on the characteristic function of the inputs. For the special case of one-mode mappings, we also show that any Gaussian-to-Gaussian superoperator can be expressed as a concatenation of a phase-space dilatation, followed by the action of a completely positive Gaussian channel, possibly composed with a transposition. While a similar decomposition is shown to fail in the multi-mode scenario, we prove that it still holds at least under the further hypothesis of homogeneous action on the covariance matrix.
NASA Technical Reports Server (NTRS)
Vazirani, P.
1995-01-01
The process of combining telemetry signals received at multiple antennas, commonly referred to as arraying, can be used to improve communication link performance in the Deep Space Network (DSN). By coherently adding telemetry from multiple receiving sites, arraying produces an enhancement in signal-to-noise ratio (SNR) over that achievable with any single antenna in the array. A number of different techniques for arraying have been proposed and their performances analyzed in past literature. These analyses have compared different arraying schemes under the assumption that the signals contain additive white Gaussian noise (AWGN) and that the noise observed at distinct antennas is independent. In situations where an unwanted background body is visible to multiple antennas in the array, however, the assumption of independent noises is no longer applicable. A planet with significant radiation emissions in the frequency band of interest can be one such source of correlated noise. For example, during much of Galileo's tour of Jupiter, the planet will contribute significantly to the total system noise at various ground stations. This article analyzes the effects of correlated noise on two arraying schemes currently being considered for DSN applications: full-spectrum combining (FSC) and complex-symbol combining (CSC). A framework is presented for characterizing the correlated noise based on physical parameters, and the impact of the noise correlation on the array performance is assessed for each scheme.
Efficient entanglement criteria beyond Gaussian limits using Gaussian measurements.
Nha, Hyunchul; Lee, Su-Yong; Ji, Se-Wan; Kim, M S
2012-01-20
We present a formalism to derive entanglement criteria beyond the Gaussian regime that can be readily tested by only homodyne detection. The measured observable is the Einstein-Podolsky-Rosen (EPR) correlation. Its arbitrary functional form enables us to detect non-Gaussian entanglement even when an entanglement test based on second-order moments fails. We illustrate the power of our experimentally friendly criteria for a broad class of non-Gaussian states under realistic conditions. We also show rigorously that quantum teleportation for continuous variables employs a specific functional form of EPR correlation. PMID:22400723
Recurrence plots of discrete-time Gaussian stochastic processes
NASA Astrophysics Data System (ADS)
Ramdani, Sofiane; Bouchara, Frédéric; Lagarde, Julien; Lesne, Annick
2016-09-01
We investigate the statistical properties of recurrence plots (RPs) of data generated by discrete-time stationary Gaussian random processes. We analytically derive the theoretical values of the probabilities of occurrence of recurrence points and consecutive recurrence points forming diagonals in the RP, with an embedding dimension equal to 1. These results allow us to obtain theoretical values of three measures: (i) the recurrence rate (REC) (ii) the percent determinism (DET) and (iii) RP-based estimation of the ε-entropy κ(ε) in the sense of correlation entropy. We apply these results to two Gaussian processes, namely first order autoregressive processes and fractional Gaussian noise. For these processes, we simulate a number of realizations and compare the RP-based estimations of the three selected measures to their theoretical values. These comparisons provide useful information on the quality of the estimations, such as the minimum required data length and threshold radius used to construct the RP.
Constraining primordial non-Gaussianity with cosmological weak lensing: shear and flexion
Fedeli, C.; Bartelmann, M.; Moscardini, L. E-mail: bartelmann@uni-heidelberg.de
2012-10-01
being still subdominant, improves the shear constraints by ∼ 10% when added. However on such small scales the highly non-linear clustering of matter, the impact of baryonic physics, and the non-Gaussian part of the covariance matrix make any error estimation uncertain. By considering lower, and possibly more realistic, values of the flexion intrinsic shape noise results in flexion constraining power being a factor of ∼ 2 better than that of shear, and the bounds on σ{sub 8} and f{sub NL} being improved by a factor of ∼ 3 upon their combination.
Detection methods for non-Gaussian gravitational wave stochastic backgrounds
NASA Astrophysics Data System (ADS)
Drasco, Steve; Flanagan, Éanna É.
2003-04-01
A gravitational wave stochastic background can be produced by a collection of independent gravitational wave events. There are two classes of such backgrounds, one for which the ratio of the average time between events to the average duration of an event is small (i.e., many events are on at once), and one for which the ratio is large. In the first case the signal is continuous, sounds something like a constant hiss, and has a Gaussian probability distribution. In the second case, the discontinuous or intermittent signal sounds something like popcorn popping, and is described by a non-Gaussian probability distribution. In this paper we address the issue of finding an optimal detection method for such a non-Gaussian background. As a first step, we examine the idealized situation in which the event durations are short compared to the detector sampling time, so that the time structure of the events cannot be resolved, and we assume white, Gaussian noise in two collocated, aligned detectors. For this situation we derive an appropriate version of the maximum likelihood detection statistic. We compare the performance of this statistic to that of the standard cross-correlation statistic both analytically and with Monte Carlo simulations. In general the maximum likelihood statistic performs better than the cross-correlation statistic when the stochastic background is sufficiently non-Gaussian, resulting in a gain factor in the minimum gravitational-wave energy density necessary for detection. This gain factor ranges roughly between 1 and 3, depending on the duty cycle of the background, for realistic observing times and signal strengths for both ground and space based detectors. The computational cost of the statistic, although significantly greater than that of the cross-correlation statistic, is not unreasonable. Before the statistic can be used in practice with real detector data, further work is required to generalize our analysis to accommodate separated, misaligned
Non-Gaussian eccentricity fluctuations
NASA Astrophysics Data System (ADS)
Grönqvist, Hanna; Blaizot, Jean-Paul; Ollitrault, Jean-Yves
2016-09-01
We study the fluctuations of the anisotropy of the energy density profile created in a high-energy collision at the LHC. We show that the anisotropy in harmonic n has generic non-Gaussian fluctuations. We argue that these non-Gaussianities have a universal character for small systems such as p+Pb collisions, but not for large systems such as Pb+Pb collisions where they depend on the underlying non-Gaussian statistics of the initial density profile. We generalize expressions for the eccentricity cumulants ɛ2{4 } and ɛ3{4 } previously obtained within the independent-source model to a general fluctuating initial density profile.
ERIC Educational Resources Information Center
UCLA IDEA, 2012
2012-01-01
Value added measures (VAM) uses changes in student test scores to determine how much "value" an individual teacher has "added" to student growth during the school year. Some policymakers, school districts, and educational advocates have applauded VAM as a straightforward measure of teacher effectiveness: the better a teacher, the better students…
Gaussian-Based Hue Descriptors.
Mirzaei, Hamidreza; Funt, Brian
2015-12-01
A robust and accurate hue descriptor that is useful in modeling human color perception and for computer vision applications is explored. The hue descriptor is based on the peak wavelength of a Gaussian-like function (called a wraparound Gaussian) and is shown to correlate as well as CIECAM02 hue to the hue designators of papers from the Munsell and Natural Color System color atlases and to the hue names found in Moroney's Color Thesaurus. The new hue descriptor is also shown to be significantly more stable under a variety of illuminants than CIECAM02. The use of wraparound Gaussians as a hue model is similar in spirit to the use of subtractive Gaussians proposed by Mizokami et al., but overcomes many of their limitations. PMID:26539849
Passive interferometric symmetries of multimode Gaussian pure states
NASA Astrophysics Data System (ADS)
Gabay, Natasha; Menicucci, Nicolas C.
2016-05-01
As large-scale multimode Gaussian states begin to become accessible in the laboratory, their representation and analysis become a useful topic of research in their own right. The graphical calculus for Gaussian pure states provides powerful tools for their representation, while this work presents a useful tool for their analysis: passive interferometric (i.e., number-conserving) symmetries. Here we show that these symmetries of multimode Gaussian states simplify calculations in measurement-based quantum computing and provide constructive tools for engineering large-scale harmonic systems with specific physical properties, and we provide a general mathematical framework for deriving them. Such symmetries are generated by linear combinations of operators expressed in the Schwinger representation of U (2 ) , called nullifiers because the Gaussian state in question is a zero eigenstate of them. This general framework is shown to have applications in the noise analysis of continuous-various cluster states and is expected to have additional applications in future work with large-scale multimode Gaussian states.
Searching for primordial non-Gaussianity in Planck CMB maps using a combined estimator
Novaes, C.P.; Wuensche, C.A.; Bernui, A.; Ferreira, I.S. E-mail: bernui@on.br E-mail: ca.wuensche@inpe.br
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
Brain MRI segmentation and lesion detection using generalized Gaussian and Rician modeling
NASA Astrophysics Data System (ADS)
Wu, Xuqiang; Bricq, Stéphanie; Collet, Christophe
2011-03-01
In this paper we propose a mixed noise modeling so as to segment the brain and to detect lesion. Indeed, accurate segmentation of multimodal (T1, T2 and Flair) brain MR images is of great interest for many brain disorders but requires to efficiently manage multivariate correlated noise between available modalities. We addressed this problem in1 by proposing an entirely unsupervised segmentation scheme, taking into account multivariate Gaussian noise, imaging artifacts,intrinsic tissue variation and partial volume effects in a Bayesian framework. Nevertheless, tissue classification remains a challenging task especially when one addresses the lesion detection during segmentation process2 as we did. In order to improve brain segmentation into White and Gray Matter (resp. WM and GM) and cerebro-spinal fluid (CSF), we propose to fit a Rician (RC) density distribution for CSF whereas Generalized Gaussian (GG) models are used to fit the likelihood between model and data corresponding to WM and GM. In this way, we present in this paper promising results showing that in a multimodal segmentation-detection scheme, this model fits better with the data and increases lesion detection rate. One of the main challenges consists in being able to take into account various pdf (Gaussian and non- Gaussian) for correlated noise between modalities and to show that lesion-detection is then clearly improved, probably because non-Gaussian noise better fits to the physic of MRI image acquisition.
NASA Technical Reports Server (NTRS)
Huston, R. J. (Compiler)
1982-01-01
The establishment of a realistic plan for NASA and the U.S. helicopter industry to develop a design-for-noise methodology, including plans for the identification and development of promising noise reduction technology was discussed. Topics included: noise reduction techniques, scaling laws, empirical noise prediction, psychoacoustics, and methods of developing and validing noise prediction methods.
Energy-independent factors influencing noise-induced hearing loss in the chinchilla model
NASA Astrophysics Data System (ADS)
Hamernik, Roger P.; Qiu, Wei
2001-12-01
The effects on hearing and the sensory cell population of four continuous, non-Gaussian noise exposures each having an A-weighted Leq=100 dB SPL were compared to the effects of an energy-equivalent Gaussian noise. The non-Gaussian noise conditions were characterized by the statistical metric, kurtosis (β), computed on the unfiltered, β(t), and the filtered, β(f ), time-domain signals. The chinchilla (n=58) was used as the animal model. Hearing thresholds were estimated using auditory-evoked potentials (AEP) recorded from the inferior colliculus and sensory cell populations were obtained from surface preparation histology. Despite equivalent exposure energies, the four non-Gaussian conditions produced considerably greater hearing and sensory cell loss than did the Gaussian condition. The magnitude of this excess trauma produced by the non-Gaussian noise was dependent on the frequency content, but not on the average energy content of the impacts which gave the noise its non-Gaussian character. These results indicate that β(t) is an appropriate index of the increased hazard of exposure to non-Gaussian noises and that β(f ) may be useful in the prediction of the place-specific additional outer hair cell loss produced by non-Gaussian exposures. The results also suggest that energy-based metrics, while necessary for the prediction of noise-induced hearing loss, are not sufficient.
Energy-independent factors influencing noise-induced hearing loss in the chinchilla model.
Hamernik, R P; Qiu, W
2001-12-01
The effects on hearing and the sensory cell population of four continuous, non-Gaussian noise exposures each having an A-weighted L(eq)=100 dB SPL were compared to the effects of an energy-equivalent Gaussian noise. The non-Gaussian noise conditions were characterized by the statistical metric, kurtosis (beta), computed on the unfiltered, beta(t), and the filtered, beta(f), time-domain signals. The chinchilla (n=58) was used as the animal model. Hearing thresholds were estimated using auditory-evoked potentials (AEP) recorded from the inferior colliculus and sensory cell populations were obtained from surface preparation histology. Despite equivalent exposure energies, the four non-Gaussian conditions produced considerably greater hearing and sensory cell loss than did the Gaussian condition. The magnitude of this excess trauma produced by the non-Gaussian noise was dependent on the frequency content, but not on the average energy content of the impacts which gave the noise its non-Gaussian character. These results indicate that beta(t) is an appropriate index of the increased hazard of exposure to non-Gaussian noises and that beta(f) may be useful in the prediction of the place-specific additional outer hair cell loss produced by non-Gaussian exposures. The results also suggest that energy-based metrics, while necessary for the prediction of noise-induced hearing loss, are not sufficient. PMID:11785817
Continuous-variable quantum teleportation with non-Gaussian resources
Dell'Anno, F.; De Siena, S.; Albano, L.; Illuminati, F.
2007-08-15
We investigate continuous variable quantum teleportation using non-Gaussian states of the radiation field as entangled resources. We compare the performance of different classes of degaussified resources, including two-mode photon-added and two-mode photon-subtracted squeezed states. We then introduce a class of two-mode squeezed Bell-like states with one-parameter dependence for optimization. These states interpolate between and include as subcases different classes of degaussified resources. We show that optimized squeezed Bell-like resources yield a remarkable improvement in the fidelity of teleportation both for coherent and nonclassical input states. The investigation reveals that the optimal non-Gaussian resources for continuous variable teleportation are those that most closely realize the simultaneous maximization of the content of entanglement, the degree of affinity with the two-mode squeezed vacuum, and the, suitably measured, amount of non-Gaussianity.
Quantum correlations in Gaussian states via Gaussian channels: steering, entanglement, and discord
NASA Astrophysics Data System (ADS)
Wang, Zhong-Xiao; Wang, Shuhao; Li, Qiting; Wang, Tie-Jun; Wang, Chuan
2016-06-01
Here we study the quantum steering, quantum entanglement, and quantum discord for Gaussian Einstein-Podolsky-Rosen states via Gaussian channels. And the sudden death phenomena for Gaussian steering and Gaussian entanglement are theoretically observed. We find that some Gaussian states have only one-way steering, which confirms the asymmetry of quantum steering. Also we investigate that the entangled Gaussian states without Gaussian steering and correlated Gaussian states own no Gaussian entanglement. Meanwhile, our results support the assumption that quantum entanglement is intermediate between quantum discord and quantum steering. Furthermore, we give experimental recipes for preparing quantum states with desired types of quantum correlations.
Symptoms of chaos in observed oscillations near a bifurcation with noise
NASA Astrophysics Data System (ADS)
Harding, Robert H.; Ross, John
1988-10-01
We examine an experimental transition from periodic to aperiodic and back to periodic dynamics in the combustion of acetaldehyde(ACH) in a continuous stirred tank reactor (CSTR) with power spectra, autocorrelation functions, phase portraits, Poincaŕe sections, the Wolf-Swift-Swinney-Vastano (WSSV) method for determining the largest Lyapounov exponent, and the Grassberger-Procaccia (GP) method for determining correlation dimension. Each technique gives some indications of a transition to chaos, but there are discrepancies in that the largest Lyapounov exponent is positive but does not converge and the GP method results in a correlation dimension between one and two for two aperiodic data sets. We explore in instructive detail possible explanations for false indications of chaos by comparing our results with calculations on the Rössler chaotic attractor and the van der Pol periodic attractor modified to examine the effects of uneven point distribution and three types of experimental noise. An uneven distribution of points results in a decreased range of length scales for convergence and a larger required embedding dimension for the GP method, but does not explain our experimental results. Observation noise (a Gaussian noise added to each term in the time series but not entering in the equations of motion) and constraint shift (the motion relaxes to an attractor but a constraint changes monotonically during the course of measurement) added to a periodic attractor both result in a low length scale cutoff below which the attractor dimension does not converge with embedding dimension, and above which it converges to 1. Constraint variation noise (a Gaussian noise is added to each term in the time series and enters into the equations of motion as a stochastic perturbation) does yield correlation dimensions between 1 and 2. The experimental transition shows many similarities to a Hopf bifurcation found in another experiment on the same system and to a theoretical Hopf
Information geometry of Gaussian channels
Monras, Alex; Illuminati, Fabrizio
2010-06-15
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).
Capacity and optimal collusion attack channels for Gaussian fingerprinting games
NASA Astrophysics Data System (ADS)
Wang, Ying; Moulin, Pierre
2007-02-01
constraints. Under those constraints on the fingerprint embedder and the colluders, fingerprinting capacity is obtained as the solution of a mutual-information game involving probability density functions (pdf's) designed by the embedder and the colluders. We show that the optimal fingerprinting strategy is a Gaussian test channel where the fingerprinted signal is the sum of an attenuated version of the cover signal plus a Gaussian information-bearing noise, and the optimal collusion strategy is to average fingerprinted signals possessed by all the colluders and pass the averaged copy through a Gaussian test channel. The capacity result and the optimal strategies are the same for both the private and public games. In the former scenario, the original covertext is available to the decoder, while in the latter setup, the original covertext is available to the encoder but not to the decoder.
Wang, Lu; Xu, Lisheng; Zhao, Dazhe; Yao, Yang; Song, Dan
2015-04-01
Because arterial pulse waves contain vital information related to the condition of the cardiovascular system, considerable attention has been devoted to the study of pulse waves in recent years. Accurate acquisition is essential to investigate arterial pulse waves. However, at the stage of developing equipment for acquiring and analyzing arterial pulse waves, specific pulse signals may be unavailable for debugging and evaluating the system under development. To produce test signals that reflect specific physiological conditions, in this paper, an arterial pulse wave generator has been designed and implemented using a field programmable gate array (FPGA), which can produce the desired pulse waves according to the feature points set by users. To reconstruct a periodic pulse wave from the given feature points, a method known as piecewise Gaussian-cosine fitting is also proposed in this paper. Using a test database that contains four types of typical pulse waves with each type containing 25 pulse wave signals, the maximum residual error of each sampling point of the fitted pulse wave in comparison with the real pulse wave is within 8%. In addition, the function for adding baseline drift and three types of noises is integrated into the developed system because the baseline occasionally wanders, and noise needs to be added for testing the performance of the designed circuits and the analysis algorithms. The proposed arterial pulse wave generator can be considered as a special signal generator with a simple structure, low cost and compact size, which can also provide flexible solutions for many other related research purposes.
Investigation of 1/f Noise and Superimposed RTS Noise in Ti-Au/n-Type GaAs Schottky Barrier Diodes
NASA Astrophysics Data System (ADS)
Klyuev, Alexey V.; Yakimov, Arkady V.
2015-10-01
Low frequency noise characteristics of Schottky diodes are investigated. Two noise components were found in experimental noise records: random telegraph signal (RTS), caused by burst noise, and 1/f Gaussian noise. The noise is sampled and recorded on a PC. Then, in addition to the spectrum, the probability density function (pdf) of the total noise is analyzed. In the case of the mixture of the burst noise and Gaussian (1/f) noise, the pdf has two maxima separated by a local minimum. Extraction of burst noise component from Gaussian noise background was performed using the pdf, standard signal detection theory, and advanced signal-processing techniques. It is concluded that the RTS noise and 1/f noise have different physical origins in Schottky diodes. The raw noise is split into two components. One appeared to be burst noise with a Lorentzian-like spectral shape. The other component is 1/f noise. Having extracted 1/f noise, we have studied the dependence of noise spectral values on the current across the diode.
Optimum threshold selection method of centroid computation for Gaussian spot
NASA Astrophysics Data System (ADS)
Li, Xuxu; Li, Xinyang; Wang, Caixia
2015-10-01
Centroid computation of Gaussian spot is often conducted to get the exact position of a target or to measure wave-front slopes in the fields of target tracking and wave-front sensing. Center of Gravity (CoG) is the most traditional method of centroid computation, known as its low algorithmic complexity. However both electronic noise from the detector and photonic noise from the environment reduces its accuracy. In order to improve the accuracy, thresholding is unavoidable before centroid computation, and optimum threshold need to be selected. In this paper, the model of Gaussian spot is established to analyze the performance of optimum threshold under different Signal-to-Noise Ratio (SNR) conditions. Besides, two optimum threshold selection methods are introduced: TmCoG (using m % of the maximum intensity of spot as threshold), and TkCoG ( usingμn +κσ n as the threshold), μn and σn are the mean value and deviation of back noise. Firstly, their impact on the detection error under various SNR conditions is simulated respectively to find the way to decide the value of k or m. Then, a comparison between them is made. According to the simulation result, TmCoG is superior over TkCoG for the accuracy of selected threshold, and detection error is also lower.
Streak image denoising and segmentation using adaptive Gaussian guided filter.
Jiang, Zhuocheng; Guo, Baoping
2014-09-10
In streak tube imaging lidar (STIL), streak images are obtained using a CCD camera. However, noise in the captured streak images can greatly affect the quality of reconstructed 3D contrast and range images. The greatest challenge for streak image denoising is reducing the noise while preserving details. In this paper, we propose an adaptive Gaussian guided filter (AGGF) for noise removal and detail enhancement of streak images. The proposed algorithm is based on a guided filter (GF) and part of an adaptive bilateral filter (ABF). In the AGGF, the details are enhanced by optimizing the offset parameter. AGGF-denoised streak images are significantly sharper than those denoised by the GF. Moreover, the AGGF is a fast linear time algorithm achieved by recursively implementing a Gaussian filter kernel. Experimentally, AGGF demonstrates its capacity to preserve edges and thin structures and outperforms the existing bilateral filter and domain transform filter in terms of both visual quality and peak signal-to-noise ratio performance.
Streak image denoising and segmentation using adaptive Gaussian guided filter.
Jiang, Zhuocheng; Guo, Baoping
2014-09-10
In streak tube imaging lidar (STIL), streak images are obtained using a CCD camera. However, noise in the captured streak images can greatly affect the quality of reconstructed 3D contrast and range images. The greatest challenge for streak image denoising is reducing the noise while preserving details. In this paper, we propose an adaptive Gaussian guided filter (AGGF) for noise removal and detail enhancement of streak images. The proposed algorithm is based on a guided filter (GF) and part of an adaptive bilateral filter (ABF). In the AGGF, the details are enhanced by optimizing the offset parameter. AGGF-denoised streak images are significantly sharper than those denoised by the GF. Moreover, the AGGF is a fast linear time algorithm achieved by recursively implementing a Gaussian filter kernel. Experimentally, AGGF demonstrates its capacity to preserve edges and thin structures and outperforms the existing bilateral filter and domain transform filter in terms of both visual quality and peak signal-to-noise ratio performance. PMID:25321679
Large-size Gaussian mode in unstable resonators using Gaussian mirrors.
McCarthy, N; Lavigne, P
1985-11-01
Gaussian modes with large sections have been experimentally produced in Cassegrain resonators using Gaussian reflectivity convex couplers. The far field of the beam, which was coupled through a Gaussian coupler, was found to be free from secondary rings. PMID:19730482
Tachyon mediated non-Gaussianity
Dutta, Bhaskar; Leblond, Louis; Kumar, Jason
2008-10-15
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.
Optimal Mueller matrix estimation in the presence of Poisson shot noise.
Anna, Guillaume; Goudail, François
2012-09-10
We address the optimization of Mueller polarimeters in the presence of additive Gaussian noise and signal-dependent shot noise, which are two dominant types of noise in most imaging systems. We propose polarimeter architectures in which the noise variances on each coefficient of the Mueller matrix are equalized and independent of the observed matrices. PMID:23037256
Comparative Analysis of Median and Average Filters in Impulse Noise Suppression
NASA Astrophysics Data System (ADS)
Shi, Luyao; Chen, Yang; Yuan, Wenlong; Zhang, Libo; Yang, Benqiang; Shu, Huazhong; Luo, Limin; Coatrieux, Jean-Louis
2015-10-01
Median type filters coupled with the Laplacian distribution assumption have shown a high efficiency in suppressing impulse noise. We however demonstrate in this paper that the Gaussian distribution assumption is more preferable than Laplacian distribution assumption in suppressing impulse noise, especially for high noise densities. This conclusion is supported by numerical experiments with different noise densities and filter models.
Analysis and modeling of noise in biomedical systems.
Ranjbaran, Mina; Jalaleddini, Kian; Lopez, Diego Guarin; Kearney, Robert E; Galiana, Henrietta L
2013-01-01
Noise characteristics play an important role in evaluating tools developed to study biomedical systems. Despite usual assumptions, noise in biomedical systems is often nonwhite or non-Gaussian. In this paper, we present a method to analyze the noise component of a biomedical system. We demonstrate the effectiveness of the method in the analysis of noise in voluntary ankle torque measured by a torque transducer and eye movements measured by electro-oculography (EOG).
NASA Technical Reports Server (NTRS)
Pendley, R. E.
1982-01-01
The problem of airport noise at several airports and air bases is detailed. Community reactions to the noise, steps taken to reduce jet engine noise, and the effect of airport use restrictions and curfews on air transportation are discussed. The adverse effect of changes in allowable operational noise on airport safety and altenative means for reducing noise pollution are considered. Community-airport relations and public relations are discussed.
NASA Technical Reports Server (NTRS)
Strahle, W. C.
1977-01-01
A review of the subject of combustion generated noise is presented. Combustion noise is an important noise source in industrial furnaces and process heaters, turbopropulsion and gas turbine systems, flaring operations, Diesel engines, and rocket engines. The state-of-the-art in combustion noise importance, understanding, prediction and scaling is presented for these systems. The fundamentals and available theories of combustion noise are given. Controversies in the field are discussed and recommendations for future research are made.
Non-Gaussian microwave background fluctuations from nonlinear gravitational effects
NASA Technical Reports Server (NTRS)
Salopek, D. S.; Kunstatter, G. (Editor)
1991-01-01
Whether the statistics of primordial fluctuations for structure formation are Gaussian or otherwise may be determined if the Cosmic Background Explorer (COBE) Satellite makes a detection of the cosmic microwave-background temperature anisotropy delta T(sub CMB)/T(sub CMB). Non-Gaussian fluctuations may be generated in the chaotic inflationary model if two scalar fields interact nonlinearly with gravity. Theoretical contour maps are calculated for the resulting Sachs-Wolfe temperature fluctuations at large angular scales (greater than 3 degrees). In the long-wavelength approximation, one can confidently determine the nonlinear evolution of quantum noise with gravity during the inflationary epoch because: (1) different spatial points are no longer in causal contact; and (2) quantum gravity corrections are typically small-- it is sufficient to model the system using classical random fields. If the potential for two scalar fields V(phi sub 1, phi sub 2) possesses a sharp feature, then non-Gaussian fluctuations may arise. An explicit model is given where cold spots in delta T(sub CMB)/T(sub CMB) maps are suppressed as compared to the Gaussian case. The fluctuations are essentially scale-invariant.
Gaussian quantum steering and its asymmetry in curved spacetime
NASA Astrophysics Data System (ADS)
Wang, Jieci; Cao, Haixin; Jing, Jiliang; Fan, Heng
2016-06-01
We study Gaussian quantum steering and its asymmetry in the background of a Schwarzschild black hole. We present a Gaussian channel description of quantum state evolution under the influence of Hawking radiation. We find that thermal noise introduced by the Hawking effect will destroy the steerability between an inertial observer Alice and an accelerated observer Bob who hovers outside the event horizon, while it generates steerability between Bob and a hypothetical observer anti-Bob inside the event horizon. Unlike entanglement behaviors in curved spacetime, here the steering from Alice to Bob suffers from a "sudden death" and the steering from anti-Bob to Bob experiences a "sudden birth" with increasing Hawking temperature. We also find that the Gaussian steering is always asymmetric and the maximum steering asymmetry cannot exceed ln 2 , which means the state never evolves to an extremal asymmetry state. Furthermore, we obtain the parameter settings that maximize steering asymmetry and find that (i) s =arccosh cosh/2r 1 -sinh2r is the critical point of steering asymmetry and (ii) the attainment of maximal steering asymmetry indicates the transition between one-way steerability and both-way steerability for the two-mode Gaussian state under the influence of Hawking radiation.
Ramírez Ávila, G M; Kurths, J; Guisset, J L; Deneubourg, J L
2010-11-01
We study the influence of white gaussian noise in a system of two mutually coupled light-controlled oscillators (LCOs). We show that under certain noise intensity conditions, noise can destroy or enhance synchronization. We build some Arnold tonguelike structures in order to explain the effects due to noise. It is remarkable that noise-enhanced synchronization is possible only when the variances of the noise acting on each of the LCOs are different.
GAUSSIAN BEAM LASER RESONATOR PROGRAM
NASA Technical Reports Server (NTRS)
Cross, P. L.
1994-01-01
In designing a laser cavity, the laser engineer is frequently concerned with more than the stability of the resonator. Other considerations include the size of the beam at various optical surfaces within the resonator or the performance of intracavity line-narrowing or other optical elements. Laser resonators obey the laws of Gaussian beam propagation, not geometric optics. The Gaussian Beam Laser Resonator Program models laser resonators using Gaussian ray trace techniques. It can be used to determine the propagation of radiation through laser resonators. The algorithm used in the Gaussian Beam Resonator program has three major components. First, the ray transfer matrix for the laser resonator must be calculated. Next calculations of the initial beam parameters, specifically, the beam stability, the beam waist size and location for the resonator input element, and the wavefront curvature and beam radius at the input surface to the first resonator element are performed. Finally the propagation of the beam through the optical elements is computed. The optical elements can be modeled as parallel plates, lenses, mirrors, dummy surfaces, or Gradient Index (GRIN) lenses. A Gradient Index lens is a good approximation of a laser rod operating under a thermal load. The optical system may contain up to 50 elements. In addition to the internal beam elements the optical system may contain elements external to the resonator. The Gaussian Beam Resonator program was written in Microsoft FORTRAN (Version 4.01). It was developed for the IBM PS/2 80-071 microcomputer and has been implemented on an IBM PC compatible under MS DOS 3.21. The program was developed in 1988 and requires approximately 95K bytes to operate.
Gaussian Velocity Distributions in Avalanches
NASA Astrophysics Data System (ADS)
Shattuck, Mark
2004-03-01
Imagine a world where gravity is so strong that if an ice cube is tilted the shear forces melt the surface and water avalanches down. Further imagine that the ambient temperature is so low that the water re-freezes almost immediately. This is the world of granular flows. As a granular solid is tilted the surface undergoes a sublimation phase transition and a granular gas avalanches down the surface, but the inelastic collisions rapidly remove energy from the flow lowering the granular temperature (kinetic energy per particle) until the gas solidifies again. It is under these extreme conditions that we attempt to uncover continuum granular flow properties. Typical continuum theories like Navier-Stokes equation for fluids follow the space-time evolution of the first few moments of the velocity distribution. We study continuously avalanching flow in a rotating two-dimensional granular drum using high-speed video imaging and extract the position and velocities of the particles. We find a universal near Gaussian velocity distribution throughout the flowing regions, which are characterized by a liquid-like radial distribution function. In the remaining regions, in which the radial distribution function develops sharp crystalline peaks, the velocity distribution has a Gaussian peak but is much broader in the tails. In a companion experiment on a vibrated two-dimensional granular fluid under constant pressure, we find a clear gas-solid phase transition in which both the temperature and density change discontinuously. This suggests that a low temperature crystal and a high temperature gas can coexist in steady state. This coexistence could result in a narrower, cooler, Gaussian peak and a broader, warmer, Gaussian tail like the non-Gaussian behavior seen in the crystalline portions of the rotating drum.
NASA Astrophysics Data System (ADS)
Brett, Tobias; Galla, Tobias
2014-03-01
We present a heuristic derivation of Gaussian approximations for stochastic chemical reaction systems with distributed delay. In particular, we derive the corresponding chemical Langevin equation. Due to the non-Markovian character of the underlying dynamics, these equations are integro-differential equations, and the noise in the Gaussian approximation is coloured. Following on from the chemical Langevin equation, a further reduction leads to the linear-noise approximation. We apply the formalism to a delay variant of the celebrated Brusselator model, and show how it can be used to characterise noise-driven quasi-cycles, as well as noise-triggered spiking. We find surprisingly intricate dependence of the typical frequency of quasi-cycles on the delay period.
Brett, Tobias; Galla, Tobias
2014-03-28
We present a heuristic derivation of Gaussian approximations for stochastic chemical reaction systems with distributed delay. In particular, we derive the corresponding chemical Langevin equation. Due to the non-Markovian character of the underlying dynamics, these equations are integro-differential equations, and the noise in the Gaussian approximation is coloured. Following on from the chemical Langevin equation, a further reduction leads to the linear-noise approximation. We apply the formalism to a delay variant of the celebrated Brusselator model, and show how it can be used to characterise noise-driven quasi-cycles, as well as noise-triggered spiking. We find surprisingly intricate dependence of the typical frequency of quasi-cycles on the delay period. PMID:24697429
Brett, Tobias Galla, Tobias
2014-03-28
We present a heuristic derivation of Gaussian approximations for stochastic chemical reaction systems with distributed delay. In particular, we derive the corresponding chemical Langevin equation. Due to the non-Markovian character of the underlying dynamics, these equations are integro-differential equations, and the noise in the Gaussian approximation is coloured. Following on from the chemical Langevin equation, a further reduction leads to the linear-noise approximation. We apply the formalism to a delay variant of the celebrated Brusselator model, and show how it can be used to characterise noise-driven quasi-cycles, as well as noise-triggered spiking. We find surprisingly intricate dependence of the typical frequency of quasi-cycles on the delay period.
Brett, Tobias; Galla, Tobias
2014-03-28
We present a heuristic derivation of Gaussian approximations for stochastic chemical reaction systems with distributed delay. In particular, we derive the corresponding chemical Langevin equation. Due to the non-Markovian character of the underlying dynamics, these equations are integro-differential equations, and the noise in the Gaussian approximation is coloured. Following on from the chemical Langevin equation, a further reduction leads to the linear-noise approximation. We apply the formalism to a delay variant of the celebrated Brusselator model, and show how it can be used to characterise noise-driven quasi-cycles, as well as noise-triggered spiking. We find surprisingly intricate dependence of the typical frequency of quasi-cycles on the delay period.
Fault diagnosis using noise modeling and a new artificial immune system based algorithm
NASA Astrophysics Data System (ADS)
Abbasi, Farshid; Mojtahedi, Alireza; Ettefagh, Mir Mohammad
2015-12-01
A new fault classification/diagnosis method based on artificial immune system (AIS) algorithms for the structural systems is proposed. In order to improve the accuracy of the proposed method, i.e., higher success rate, Gaussian and non-Gaussian noise generating models are applied to simulate environmental noise. The identification of noise model, known as training process, is based on the estimation of the noise model parameters by genetic algorithms (GA) utilizing real experimental features. The proposed fault classification/diagnosis algorithm is applied to the noise contaminated features. Then, the results are compared to that obtained without noise modeling. The performance of the proposed method is examined using three laboratory case studies in two healthy and damaged conditions. Finally three different types of noise models are studied and it is shown experimentally that the proposed algorithm with non-Gaussian noise modeling leads to more accurate clustering of memory cells as the major part of the fault classification procedure.
Gaussian statistics for palaeomagnetic vectors
Love, J.J.; Constable, C.G.
2003-01-01
With the aim of treating the statistics of palaeomagnetic directions and intensities jointly and consistently, we represent the mean and the variance of palaeomagnetic vectors, at a particular site and of a particular polarity, by a probability density function in a Cartesian three-space of orthogonal magnetic-field components consisting of a single (unimoda) non-zero mean, spherically-symmetrical (isotropic) Gaussian function. For palaeomagnetic data of mixed polarities, we consider a bimodal distribution consisting of a pair of such symmetrical Gaussian functions, with equal, but opposite, means and equal variances. For both the Gaussian and bi-Gaussian distributions, and in the spherical three-space of intensity, inclination, and declination, we obtain analytical expressions for the marginal density functions, the cumulative distributions, and the expected values and variances for each spherical coordinate (including the angle with respect to the axis of symmetry of the distributions). The mathematical expressions for the intensity and off-axis angle are closed-form and especially manageable, with the intensity distribution being Rayleigh-Rician. In the limit of small relative vectorial dispersion, the Gaussian (bi-Gaussian) directional distribution approaches a Fisher (Bingham) distribution and the intensity distribution approaches a normal distribution. In the opposite limit of large relative vectorial dispersion, the directional distributions approach a spherically-uniform distribution and the intensity distribution approaches a Maxwell distribution. We quantify biases in estimating the properties of the vector field resulting from the use of simple arithmetic averages, such as estimates of the intensity or the inclination of the mean vector, or the variances of these quantities. With the statistical framework developed here and using the maximum-likelihood method, which gives unbiased estimates in the limit of large data numbers, we demonstrate how to
Dynamical suppression of telegraph and 1/f noise due to quantum bistable fluctuators
Falci, G.; D'Arrigo, A.; Mastellone, A.; Paladino, E.
2004-10-01
We study dynamical decoupling of a qubit from non-Gaussian quantum noise due to discrete sources, as bistable fluctuators and 1/f noise. We obtain analytic and numerical results for generic operating points. For very large pulse frequency, where dynamic decoupling compensates decoherence, we found universal behavior. At intermediate frequencies noise can be compensated or enhanced, depending on the nature of the fluctuators and on the operating point. Our technique can be applied to a larger class of non-Gaussian environments.
Linear-Quadratic-Gaussian Regulator Developed for a Magnetic Bearing
NASA Technical Reports Server (NTRS)
Choi, Benjamin B.
2002-01-01
Linear-Quadratic-Gaussian (LQG) control is a modern state-space technique for designing optimal dynamic regulators. It enables us to trade off regulation performance and control effort, and to take into account process and measurement noise. The Structural Mechanics and Dynamics Branch at the NASA Glenn Research Center has developed an LQG control for a fault-tolerant magnetic bearing suspension rig to optimize system performance and to reduce the sensor and processing noise. The LQG regulator consists of an optimal state-feedback gain and a Kalman state estimator. The first design step is to seek a state-feedback law that minimizes the cost function of regulation performance, which is measured by a quadratic performance criterion with user-specified weighting matrices, and to define the tradeoff between regulation performance and control effort. The next design step is to derive a state estimator using a Kalman filter because the optimal state feedback cannot be implemented without full state measurement. Since the Kalman filter is an optimal estimator when dealing with Gaussian white noise, it minimizes the asymptotic covariance of the estimation error.
Albacete, Javier L.; Kovchegov, Yuri V.; Taliotis, Anastasios
2009-03-23
We calculate the total cross section for the scattering of a quark-anti-quark dipole on a large nucleus at high energy for a strongly coupled N = 4 super Yang-Mills theory using AdS/CFT correspondence. We model the nucleus by a metric of a shock wave in AdS{sub 5}. We then calculate the expectation value of the Wilson loop (the dipole) by finding the extrema of the Nambu-Goto action for an open string attached to the quark and antiquark lines of the loop in the background of an AdS{sub 5} shock wave. We find two physically meaningful extremal string configurations. For both solutions we obtain the forward scattering amplitude N for the quark dipole-nucleus scattering. We study the onset of unitarity with increasing center-of-mass energy and transverse size of the dipole: we observe that for both solutions the saturation scale Q{sub s} is independent of energy/Bjorken-x and depends on the atomic number of the nucleus as Q{sub s}{approx}A{sup 1/3}. Finally we observe that while one of the solutions we found corresponds to the pomeron intercept of {alpha}{sub P} = 2 found earlier in the literature, when extended to higher energy or larger dipole sizes it violates the black disk limit. The other solution we found respects the black disk limit and yields the pomeron intercept of {alpha}{sub P} = 1.5. We thus conjecture that the right pomeron intercept in gauge theories at strong coupling may be {alpha}{sub P} = 1.5.
NASA Technical Reports Server (NTRS)
Bragdon, C. R.
1982-01-01
Airport and community land use planning as they relate to airport noise reduction are discussed. Legislation, community relations, and the physiological effect of airport noise are considered. Noise at the Logan, Los Angeles, and Minneapolis/St. Paul airports is discussed.
Achromatic doublets for Gaussian beams
NASA Astrophysics Data System (ADS)
Biraud, F.; Daigne, G.
1991-04-01
The properties of doublets of thin lenses in the Gaussian optics approximation were investigated. Two different ways for such a doublet to give strictly achromatic images of the input beam waist were found. Both solutions may be useful in a variety of applications, one being the possibility of shaping asymmetrical beams for fan beam antennas illumination. Using modes higher than the fundamental mode will allow the design of more realistic focal systems.
Nurmi, Sami; Byrnes, Christian T.; Tasinato, Gianmassimo E-mail: ctb22@sussex.ac.uk
2013-06-01
Primordial perturbations with wavelengths greater than the observable universe shift the effective background fields in our observable patch from their global averages over the inflating space. This leads to a landscape picture where the properties of our observable patch depend on its location and may significantly differ from the expectation values predicted by the underlying fundamental inflationary model. We show that if multiple fields are present during inflation, this may happen even if our horizon exit would be preceded by only a few e-foldings of inflation. Non-Gaussian statistics are especially affected: for example models of local non-Gaussianity predicting |f{sub NL}{sup 0}| >> 10 over the entire inflating volume can have a probability up to a few tens of percent to generate a non-detectable bispectrum in our observable patch |f{sub NL}{sup obs.}|∼<10. In this work we establish systematic connections between the observable local properties of primordial perturbations and the global properties of the inflating space which reflect the underlying high energy physics. We study in detail the implications of both a detection and non-detection of primordial non-Gaussianity by Planck, and discover novel ways of characterising the naturalness of different observational configurations.
Purification of Gaussian maximally mixed states
NASA Astrophysics Data System (ADS)
Jeong, Kabgyun; Lim, Youngrong
2016-10-01
We find that the purifications of several Gaussian maximally mixed states (GMMSs) correspond to some Gaussian maximally entangled states (GMESs) in the continuous-variable regime. Here, we consider a two-mode squeezed vacuum (TMSV) state as a purification of the thermal state and construct a general formalism of the Gaussian purification process. Moreover, we introduce other kind of GMESs via the process. All of our purified states of the GMMSs exhibit Gaussian profiles; thus, the states show maximal quantum entanglement in the Gaussian regime.
The Switch in a Genetic Toggle System with Lévy Noise
Xu, Yong; Li, Yongge; Zhang, Hao; Li, Xiaofan; Kurths, Jürgen
2016-01-01
A bistable toggle switch is a paradigmatic model in the field of biology. The dynamics of the system induced by Gaussian noise has been intensively investigated, but Gaussian noise cannot incorporate large bursts typically occurring in real experiments. This paper aims to examine effects of variations from one protein imposed by a non-Gaussian Lévy noise, which is able to describe even large jumps, on the coherent switch and the on/off switch via the steady-state probability density, the joint steady-state probability density, and the mean first passage time. We find that a large burst of one protein due to the Lévy noises can induce coherent switches even with small noise intensities in contrast to the Gaussian case which requires large intensities for this. The influences of the stability index, skewness parameter and noise intensity on the on/off switch are analyzed, leading to an adjustment of the concentrations of both proteins and a decision which stable point to stay most. The mean first passage times show complex effects under Lévy noise, especially the stability index and skewness parameter. Our results also imply that the presence of non-Gaussian Lévy noises has fundamentally changed the escape mechanism in such a system compared with Gaussian noise. PMID:27539010
The Switch in a Genetic Toggle System with Lévy Noise
NASA Astrophysics Data System (ADS)
Xu, Yong; Li, Yongge; Zhang, Hao; Li, Xiaofan; Kurths, Jürgen
2016-08-01
A bistable toggle switch is a paradigmatic model in the field of biology. The dynamics of the system induced by Gaussian noise has been intensively investigated, but Gaussian noise cannot incorporate large bursts typically occurring in real experiments. This paper aims to examine effects of variations from one protein imposed by a non-Gaussian Lévy noise, which is able to describe even large jumps, on the coherent switch and the on/off switch via the steady-state probability density, the joint steady-state probability density, and the mean first passage time. We find that a large burst of one protein due to the Lévy noises can induce coherent switches even with small noise intensities in contrast to the Gaussian case which requires large intensities for this. The influences of the stability index, skewness parameter and noise intensity on the on/off switch are analyzed, leading to an adjustment of the concentrations of both proteins and a decision which stable point to stay most. The mean first passage times show complex effects under Lévy noise, especially the stability index and skewness parameter. Our results also imply that the presence of non-Gaussian Lévy noises has fundamentally changed the escape mechanism in such a system compared with Gaussian noise.
Long-distance continuous-variable quantum key distribution with a Gaussian modulation
Jouguet, Paul; Kunz-Jacques, Sebastien; Leverrier, Anthony
2011-12-15
We designed high-efficiency error correcting codes allowing us to extract an errorless secret key in a continuous-variable quantum key distribution (CVQKD) protocol using a Gaussian modulation of coherent states and a homodyne detection. These codes are available for a wide range of signal-to-noise ratios on an additive white Gaussian noise channel with a binary modulation and can be combined with a multidimensional reconciliation method proven secure against arbitrary collective attacks. This improved reconciliation procedure considerably extends the secure range of a CVQKD with a Gaussian modulation, giving a secret key rate of about 10{sup -3} bit per pulse at a distance of 120 km for reasonable physical parameters.
Experimental demonstration of macroscopic quantum coherence in Gaussian states
Marquardt, Christoph; Leuchs, Gerd; Andersen, Ulrik L.; Takeno, Yuishi; Yukawa, Mitsuyoshi; Yonezawa, Hidehiro; Furusawa, Akira
2007-09-15
We witness experimentally the presence of macroscopic coherence in Gaussian quantum states using a recently proposed criterion [E. G. Cavalcanti and M. D. Reid, Phys. Rev. Lett. 97 170405 (2006)]. The macroscopic coherence stems from interference between macroscopically distinct states in phase space, and we prove experimentally that a coherent state contains these features with a distance in phase space of 0.51{+-}0.02 shot noise units. This is surprising because coherent states are generally considered being at the border between classical and quantum states, not yet displaying any nonclassical effect. For squeezed and entangled states the effect may be larger but depends critically on the state purity.
1/f Noise Outperforms White Noise in Sensitizing Baroreflex Function in the Human Brain
NASA Astrophysics Data System (ADS)
Soma, Rika; Nozaki, Daichi; Kwak, Shin; Yamamoto, Yoshiharu
2003-08-01
We show that externally added 1/f noise more effectively sensitizes the baroreflex centers in the human brain than white noise. We examined the compensatory heart rate response to a weak periodic signal introduced via venous blood pressure receptors while adding 1/f or white noise with the same variance to the brain stem through bilateral cutaneous stimulation of the vestibular afferents. In both cases, this noisy galvanic vestibular stimulation optimized covariance between the weak input signals and the heart rate responses. However, the optimal level with 1/f noise was significantly lower than with white noise, suggesting a functional benefit of 1/f noise for neuronal information transfer in the brain.
NASA Astrophysics Data System (ADS)
Schmitz, F. H.
1991-08-01
The physical characteristics and sources of rotorcraft noise as they exist today are presented. Emphasis is on helicopter-like vehicles, that is, on rotorcraft in nonaxial flight. The mechanisms of rotor noise are reviewed in a simple physical manner for the most dominant sources of rotorcraft noise. With simple models, the characteristic time- and frequency-domain features of these noise sources are presented for idealized cases. Full-scale data on several rotorcraft are then reviewed to allow for the easy identification of the type and extent of the radiating noise. Methods and limitations of using scaled models to test for several noise sources are subsequently presented. Theoretical prediction methods are then discussed and compared with experimental data taken under very controlled conditions. Finally, some promising noise reduction technology is reviewed.
NASA Technical Reports Server (NTRS)
Schmitz, F. H.
1991-01-01
The physical characteristics and sources of rotorcraft noise as they exist today are presented. Emphasis is on helicopter-like vehicles, that is, on rotorcraft in nonaxial flight. The mechanisms of rotor noise are reviewed in a simple physical manner for the most dominant sources of rotorcraft noise. With simple models, the characteristic time- and frequency-domain features of these noise sources are presented for idealized cases. Full-scale data on several rotorcraft are then reviewed to allow for the easy identification of the type and extent of the radiating noise. Methods and limitations of using scaled models to test for several noise sources are subsequently presented. Theoretical prediction methods are then discussed and compared with experimental data taken under very controlled conditions. Finally, some promising noise reduction technology is reviewed.
Probabilistic stellar rotation periods with Gaussian processes
NASA Astrophysics Data System (ADS)
Angus, Ruth; Aigrain, Suzanne; Foreman-Mackey, Daniel
2015-08-01
Stellar rotation has many applications in the field of exoplanets. High-precision photometry from space-based missions like Kepler and K2 allows us to measure stellar rotation periods directly from light curves. Stellar variability produced by rotation is usually not sinusoidal or perfectly periodic, therefore sine-fitting periodograms are not well suited to rotation period measurement. Autocorrelation functions are often used to extract periodic information from light curves, however uncertainties on rotation periods measured by autocorrelation are difficult to define. A ‘by eye’ check, or a set of heuristic criteria are used to validate measurements and rotation periods are only reported for stars that pass this vetting process. A probabilistic rotation period measurement method, with a suitable generative model bypasses the need for a validation stage and can produce realistic uncertainties. The physics driving the production of variability in stellar light curves is still poorly understood and difficult to model. We therefore use an effective model for stellar variability: a Gaussian process with a quasi-periodic covariance function. By injecting fake signals into Kepler light curves we show that the GP model is well suited to quasi-periodic, non-sinusoidal signals, is capable of modelling noise and physical signals simultaneously and provides probabilistic rotation period measurements with realistic uncertainties.
Bayesian nonparametric adaptive control using Gaussian processes.
Chowdhary, Girish; Kingravi, Hassan A; How, Jonathan P; Vela, Patricio A
2015-03-01
Most current model reference adaptive control (MRAC) methods rely on parametric adaptive elements, in which the number of parameters of the adaptive element are fixed a priori, often through expert judgment. An example of such an adaptive element is radial basis function networks (RBFNs), with RBF centers preallocated based on the expected operating domain. If the system operates outside of the expected operating domain, this adaptive element can become noneffective in capturing and canceling the uncertainty, thus rendering the adaptive controller only semiglobal in nature. This paper investigates a Gaussian process-based Bayesian MRAC architecture (GP-MRAC), which leverages the power and flexibility of GP Bayesian nonparametric models of uncertainty. The GP-MRAC does not require the centers to be preallocated, can inherently handle measurement noise, and enables MRAC to handle a broader set of uncertainties, including those that are defined as distributions over functions. We use stochastic stability arguments to show that GP-MRAC guarantees good closed-loop performance with no prior domain knowledge of the uncertainty. Online implementable GP inference methods are compared in numerical simulations against RBFN-MRAC with preallocated centers and are shown to provide better tracking and improved long-term learning.
Industrial jet noise: Coanda nozzles
NASA Astrophysics Data System (ADS)
Li, P.; Halliwell, N. A.
1985-04-01
Within the U.K. manufacturing industries noise from industrial jets ranks third as a major contributor to industrial deafness. Noise control is hindered because use is made of the air once it has exuded from the nozzle exit. Important tasks include swarf removal, paint spreading, cooling, etc. Nozzles which employ the Coanda effect appear to offer the possibility of significant noise reduction whilst maintaining high thrust efficiency when compared with the commonly used simple open pipe or ordinary convergent nozzle. In this paper the performance of Coanda-type nozzles is examined in detail and an index rating for nozzle performance is introduced. Results show that far field stagnation pressure distributions are Gaussian and similar in all cases with a dispersion coefficient σ = 0·64. Noise reduction and thrust efficiency are shown to be closely related to the design geometry of the central body of the nozzle. Performance is based on four fundamental characteristics, these being the noise level at 1 m from the exit and at a 90° station to the nozzle axis, and the thrust on a chosen profile, the noise reduction and the thrust efficiency. Physically, performance is attributed to flow near field effects where, although all nozzles are choked, shock cell associated noise is absent.
Laser line shape and spectral density of frequency noise
Stephan, G.M.; Blin, S.; Besnard, P.; Tam, T.T.; Tetu, M.
2005-04-01
Published experimental results show that single-mode laser light is characterized in the microwave range by a frequency noise which essentially includes a white part and a 1/f (flicker) part. We theoretically show that the spectral density (the line shape) which is compatible with these results is a Voigt profile whose Lorentzian part or homogeneous component is linked to the white noise and the Gaussian part to the 1/f noise. We measure semiconductor laser line profiles and verify that they can be fit with Voigt functions. It is also verified that the width of the Lorentzian part varies like 1/P where P is the laser power while the width of the Gaussian part is more of a constant. Finally, we theoretically show from first principles that laser line shapes are also described by Voigt functions where the Lorentzian part is the laser Airy function and the Gaussian part originates from population noise.
NASA Astrophysics Data System (ADS)
Ye, Chuyang; Bazin, Pierre-Louis; Bogovic, John A.; Ying, Sarah H.; Prince, Jerry L.
2012-02-01
The cerebellar peduncles are white matter tracts that play an important role in the communication of the cerebellum with other regions of the brain. They can be grouped into three fiber bundles: inferior cerebellar peduncle middle cerebellar peduncle, and superior cerebellar peduncle. Their automatic segmentation on diffusion tensor images would enable a better understanding of the cerebellum and would be less time-consuming and more reproducible than manual delineation. This paper presents a method that automatically labels the three fiber bundles based on the segmentatin results from the diffusion oriented tract segmentation (DOTS) algorithm, which achieves volume segmentation of white matter tracts using a Markov random field (MRF) framework. We use the DOTS labeling result as a guide to determine the classification of fibers produced by wild bootstrap probabilistic tractography. Mean distances from each fiber to each DOTS volume label are defined and then used as features that contribute to classification. A supervised Gaussian classifier is employed to label the fibers. Manually delineated cerebellar peduncles serve as training data to determine the parameters of class probabilities for each label. Fibers are labeled ad the class that has the highest posterior probability. An outlier detection ste[ re,pves fober tracts that belong to noise of that are not modeled by DOTS. Experiments show a successful classification of the cerebellar peduncles. We have also compared results between successive scans to demonstrate the reproducibility of the proposed method.
Detection of Gaussian signals in Poisson-modulated interference.
Streit, R L
2000-10-01
Passive broadband detection of target signals by an array of hydrophones in the presence of multiple discrete interferers is analyzed under Gaussian statistics and low signal-to-noise ratio conditions. A nonhomogeneous Poisson-modulated interference process is used to model the ensemble of possible arrival directions of the discrete interferers. Closed-form expressions are derived for the recognition differential of the passive-sonar equation in the presence of Poisson-modulated interference. The interference-compensated recognition differential differs from the classical recognition differential by an additive positive term that depend on the interference-to-noise ratio, the directionality of the Poisson-modulated interference, and the array beam pattern.
NASA Astrophysics Data System (ADS)
Crighton, David G.
1991-08-01
Current understanding of airframe noise was reviewed as represented by experiment at model and full scale, by theoretical modeling, and by empirical correlation models. The principal component sources are associated with the trailing edges of wing and tail, deflected trailing edge flaps, flap side edges, leading edge flaps or slats, undercarriage gear elements, gear wheel wells, fuselage and wing boundary layers, and panel vibration, together with many minor protrusions like radio antennas and air conditioning intakes which may contribute significantly to perceived noise. There are also possibilities for interactions between the various mechanisms. With current engine technology, the principal airframe noise mechanisms dominate only at low frequencies, typically less than 1 kHz and often much lower, but further reduction of turbomachinery noise in particular may make airframe noise the principal element of approach noise at frequencies in the sensitive range.
Gaussian effective potential: Quantum mechanics
NASA Astrophysics Data System (ADS)
Stevenson, P. M.
1984-10-01
We advertise the virtues of the Gaussian effective potential (GEP) as a guide to the behavior of quantum field theories. Much superior to the usual one-loop effective potential, the GEP is a natural extension of intuitive notions familiar from quantum mechanics. A variety of quantum-mechanical examples are studied here, with an eye to field-theoretic analogies. Quantum restoration of symmetry, dynamical mass generation, and "quantum-mechanical resuscitation" are among the phenomena discussed. We suggest how the GEP could become the basis of a systematic approximation procedure. A companion paper will deal with scalar field theory.
NASA Technical Reports Server (NTRS)
Mixson, John S.; Wilby, John F.
1991-01-01
The generation and control of flight vehicle interior noise is discussed. Emphasis is placed on the mechanisms of transmission through airborne and structure-borne paths and the control of cabin noise by path modification. Techniques for identifying the relative contributions of the various source-path combinations are also discussed along with methods for the prediction of aircraft interior noise such as those based on the general modal theory and statistical energy analysis.
Robust Point Set Registration Using Gaussian Mixture Models.
Jian, Bing; Vemuri, Baba C
2011-08-01
In this paper, we present a unified framework for the rigid and nonrigid point set registration problem in the presence of significant amounts of noise and outliers. The key idea of this registration framework is to represent the input point sets using Gaussian mixture models. Then, the problem of point set registration is reformulated as the problem of aligning two Gaussian mixtures such that a statistical discrepancy measure between the two corresponding mixtures is minimized. We show that the popular iterative closest point (ICP) method [1] and several existing point set registration methods [2], [3], [4], [5], [6], [7] in the field are closely related and can be reinterpreted meaningfully in our general framework. Our instantiation of this general framework is based on the the L2 distance between two Gaussian mixtures, which has the closed-form expression and in turn leads to a computationally efficient registration algorithm. The resulting registration algorithm exhibits inherent statistical robustness, has an intuitive interpretation, and is simple to implement. We also provide theoretical and experimental comparisons with other robust methods for point set registration. PMID:21173443
An adaptive unsupervised hyperspectral classification method based on Gaussian distribution
NASA Astrophysics Data System (ADS)
Yue, Jiang; Wu, Jing-wei; Zhang, Yi; Bai, Lian-fa
2014-11-01
In order to achieve adaptive unsupervised clustering in the high precision, a method using Gaussian distribution to fit the similarity of the inter-class and the noise distribution is proposed in this paper, and then the automatic segmentation threshold is determined by the fitting result. First, according with the similarity measure of the spectral curve, this method assumes that the target and the background both in Gaussian distribution, the distribution characteristics is obtained through fitting the similarity measure of minimum related windows and center pixels with Gaussian function, and then the adaptive threshold is achieved. Second, make use of the pixel minimum related windows to merge adjacent similar pixels into a picture-block, then the dimensionality reduction is completed and the non-supervised classification is realized. AVIRIS data and a set of hyperspectral data we caught are used to evaluate the performance of the proposed method. Experimental results show that the proposed algorithm not only realizes the adaptive but also outperforms K-MEANS and ISODATA on the classification accuracy, edge recognition and robustness.
X-ray cluster constraints on non-Gaussianity
Shandera, Sarah; Mantz, Adam; Rapetti, David; Allen, Steven W. E-mail: amantz@kicp.uchicago.edu E-mail: swa@stanford.edu
2013-08-01
We report constraints on primordial non-Gaussianity from the abundance of X-ray detected clusters. Our analytic prescription for adding non-Gaussianity to the cluster mass function takes into account moments beyond the skewness, and we demonstrate that those moments should not be ignored in most analyses of cluster data. We constrain the amplitude of the skewness for two scenarios that have different overall levels of non-Gaussianity, characterized by how amplitudes of higher cumulants scale with the skewness. We find that current data can constrain these one-parameter non-Gaussian models at a useful level, but are not sensitive to adding further details of the corresponding inflation scenarios. Combining cluster data with Cosmic Microwave Background constraints on the cosmology and power spectrum amplitude, we find the dimensionless skewness to be 10{sup 3}M{sub 3} = −1{sub −28}{sup +24} for one of our scaling scenarios, and 10{sup 3}M{sub 3} = −4±7 for the other. These are the first constraints on non-Gaussianity from Large Scale Structure that can be usefully applied to any model of primordial non-Gaussianity. The former constraint, when applied to the standard local ansatz (where the n-th cumulant scales as M{sub n}∝M{sub 3}{sup n−2}), corresponds to f{sub NL}{sup local} = −3{sub −91}{sup +78}. When applied to a model with a local-shape bispectrum but higher cumulants that scale as M{sub n}∝M{sub 3}{sup n/3} (the second scaling scenario), the amplitude of the local-shape bispectrum is constrained to be f{sub NL}{sup local*} = −14{sub −21}{sup +22}. For this second scaling (which occurs in various well-motivated models of inflation), we also obtain strong constraints on the equilateral and orthogonal shapes of the bispectrum, f{sub NL}{sup equil} = −52{sub −79}{sup +85} and f{sub NL}{sup orth} = 63{sub −104}{sup +97}. This sensitivity implies that cluster counts could be used to distinguish qualitatively different models for the
NASA Technical Reports Server (NTRS)
1980-01-01
Environmental Health Systems puts forth an increasing effort in the U.S. to develop ways of controlling noise, particularly in industrial environments due to Federal and State laws, labor union insistence and new findings relative to noise pollution impact on human health. NASA's Apollo guidance control system aided in the development of a noise protection product, SMART. The basis of all SMART products is SMART compound a liquid plastic mixture with exceptional energy/sound absorbing qualities. The basic compound was later refined for noise protection use.
Noise-Induced Phase Transitions: Effects of the Noises' Statistics and Spectrum
NASA Astrophysics Data System (ADS)
Deza, Roberto R.; Wio, Horacio S.; Fuentes, Miguel A.
2007-05-01
The local, uncorrelated multiplicative noises driving a second-order, purely noise-induced, ordering phase transition (NIPT) were assumed to be Gaussian and white in the model of [Phys. Rev. Lett. 73, 3395 (1994)]. The potential scientific and technological interest of this phenomenon calls for a study of the effects of the noises' statistics and spectrum. This task is facilitated if these noises are dynamically generated by means of stochastic differential equations (SDE) driven by white noises. One such case is that of Ornstein-Uhlenbeck noises which are stationary, with Gaussian pdf and a variance reduced by the self-correlation time τ, and whose effect on the NIPT phase diagram has been studied some time ago. Another such case is when the stationary pdf is a (colored) Tsallis' q-Gaussian which, being a fat-tail distribution for q > 1 and a compact-support one for q < 1, allows for a controlled exploration of the effects of the departure from Gaussian statistics. As done before with stochastic resonance and other phenomena, we now exploit this tool to study—within a simple mean-field approximation and with an emphasis on the order parameter and the "susceptibility"—the combined effect on NIPT of the noises' statistics and spectrum. Even for relatively small τ, it is shown that whereas fat-tail noise distributions (q > 1) counteract the effect of self-correlation, compact-support ones (q < 1) enhance it. Also, an interesting effect on the susceptibility is seen in the last case.
Monogamy inequality for distributed gaussian entanglement.
Hiroshima, Tohya; Adesso, Gerardo; Illuminati, Fabrizio
2007-02-01
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.
Strongly scale-dependent non-Gaussianity
Riotto, Antonio; Sloth, Martin S.
2011-02-15
We discuss models of primordial density perturbations where the non-Gaussianity is strongly scale dependent. In particular, the non-Gaussianity may have a sharp cutoff and be very suppressed on large cosmological scales, but sizable on small scales. This may have an impact on probes of non-Gaussianity in the large-scale structure and in the cosmic microwave background radiation anisotropies.
D'Amico, Guido; Kleban, Matthew
2014-08-22
We analyze primordial non-Gaussianity in single-field inflationary models when the tensor-to-scalar ratio is large. Our results show that detectable levels of non-Gaussianity f(NL) ∼ 50 are still possible in the simplest class of models described by the effective theory of inflation. However, the shape is very tightly constrained, making a sharp prediction that could be confirmed or falsified by a future detection of non-Gaussianity. PMID:25192084
Monogamy inequality for distributed gaussian entanglement.
Hiroshima, Tohya; Adesso, Gerardo; Illuminati, Fabrizio
2007-02-01
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. PMID:17358836
Sivakumar, Vidyashankar; Banerjee, Arindam; Ravikumar, Pradeep
2016-01-01
We consider the problem of high-dimensional structured estimation with norm-regularized estimators, such as Lasso, when the design matrix and noise are drawn from sub-exponential distributions. Existing results only consider sub-Gaussian designs and noise, and both the sample complexity and non-asymptotic estimation error have been shown to depend on the Gaussian width of suitable sets. In contrast, for the sub-exponential setting, we show that the sample complexity and the estimation error will depend on the exponential width of the corresponding sets, and the analysis holds for any norm. Further, using generic chaining, we show that the exponential width for any set will be at most logp times the Gaussian width of the set, yielding Gaussian width based results even for the sub-exponential case. Further, for certain popular estimators, viz Lasso and Group Lasso, using a VC-dimension based analysis, we show that the sample complexity will in fact be the same order as Gaussian designs. Our general analysis and results are the first in the sub-exponential setting, and are readily applicable to special sub-exponential families such as log-concave and extreme-value distributions. PMID:27563230
Robustness of composite pulse sequences to time-dependent noise
NASA Astrophysics Data System (ADS)
Kabytayev, Chingiz; Green, Todd J.; Khodjasteh, Kaveh; Viola, Lorenza; Biercuk, Michael J.; Brown, Kenneth R.
2014-03-01
Quantum control protocols can minimize the effect of noise sources that reduce the quality of quantum operations. Originally developed for NMR, composite pulse sequences correct for unknown static control errors . We study these compensating pulses in the general case of time-varying Gaussian control noise using a filter-function approach and detailed numerics. Three different noise models were considered in this work: amplitude noise, detuning noise and simultaneous presence of both noises. Pulse sequences are shown to be robust to noise up to frequencies as high as ~10% of the Rabi frequency. Robustness of pulses designed for amplitude noise is explained using a geometric picture that naturally follows from filter function. We also discuss future directions including new pulses correcting for noise of certain frequency. True J. Merrill and Kenneth R. Brown. arXiv:1203.6392v1. In press Adv. Chem. Phys. (2013)
Fuzzy local Gaussian mixture model for brain MR image segmentation.
Ji, Zexuan; Xia, Yong; Sun, Quansen; Chen, Qiang; Xia, Deshen; Feng, David Dagan
2012-05-01
Accurate brain tissue segmentation from magnetic resonance (MR) images is an essential step in quantitative brain image analysis. However, due to the existence of noise and intensity inhomogeneity in brain MR images, many segmentation algorithms suffer from limited accuracy. In this paper, we assume that the local image data within each voxel's neighborhood satisfy the Gaussian mixture model (GMM), and thus propose the fuzzy local GMM (FLGMM) algorithm for automated brain MR image segmentation. This algorithm estimates the segmentation result that maximizes the posterior probability by minimizing an objective energy function, in which a truncated Gaussian kernel function is used to impose the spatial constraint and fuzzy memberships are employed to balance the contribution of each GMM. We compared our algorithm to state-of-the-art segmentation approaches in both synthetic and clinical data. Our results show that the proposed algorithm can largely overcome the difficulties raised by noise, low contrast, and bias field, and substantially improve the accuracy of brain MR image segmentation.
Interspike interval statistics of neurons driven by colored noise.
Lindner, Benjamin
2004-02-01
A perfect integrate-and-fire model driven by colored noise is studied by means of the interspike interval (ISI) density and the serial correlation coefficient. Exact and approximate expressions for these functions are derived for weak dichotomous or Gaussian noise, respectively. It is shown that correlations in the input result in positive correlations in the ISI sequence and in a reduction of ISI variability. The results also indicate that for weak noise, the noise distribution only shapes the ISI density but not the ISI correlations which are determined by the noise's correlation function.
Elegant Gaussian beams for enhanced optical manipulation
Alpmann, Christina Schöler, Christoph; Denz, Cornelia
2015-06-15
Generation of micro- and nanostructured complex light beams attains increasing impact in photonics and laser applications. In this contribution, we demonstrate the implementation and experimental realization of the relatively unknown, but highly versatile class of complex-valued Elegant Hermite- and Laguerre-Gaussian beams. These beams create higher trapping forces compared to standard Gaussian light fields due to their propagation changing properties. We demonstrate optical trapping and alignment of complex functional particles as nanocontainers with standard and Elegant Gaussian light beams. Elegant Gaussian beams will inspire manifold applications in optical manipulation, direct laser writing, or microscopy, where the design of the point-spread function is relevant.
Self-Calibrated Cluster Counts as a Probe of Primordial Non-Gaussianity
Oguri, Masamune; /KIPAC, Menlo Park
2009-05-07
We show that the ability to probe primordial non-Gaussianity with cluster counts is drastically improved by adding the excess variance of counts which contains information on the clustering. The conflicting dependences of changing the mass threshold and including primordial non-Gaussianity on the mass function and biasing indicate that the self-calibrated cluster counts well break the degeneracy between primordial non-Gaussianity and the observable-mass relation. Based on the Fisher matrix analysis, we show that the count variance improves constraints on f{sub NL} by more than an order of magnitude. It exhibits little degeneracy with dark energy equation of state. We forecast that upcoming Hyper Suprime-cam cluster surveys and Dark Energy Survey will constrain primordial non-Gaussianity at the level {sigma}(f{sub NL}) {approx} 8, which is competitive with forecasted constraints from next-generation cosmic microwave background experiments.
Robust stochastic resonance: Signal detection and adaptation in impulsive noise
NASA Astrophysics Data System (ADS)
Kosko, Bart; Mitaim, Sanya
2001-11-01
Stochastic resonance (SR) occurs when noise improves a system performance measure such as a spectral signal-to-noise ratio or a cross-correlation measure. All SR studies have assumed that the forcing noise has finite variance. Most have further assumed that the noise is Gaussian. We show that SR still occurs for the more general case of impulsive or infinite-variance noise. The SR effect fades as the noise grows more impulsive. We study this fading effect on the family of symmetric α-stable bell curves that includes the Gaussian bell curve as a special case. These bell curves have thicker tails as the parameter α falls from 2 (the Gaussian case) to 1 (the Cauchy case) to even lower values. Thicker tails create more frequent and more violent noise impulses. The main feedback and feedforward models in the SR literature show this fading SR effect for periodic forcing signals when we plot either the signal-to-noise ratio or a signal correlation measure against the dispersion of the α-stable noise. Linear regression shows that an exponential law γopt(α)=cAα describes this relation between the impulsive index α and the SR-optimal noise dispersion γopt. The results show that SR is robust against noise ``outliers.'' So SR may be more widespread in nature than previously believed. Such robustness also favors the use of SR in engineering systems. We further show that an adaptive system can learn the optimal noise dispersion for two standard SR models (the quartic bistable model and the FitzHugh-Nagumo neuron model) for the signal-to-noise ratio performance measure. This also favors practical applications of SR and suggests that evolution may have tuned the noise-sensitive parameters of biological systems.
Classical nature of nuclear spin noise near clock transitions of Bi donors in silicon
NASA Astrophysics Data System (ADS)
Ma, Wen-Long; Wolfowicz, Gary; Li, Shu-Shen; Morton, John J. L.; Liu, Ren-Bao
2015-10-01
Whether a quantum bath can be approximated as classical Gaussian noise is a fundamental issue in central spin decoherence and also of practical importance in designing noise-resilient quantum control. Spin qubits based on bismuth donors in silicon have tunable interactions with nuclear spin baths and are first-order insensitive to magnetic noise at so-called clock transitions (CTs). This system is therefore ideal for studying the quantum/classical Gaussian nature of nuclear spin baths since the qubit-bath interaction strength determines the back-action on the baths and hence the adequacy of a Gaussian noise model. We develop a Gaussian noise model with noise correlations determined by quantum calculations and compare the classical noise approximation to the full quantum bath theory. We experimentally test our model through a dynamical decoupling sequence of up to 128 pulses, finding good agreement with simulations and measuring electron spin coherence times approaching 1 s—notably using natural silicon. Our theoretical and experimental study demonstrates that the noise from a nuclear spin bath is analogous to classical Gaussian noise if the back-action of the qubit on the bath is small compared to the internal bath dynamics, as is the case close to CTs. However, far from the CTs, the back-action of the central spin on the bath is such that the quantum model is required to accurately model spin decoherence.
Noise in phase-preserving linear amplifiers
Pandey, Shashank; Jiang, Zhang; Combes, Joshua; Caves, Carlton M.
2014-12-04
The purpose of a phase-preserving linear amplifier is to make a small signal larger, so that it can be perceived by instruments incapable of resolving the original signal, while sacrificing as little as possible in signal-to-noise. Quantum mechanics limits how well this can be done: the noise added by the amplifier, referred to the input, must be at least half a quantum at the operating frequency. This well-known quantum limit only constrains the second moments of the added noise. Here we provide the quantum constraints on the entire distribution of added noise: any phasepreserving linear amplifier is equivalent to a parametric amplifier with a physical state σ for the ancillary mode; σ determines the properties of the added noise.
Continuous-variable quantum cryptography is secure against non-Gaussian attacks.
Grosshans, Frédéric; Cerf, Nicolas J
2004-01-30
A general study of arbitrary finite-size coherent attacks against continuous-variable quantum cryptographic schemes is presented. It is shown that, if the size of the blocks that can be coherently attacked by an eavesdropper is fixed and much smaller than the key size, then the optimal attack for a given signal-to-noise ratio in the transmission line is an individual Gaussian attack. Consequently, non-Gaussian coherent attacks do not need to be considered in the security analysis of such quantum cryptosystems.
Gras, S.; Blair, D. G.; Ju, L.
2010-02-15
To reduce the thermal noise in the future generation of gravitational wave detectors, flat-top beams have been proposed to replace conventional Gaussian beams, so as to obtain better averaging over the Brownian motion of the test masses. Here, we present a detailed investigation of the unwanted opto-acoustic interactions in such interferometers, which can lead to the phenomenon of parametric instability. Our results show that the increased overlap of the Mesa beams with the test masses leads to approximately 3 times as many unstable modes in comparison to a similar interferometer with Gaussian beams.
Electronic noise modeling in statistical iterative reconstruction.
Xu, Jingyan; Tsui, Benjamin M W
2009-06-01
We consider electronic noise modeling in tomographic image reconstruction when the measured signal is the sum of a Gaussian distributed electronic noise component and another random variable whose log-likelihood function satisfies a certain linearity condition. Examples of such likelihood functions include the Poisson distribution and an exponential dispersion (ED) model that can approximate the signal statistics in integration mode X-ray detectors. We formulate the image reconstruction problem as a maximum-likelihood estimation problem. Using an expectation-maximization approach, we demonstrate that a reconstruction algorithm can be obtained following a simple substitution rule from the one previously derived without electronic noise considerations. To illustrate the applicability of the substitution rule, we present examples of a fully iterative reconstruction algorithm and a sinogram smoothing algorithm both in transmission CT reconstruction when the measured signal contains additive electronic noise. Our simulation studies show the potential usefulness of accurate electronic noise modeling in low-dose CT applications.
Pelli, D G; Farell, B
1999-03-01
Measuring the dependence of visual sensitivity on parameters of the visual stimulus is a mainstay of vision science. However, it is not widely appreciated that visual sensitivity is a product of two factors that are each invariant with respect to many properties of the stimulus and task. By estimating these two factors, one can isolate visual processes more easily than by using sensitivity measures alone. The underlying idea is that noise limits all forms of communication, including vision. As an empirical matter, it is often useful to measure the human observer's threshold with and without a noise background added to the display, to disentangle the observer's ability from the observer's intrinsic noise. And when we know how much noise there is, it is often useful to calculate ideal performance of the task at hand, as a benchmark for human performance. This strips away the intrinsic difficulty of the task to reveal a pure measure of human ability. Here we show how to do the factoring of sensitivity into efficiency and equivalent noise, and we document the invariances of the two factors.
Teleportation of squeezing: Optimization using non-Gaussian resources
Dell'Anno, Fabio; De Siena, Silvio; Illuminati, Fabrizio; Adesso, Gerardo
2010-12-15
We study the continuous-variable quantum teleportation of states, statistical moments of observables, and scale parameters such as squeezing. We investigate the problem both in ideal and imperfect Vaidman-Braunstein-Kimble protocol setups. We show how the teleportation fidelity is maximized and the difference between output and input variances is minimized by using suitably optimized entangled resources. Specifically, we consider the teleportation of coherent squeezed states, exploiting squeezed Bell states as entangled resources. This class of non-Gaussian states, introduced by Illuminati and co-workers [F. Dell'Anno, S. De Siena, L. Albano, and F. Illuminati, Phys. Rev. A 76, 022301 (2007); F. Dell'Anno, S. De Siena, and F. Illuminati, ibid. 81, 012333 (2010)], includes photon-added and photon-subtracted squeezed states as special cases. At variance with the case of entangled Gaussian resources, the use of entangled non-Gaussian squeezed Bell resources allows one to choose different optimization procedures that lead to inequivalent results. Performing two independent optimization procedures, one can either maximize the state teleportation fidelity, or minimize the difference between input and output quadrature variances. The two different procedures are compared depending on the degrees of displacement and squeezing of the input states and on the working conditions in ideal and nonideal setups.
Teleportation of squeezing: Optimization using non-Gaussian resources
NASA Astrophysics Data System (ADS)
Dell'Anno, Fabio; de Siena, Silvio; Adesso, Gerardo; Illuminati, Fabrizio
2010-12-01
We study the continuous-variable quantum teleportation of states, statistical moments of observables, and scale parameters such as squeezing. We investigate the problem both in ideal and imperfect Vaidman-Braunstein-Kimble protocol setups. We show how the teleportation fidelity is maximized and the difference between output and input variances is minimized by using suitably optimized entangled resources. Specifically, we consider the teleportation of coherent squeezed states, exploiting squeezed Bell states as entangled resources. This class of non-Gaussian states, introduced by Illuminati and co-workers [F. Dell’Anno, S. De Siena, L. Albano, and F. Illuminati, Phys. Rev. APLRAAN1050-294710.1103/PhysRevA.76.022301 76, 022301 (2007); F. Dell’Anno, S. De Siena, and F. Illuminati, Phys. Rev. APLRAAN1050-294710.1103/PhysRevA.81.012333 81, 012333 (2010)], includes photon-added and photon-subtracted squeezed states as special cases. At variance with the case of entangled Gaussian resources, the use of entangled non-Gaussian squeezed Bell resources allows one to choose different optimization procedures that lead to inequivalent results. Performing two independent optimization procedures, one can either maximize the state teleportation fidelity, or minimize the difference between input and output quadrature variances. The two different procedures are compared depending on the degrees of displacement and squeezing of the input states and on the working conditions in ideal and nonideal setups.
Probability distributions for one component equations with multiplicative noise
NASA Astrophysics Data System (ADS)
Deutsch, J. M.
1994-08-01
Systems described by equations involving both multiplicative and additive noise are common in nature. Examples include convection of a passive scalar field, polymers in turbulent flow, and noise in dye lasers. In this paper the one component version of this problem is studied. The steady state probability distribution is classified into two different types of behavior. One class has power law tails and the other is of the form of an exponential to a power law. The value of the power law exponent is determined analytically for models having colored gaussian noise. It is found to only depend on the power spectrum of the noise at zero frequency. When non-gaussian noise is considered it is shown that stretched exponential tails are possible. An intuitive understanding of the results is found and makes use of the Lyapunov exponents for these systems.
Okokon, Enembe Oku; Turunen, Anu W.; Ung-Lanki, Sari; Vartiainen, Anna-Kaisa; Tiittanen, Pekka; Lanki, Timo
2015-01-01
Exposure to road-traffic noise commonly engenders annoyance, the extent of which is determined by factors not fully understood. Our aim was to estimate the prevalence and determinants of road-traffic noise annoyance and noise sensitivity in the Finnish adult population, while comparing the perceptions of road-traffic noise to exhausts as environmental health problems. Using a questionnaire that yielded responses from 1112 randomly selected adult Finnish respondents, we estimated road-traffic noise- and exhausts-related perceived exposures, health-risk perceptions, and self-reported annoyance on five-point scales, while noise sensitivity estimates were based on four questions. Determinants of noise annoyance and sensitivity were investigated using multivariate binary logistic regression and linear regression models, respectively. High or extreme noise annoyance was reported by 17% of respondents. Noise sensitivity scores approximated a Gaussian distribution. Road-traffic noise and exhausts were, respectively, considered high or extreme population-health risks by 22% and 27% of respondents. Knowledge of health risks from traffic noise, OR: 2.04 (1.09–3.82) and noise sensitivity, OR: 1.07 (1.00–1.14) were positively associated with annoyance. Knowledge of health risks (p < 0.045) and positive environmental attitudes (p < 000) were associated with higher noise sensitivity. Age and sex were associated with annoyance and sensitivity only in bivariate models. A considerable proportion of Finnish adults are highly annoyed by road-traffic noise, and perceive it to be a significant health risk, almost comparable to traffic exhausts. There is no distinct noise-sensitive population subgroup. Knowledge of health risks of road-traffic noise, and attitudinal variables are associated with noise annoyance and sensitivity. PMID:26016432
Okokon, Enembe Oku; Turunen, Anu W; Ung-Lanki, Sari; Vartiainen, Anna-Kaisa; Tiittanen, Pekka; Lanki, Timo
2015-05-26
Exposure to road-traffic noise commonly engenders annoyance, the extent of which is determined by factors not fully understood. Our aim was to estimate the prevalence and determinants of road-traffic noise annoyance and noise sensitivity in the Finnish adult population, while comparing the perceptions of road-traffic noise to exhausts as environmental health problems. Using a questionnaire that yielded responses from 1112 randomly selected adult Finnish respondents, we estimated road-traffic noise- and exhausts-related perceived exposures, health-risk perceptions, and self-reported annoyance on five-point scales, while noise sensitivity estimates were based on four questions. Determinants of noise annoyance and sensitivity were investigated using multivariate binary logistic regression and linear regression models, respectively. High or extreme noise annoyance was reported by 17% of respondents. Noise sensitivity scores approximated a Gaussian distribution. Road-traffic noise and exhausts were, respectively, considered high or extreme population-health risks by 22% and 27% of respondents. Knowledge of health risks from traffic noise, OR: 2.04 (1.09-3.82) and noise sensitivity, OR: 1.07 (1.00-1.14) were positively associated with annoyance. Knowledge of health risks (p<0.045) and positive environmental attitudes (p<000) were associated with higher noise sensitivity. Age and sex were associated with annoyance and sensitivity only in bivariate models. A considerable proportion of Finnish adults are highly annoyed by road-traffic noise, and perceive it to be a significant health risk, almost comparable to traffic exhausts. There is no distinct noise-sensitive population subgroup. Knowledge of health risks of road-traffic noise, and attitudinal variables are associated with noise annoyance and sensitivity.
Okokon, Enembe Oku; Turunen, Anu W; Ung-Lanki, Sari; Vartiainen, Anna-Kaisa; Tiittanen, Pekka; Lanki, Timo
2015-06-01
Exposure to road-traffic noise commonly engenders annoyance, the extent of which is determined by factors not fully understood. Our aim was to estimate the prevalence and determinants of road-traffic noise annoyance and noise sensitivity in the Finnish adult population, while comparing the perceptions of road-traffic noise to exhausts as environmental health problems. Using a questionnaire that yielded responses from 1112 randomly selected adult Finnish respondents, we estimated road-traffic noise- and exhausts-related perceived exposures, health-risk perceptions, and self-reported annoyance on five-point scales, while noise sensitivity estimates were based on four questions. Determinants of noise annoyance and sensitivity were investigated using multivariate binary logistic regression and linear regression models, respectively. High or extreme noise annoyance was reported by 17% of respondents. Noise sensitivity scores approximated a Gaussian distribution. Road-traffic noise and exhausts were, respectively, considered high or extreme population-health risks by 22% and 27% of respondents. Knowledge of health risks from traffic noise, OR: 2.04 (1.09-3.82) and noise sensitivity, OR: 1.07 (1.00-1.14) were positively associated with annoyance. Knowledge of health risks (p<0.045) and positive environmental attitudes (p<000) were associated with higher noise sensitivity. Age and sex were associated with annoyance and sensitivity only in bivariate models. A considerable proportion of Finnish adults are highly annoyed by road-traffic noise, and perceive it to be a significant health risk, almost comparable to traffic exhausts. There is no distinct noise-sensitive population subgroup. Knowledge of health risks of road-traffic noise, and attitudinal variables are associated with noise annoyance and sensitivity. PMID:26016432
Cadman, J D; Goodman, R E
1967-12-01
Acoustical monitoring of real landslides has revealed the existence of subaudible noise activity prior to failure and has enabled prediction of the depth of the seat of sliding when conducted in boreholes beneath the surface. Recordings of noise generated in small slopes of moist sand, tilted to failure in laboratory tests, have been analyzed to determine the foci of discrete subaudible noise events. The noises emitted shortly before failure were plotted close to the true sliding surface observed after failure. The foci of earlier events lay either within the central portion of the sliding mass or in a region behind the failure surface. The head and toe zones were devoid of strong seismic activity. PMID:17734306
Thermal noise in confined fluids.
Sanghi, T; Aluru, N R
2014-11-01
In this work, we discuss a combined memory function equation (MFE) and generalized Langevin equation (GLE) approach (referred to as MFE/GLE formulation) to characterize thermal noise in confined fluids. Our study reveals that for fluids confined inside nanoscale geometries, the correlation time and the time decay of the autocorrelation function of the thermal noise are not significantly different across the confinement. We show that it is the strong cross-correlation of the mean force with the molecular velocity that gives rise to the spatial anisotropy in the velocity-autocorrelation function of the confined fluids. Further, we use the MFE/GLE formulation to extract the thermal force a fluid molecule experiences in a MD simulation. Noise extraction from MD simulation suggests that the frequency distribution of the thermal force is non-Gaussian. Also, the frequency distribution of the thermal force near the confining surface is found to be different in the direction parallel and perpendicular to the confinement. We also use the formulation to compute the noise correlation time of water confined inside a (6,6) carbon-nanotube (CNT). It is observed that inside the (6,6) CNT, in which water arranges itself in a highly concerted single-file arrangement, the correlation time of thermal noise is about an order of magnitude higher than that of bulk water.
Thermal noise in confined fluids
NASA Astrophysics Data System (ADS)
Sanghi, T.; Aluru, N. R.
2014-11-01
In this work, we discuss a combined memory function equation (MFE) and generalized Langevin equation (GLE) approach (referred to as MFE/GLE formulation) to characterize thermal noise in confined fluids. Our study reveals that for fluids confined inside nanoscale geometries, the correlation time and the time decay of the autocorrelation function of the thermal noise are not significantly different across the confinement. We show that it is the strong cross-correlation of the mean force with the molecular velocity that gives rise to the spatial anisotropy in the velocity-autocorrelation function of the confined fluids. Further, we use the MFE/GLE formulation to extract the thermal force a fluid molecule experiences in a MD simulation. Noise extraction from MD simulation suggests that the frequency distribution of the thermal force is non-Gaussian. Also, the frequency distribution of the thermal force near the confining surface is found to be different in the direction parallel and perpendicular to the confinement. We also use the formulation to compute the noise correlation time of water confined inside a (6,6) carbon-nanotube (CNT). It is observed that inside the (6,6) CNT, in which water arranges itself in a highly concerted single-file arrangement, the correlation time of thermal noise is about an order of magnitude higher than that of bulk water.
Measurement-induced disturbances and nonclassical correlations of Gaussian states
Mista, Ladislav Jr.; Tatham, Richard; Korolkova, Natalia; Girolami, Davide; Adesso, Gerardo
2011-04-15
We study quantum correlations beyond entanglement in two-mode Gaussian states of continuous-variable systems by means of the measurement-induced disturbance (MID) and its ameliorated version (AMID). In analogy with the recent studies of the Gaussian quantum discord, we define a Gaussian AMID by constraining the optimization to all bi-local Gaussian positive operator valued measurements. We solve the optimization explicitly for relevant families of states, including squeezed thermal states. Remarkably, we find that there is a finite subset of two-mode Gaussian states comprising pure states where non-Gaussian measurements such as photon counting are globally optimal for the AMID and realize a strictly smaller state disturbance compared to the best Gaussian measurements. However, for the majority of two-mode Gaussian states the unoptimized MID provides a loose overestimation of the actual content of quantum correlations, as evidenced by its comparison with Gaussian discord. This feature displays strong similarity with the case of two qubits. Upper and lower bounds for the Gaussian AMID at fixed Gaussian discord are identified. We further present a comparison between Gaussian AMID and Gaussian entanglement of formation, and classify families of two-mode states in terms of their Gaussian AMID, Gaussian discord, and Gaussian entanglement of formation. Our findings provide a further confirmation of the genuinely quantum nature of general Gaussian states, yet they reveal that non-Gaussian measurements can play a crucial role for the optimized extraction and potential exploitation of classical and nonclassical correlations in Gaussian states.
[Application of a modified method of wavelet noise removing to noisy ICP-AES spectra].
Ma, Xiao-guo; Zhang, Zhan-xia
2003-06-01
A new method for noise removal from signal by the wavelet transform was developed. Compared with analytical signal, noise has higher frequency and smaller amplitude. By the new wavelet filtering method, the high frequency components were first removed, and then the small ones in the remaining transformed vectors were discarded. The proposed approach was evaluated by the processing of simulated and experimental noisy ICP-AES spectra. Different amounts of noise were added to a Gaussian peak to obtain a series of noisy ICP spectra. The simulated noisy spectra with R (signal to noise ratio) = 6 and N (data number) = 51, and with R = 6 and N = 17 were used to illustrate the feasibility of the proposed method. The performances of noise removal by the wavelet smoothing, the wavelet denoising and the proposed technique were compared. It was found that using the new approach, the relative errors of peak height would be no more than 5% for spectra with normal sampling points and R > or = 2. Moreover, the baseline could be easily defined, which was helpful to the accurate measurement of peak height. Experimental spectra of Al and V at low concentrations were processed by the proposed method. Intense noises were efficiently removed and the spectra became smoother without underestimating the analytical signal. The distortion of V 303.310 nm line was substantially rectified. The linear correlation coefficients between the peak heights in the reconstructed spectra and the concentrations were found to be 0.9953 for Al and 0.9836 for V, respectively. PMID:12953539
Gaussian translation operator in a multilevel scheme
NASA Astrophysics Data System (ADS)
Hansen, Thorkild B.; Borries, Oscar
2015-08-01
A multilevel computation scheme for time-harmonic fields in three dimensions will be formulated with a new Gaussian translation operator that decays exponentially outside a circular cone centered on the line connecting the source and observation groups. This Gaussian translation operator is directional and diagonal with its sharpness determined by a beam parameter. When the beam parameter is set to zero, the Gaussian translation operator reduces to the standard fast multipole method translation operator. The directionality of the Gaussian translation operator makes it possible to reduce the number of plane waves required to achieve a given accuracy. The sampling rate can be determined straightforwardly to achieve any desired accuracy. The use of the computation scheme will be illustrated through a near-field scanning problem where the far-field pattern of a source is determined from near-field measurements with a known probe. Here the Gaussian translation operator improves the condition number of the matrix equation that determines the far-field pattern. The Gaussian translation operator can also be used when the probe pattern is known only in one hemisphere, as is common in practice. Also, the Gaussian translation operator will be used to solve the scattering problem of the perfectly conducting sphere.
Effects of the Noises' Statistics and Spectrum on Noise-Induced Phase Transitions
NASA Astrophysics Data System (ADS)
Deza, Roberto R.; Fuentes, Miguel A.; Wio, Horacio S.
2007-07-01
The study of the effect of the noises' statistics and spectrum on second-order, purely noise-induced phase transition (NIPT) is of wide interest: It is simplified if the noises are dynamically generated by means of stochastic differential equations driven by white noises, a well known case being that of Ornstein-Uhlenbeck noises with a self-correlation time τ whose effect on the NIPT phase diagram has been studied some time ago. Another case is when the stationary pdf is a (colored) q-Gaussian which, being a fat-tail distribution for q > 1 and a compact-support one for q < 1, allows for a controlled study of the effects of the departure from Gaussian statistics. As done with stochastic resonance and other phenomena, we exploit this tool to study—within a simple mean-field approximation—the combined effect on NIPT of the noises' statistics and spectrum. Even for relatively small τ, it is shown that whereas for fat-tail noise distributions counteract the effect of self-correlation, compact-support ones enhance it.
Asymmetric Laguerre-Gaussian beams
NASA Astrophysics Data System (ADS)
Kovalev, A. A.; Kotlyar, V. V.; Porfirev, A. P.
2016-06-01
We introduce a family of asymmetric Laguerre-Gaussian (aLG) laser beams. The beams have been derived via a complex-valued shift of conventional LG beams in the Cartesian plane. While propagating in a uniform medium, the first bright ring of the aLG beam becomes less asymmetric and the energy is redistributed toward peripheral diffraction rings. The projection of the orbital angular momentum (OAM) onto the optical axis is calculated. The OAM is shown to grow quadratically with increasing asymmetry parameter of the aLG beam, which equals the ratio of the shift to the waist radius. Conditions for the OAM becoming equal to the topological charge have been derived. For aLG beams with zero radial index, we have deduced an expression to define the intensity maximum coordinates and shown the crescent-shaped intensity pattern to rotate during propagation. Results of the experimental generation and rotation of aLG beams agree well with theoretical predictions.
Flow-induced instabilities of shells of revolution with non-zero Gaussian curvatures conveying fluid
NASA Astrophysics Data System (ADS)
Chang, Gary Han; Modarres-Sadeghi, Yahya
2016-02-01
We study flow-induced instabilities of axis-symmetric shells of revolution with an arbitrary meridian and non-zero Gaussian curvatures. We consider a fluid-structure interaction (FSI) model based on an inviscid flow model and a thin shell theory. This FSI model is solved using a method that combines the Galerkin technique with the boundary element method (BEM). The present method is capable of investigating the dynamic behavior of doubly-curved shells in contact with flow without the need for an analytical solution of the perturbed flow potential. Shells of revolution with different values of non-zero Gaussian curvatures are investigated and their behavior is compared to shells with zero Gaussian curvature. It is found that the added mass natural frequencies of shells of revolution are larger than those of conical shells with the same inlet, outlet and length. Shells of revolution, with both positive and negative Gaussian curvatures, lose their instability by buckling, however, shells with negative Gaussian curvatures buckle at modes similar to those observed in uniform and conical shells, while shells with positive Gaussian curvatures buckle with localized deformations close to the area with higher local flow velocities.
NASA Astrophysics Data System (ADS)
Callebaut, Nele; Gubser, Steven S.; Samberg, Andreas; Toldo, Chiara
2015-11-01
We study segmented strings in flat space and in AdS 3. In flat space, these well known classical motions describe strings which at any instant of time are piecewise linear. In AdS 3, the worldsheet is composed of faces each of which is a region bounded by null geodesics in an AdS 2 subspace of AdS 3. The time evolution can be described by specifying the null geodesic motion of kinks in the string at which two segments are joined. The outcome of collisions of kinks on the worldsheet can be worked out essentially using considerations of causality. We study several examples of closed segmented strings in AdS 3 and find an unexpected quasi-periodic behavior. We also work out a WKB analysis of quantum states of yo-yo strings in AdS 5 and find a logarithmic term reminiscent of the logarithmic twist of string states on the leading Regge trajectory.
NASA Astrophysics Data System (ADS)
Costa, Miguel S.; Greenspan, Lauren; Oliveira, Miguel; Penedones, João; Santos, Jorge E.
2016-06-01
We consider solutions in Einstein-Maxwell theory with a negative cosmological constant that asymptote to global AdS 4 with conformal boundary {S}2× {{{R}}}t. At the sphere at infinity we turn on a space-dependent electrostatic potential, which does not destroy the asymptotic AdS behaviour. For simplicity we focus on the case of a dipolar electrostatic potential. We find two new geometries: (i) an AdS soliton that includes the full backreaction of the electric field on the AdS geometry; (ii) a polarised neutral black hole that is deformed by the electric field, accumulating opposite charges in each hemisphere. For both geometries we study boundary data such as the charge density and the stress tensor. For the black hole we also study the horizon charge density and area, and further verify a Smarr formula. Then we consider this system at finite temperature and compute the Gibbs free energy for both AdS soliton and black hole phases. The corresponding phase diagram generalizes the Hawking-Page phase transition. The AdS soliton dominates the low temperature phase and the black hole the high temperature phase, with a critical temperature that decreases as the external electric field increases. Finally, we consider the simple case of a free charged scalar field on {S}2× {{{R}}}t with conformal coupling. For a field in the SU(N ) adjoint representation we compare the phase diagram with the above gravitational system.
Cloning of Gaussian states by linear optics
Olivares, Stefano; Paris, Matteo G. A.; Andersen, Ulrik L.
2006-06-15
We analyze in details a scheme for cloning of Gaussian states based on linear optical components and homodyne detection recently demonstrated by Andersen et al. [Phys. Rev. Lett. 94, 240503 (2005)]. The input-output fidelity is evaluated for a generic (pure or mixed) Gaussian state taking into account the effect of nonunit quantum efficiency and unbalanced mode mixing. In addition, since in most quantum information protocols the covariance matrix of the set of input states is not perfectly known, we evaluate the average cloning fidelity for classes of Gaussian states with the degree of squeezing and the number of thermal photons being only partially known.
Quark and Lepton Masses from Gaussian Landscapes
Hall, Lawrence J.; Salem, Michael P.; Watari, Taizan
2008-04-11
The flavor structure of the standard model (SM) might arise from random selection on a landscape. We propose a class of simple models, 'Gaussian landscapes', where Yukawa couplings derive from overlap integrals of Gaussian wave functions on extra-dimensions. Statistics of vacua are generated by scanning the peak positions of these zero-modes, giving probability distributions for all flavor observables. Gaussian landscapes can account for all observed flavor patterns with few free parameters. Although they give broad probability distributions, the predictions are correlated and accounting for measured parameters sharpens the distributions of future neutrino measurements.
Quantum bit commitment under Gaussian constraints
NASA Astrophysics Data System (ADS)
Mandilara, Aikaterini; Cerf, Nicolas J.
2012-06-01
Quantum bit commitment has long been known to be impossible. Nevertheless, just as in the classical case, imposing certain constraints on the power of the parties may enable the construction of asymptotically secure protocols. Here, we introduce a quantum bit commitment protocol and prove that it is asymptotically secure if cheating is restricted to Gaussian operations. This protocol exploits continuous-variable quantum optical carriers, for which such a Gaussian constraint is experimentally relevant as the high optical nonlinearity needed to effect deterministic non-Gaussian cheating is inaccessible.
Gaussian measures of entanglement versus negativities: Ordering of two-mode Gaussian states
Adesso, Gerardo; Illuminati, Fabrizio
2005-09-15
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
Sepke, Scott M; Umstadter, Donald P
2006-05-15
The exact vector integral solution for all the electromagnetic field components of a general flattened Gaussian laser mode is derived by using the angular spectrum method. This solution includes the pure and annular Gaussian modes as special cases. The integrals are of the form of Gegenbauer's finite integral and are computed analytically for each case, yielding fields satisfying the Maxwell equations exactly in the form of quickly converging Fourier-Gegenbauer series. PMID:16642134
Statistics of a neuron model driven by asymmetric colored noise.
Müller-Hansen, Finn; Droste, Felix; Lindner, Benjamin
2015-02-01
Irregular firing of neurons can be modeled as a stochastic process. Here we study the perfect integrate-and-fire neuron driven by dichotomous noise, a Markovian process that jumps between two states (i.e., possesses a non-Gaussian statistics) and exhibits nonvanishing temporal correlations (i.e., represents a colored noise). Specifically, we consider asymmetric dichotomous noise with two different transition rates. Using a first-passage-time formulation, we derive exact expressions for the probability density and the serial correlation coefficient of the interspike interval (time interval between two subsequent neural action potentials) and the power spectrum of the spike train. Furthermore, we extend the model by including additional Gaussian white noise, and we give approximations for the interspike interval (ISI) statistics in this case. Numerical simulations are used to validate the exact analytical results for pure dichotomous noise, and to test the approximations of the ISI statistics when Gaussian white noise is included. The results may help to understand how correlations and asymmetry of noise and signals in nerve cells shape neuronal firing statistics.
Statistics of a neuron model driven by asymmetric colored noise
NASA Astrophysics Data System (ADS)
Müller-Hansen, Finn; Droste, Felix; Lindner, Benjamin
2015-02-01
Irregular firing of neurons can be modeled as a stochastic process. Here we study the perfect integrate-and-fire neuron driven by dichotomous noise, a Markovian process that jumps between two states (i.e., possesses a non-Gaussian statistics) and exhibits nonvanishing temporal correlations (i.e., represents a colored noise). Specifically, we consider asymmetric dichotomous noise with two different transition rates. Using a first-passage-time formulation, we derive exact expressions for the probability density and the serial correlation coefficient of the interspike interval (time interval between two subsequent neural action potentials) and the power spectrum of the spike train. Furthermore, we extend the model by including additional Gaussian white noise, and we give approximations for the interspike interval (ISI) statistics in this case. Numerical simulations are used to validate the exact analytical results for pure dichotomous noise, and to test the approximations of the ISI statistics when Gaussian white noise is included. The results may help to understand how correlations and asymmetry of noise and signals in nerve cells shape neuronal firing statistics.
Xiao, Zhu; Havyarimana, Vincent; Li, Tong; Wang, Dong
2016-01-01
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. PMID:27187405
Xiao, Zhu; Havyarimana, Vincent; Li, Tong; Wang, Dong
2016-01-01
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. PMID:27187405
Deviation of 1/ f voltage fluctuations from scale-similar Gaussian behavior
NASA Astrophysics Data System (ADS)
Nelkin, Mark; Tremblay, A.-M. S.
1981-06-01
Recent measurements on thin metal films suggest a pulse model of resistance fluctuations in which scale similarity and power law spectra are only approximate. We show that such a pulse model is consistent with stationary Gaussian resistance fluctuations. This is to be contrasted with the phenomenological behavior, of fluctuations near phase transitions and in turbulent fluids where the fluctuations are non-Gaussian, but exhibit scale similarity of deep physical origin. We then critically examine other tests of the Gaussian behavior of the fluctuating voltage V(t) across a resistor. These include the relaxation of the conditional mean < V(t)¦V(0)= V 0>, and the spectrum of V 2( t). We consider also the question of time reversal invariance. We further ask under what conditions 1/f noise can be measured through fluctuations of the Johnson noise power with no applied voltage. We emphasize that this possibility, suggested and observed by Voss and Clarke, requires that V(t) contain a non-Gaussian component.
Impact of measurement precision and noise on superresolution image reconstruction.
Wood, Sally L; Lee, Shu-Ting; Yang, Gao; Christensen, Marc P; Rajan, Dinesh
2008-04-01
The performance of uniform and nonuniform detector arrays for application to the PANOPTES (processing arrays of Nyquist-limited observations to produce a thin electro-optic sensor) flat camera design is analyzed for measurement noise environments including quantization noise and Gaussian and Poisson processes. Image data acquired from a commercial camera with 8 bit and 14 bit output options are analyzed, and estimated noise levels are computed. Noise variances estimated from the measurement values are used in the optimal linear estimators for superresolution image reconstruction.
Mukherjee, S; Yao, W
2015-06-15
Purpose: To study different noise-reduction algorithms and to improve the image quality of low dose cone beam CT for patient positioning in radiation therapy. Methods: In low-dose cone-beam CT, the reconstructed image is contaminated with excessive quantum noise. In this study, three well-developed noise reduction algorithms namely, a) penalized weighted least square (PWLS) method, b) split-Bregman total variation (TV) method, and c) compressed sensing (CS) method were studied and applied to the images of a computer–simulated “Shepp-Logan” phantom and a physical CATPHAN phantom. Up to 20% additive Gaussian noise was added to the Shepp-Logan phantom. The CATPHAN phantom was scanned by a Varian OBI system with 100 kVp, 4 ms and 20 mA. For comparing the performance of these algorithms, peak signal-to-noise ratio (PSNR) of the denoised images was computed. Results: The algorithms were shown to have the potential in reducing the noise level for low-dose CBCT images. For Shepp-Logan phantom, an improvement of PSNR of 2 dB, 3.1 dB and 4 dB was observed using PWLS, TV and CS respectively, while for CATPHAN, the improvement was 1.2 dB, 1.8 dB and 2.1 dB, respectively. Conclusion: Penalized weighted least square, total variation and compressed sensing methods were studied and compared for reducing the noise on a simulated phantom and a physical phantom scanned by low-dose CBCT. The techniques have shown promising results for noise reduction in terms of PSNR improvement. However, reducing the noise without compromising the smoothness and resolution of the image needs more extensive research.
Noise in Neural Networks: Thresholds, Hysteresis, and Neuromodulation of Signal-To-Noise
NASA Astrophysics Data System (ADS)
Keeler, James D.; Pichler, Elgar E.; Ross, John
1989-03-01
We study a neural-network model including Gaussian noise, higher-order neuronal interactions, and neuromodulation. For a first-order network, there is a threshold in the noise level (phase transition) above which the network displays only disorganized behavior and critical slowing down near the noise threshold. The network can tolerate more noise if it has higher-order feedback interactions, which also lead to hysteresis and multistability in the network dynamics. The signal-to-noise ratio can be adjusted in a biological neural network by neuromodulators such as norepinephrine. Comparisons are made to experimental results and further investigations are suggested to test the effects of hysteresis and neuromodulation in pattern recognition and learning. We propose that norepinephrine may ``quench'' the neural patterns of activity to enhance the ability to learn details.
Optimal cloning of mixed Gaussian states
Guta, Madalin; Matsumoto, Keiji
2006-09-15
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.
Why Should We Pivot in Gaussian Elimination?
ERIC Educational Resources Information Center
Rozema, Edward
1988-01-01
The article discusses the use of computers to teacher college level mathematics. In particular, the Gaussian elimination procedure for solving a system of n linear equations in n unknowns, using a computer, is examined. (PK)
Improved Gaussian Beam-Scattering Algorithm
NASA Technical Reports Server (NTRS)
Lock, James A.
1995-01-01
The localized model of the beam-shape coefficients for Gaussian beam-scattering theory by a spherical particle provides a great simplification in the numerical implementation of the theory. We derive an alternative form for the localized coefficients that is more convenient for computer computations and that provides physical insight into the details of the scattering process. We construct a FORTRAN program for Gaussian beam scattering with the localized model and compare its computer run time on a personal computer with that of a traditional Mie scattering program and with three other published methods for computing Gaussian beam scattering. We show that the analytical form of the beam-shape coefficients makes evident the fact that the excitation rate of morphology-dependent resonances is greatly enhanced for far off-axis incidence of the Gaussian beam.
Non-Gaussianities in New Ekpyrotic Cosmology.
Buchbinder, Evgeny I; Khoury, Justin; Ovrut, Burt A
2008-05-01
The new ekpyrotic model is an alternative scenario of the early Universe which relies on a phase of slow contraction before the big bang. We calculate the 3-point and 4-point correlation functions of primordial density perturbations and find a generically large non-Gaussian signal, just below the current sensitivity level of cosmic microwave background experiments. This is in contrast with slow-roll inflation, which predicts negligible non-Gaussianity. The model is also distinguishable from alternative inflationary scenarios that can yield large non-Gaussianity, such as Dirac-Born-Infeld inflation and the simplest curvatonlike models, through the shape dependence of the correlation functions. Non-Gaussianity therefore provides a distinguishing and testable prediction of New Ekpyrotic Cosmology.
Spectral shaping for non-Gaussian source spectra in optical coherence tomography
NASA Astrophysics Data System (ADS)
Tripathi, Renu; Nassif, Nader; Nelson, J. Stuart; Park, Boris Hyle; de Boer, Johannes F.
2002-03-01
We present a digital spectral shaping technique to reduce the sidelobes (ringing) of the axial point-spread function in optical coherence tomography for non-Gaussian-shaped source spectra. The spectra of two superluminescent diodes were combined to generate a spectrum with significant modulation. Images of onion cells demonstrate the improved image quality in a turbid biological sample. A quantitative analysis of the accompanying penalty in signal-to-noise ratio is given.
Extracting features of Gaussian self-similar stochastic processes via the Bandt-Pompe approach.
Rosso, O A; Zunino, L; Pérez, D G; Figliola, A; Larrondo, H A; Garavaglia, M; Martín, M T; Plastino, A
2007-12-01
By recourse to appropriate information theory quantifiers (normalized Shannon entropy and Martín-Plastino-Rosso intensive statistical complexity measure), we revisit the characterization of Gaussian self-similar stochastic processes from a Bandt-Pompe viewpoint. We show that the ensuing approach exhibits considerable advantages with respect to other treatments. In particular, clear quantifiers gaps are found in the transition between the continuous processes and their associated noises.
Extracting features of Gaussian self-similar stochastic processes via the Bandt-Pompe approach
NASA Astrophysics Data System (ADS)
Rosso, O. A.; Zunino, L.; Pérez, D. G.; Figliola, A.; Larrondo, H. A.; Garavaglia, M.; Martín, M. T.; Plastino, A.
2007-12-01
By recourse to appropriate information theory quantifiers (normalized Shannon entropy and Martín-Plastino-Rosso intensive statistical complexity measure), we revisit the characterization of Gaussian self-similar stochastic processes from a Bandt-Pompe viewpoint. We show that the ensuing approach exhibits considerable advantages with respect to other treatments. In particular, clear quantifiers gaps are found in the transition between the continuous processes and their associated noises.
NASA Technical Reports Server (NTRS)
Mashiku, Alinda; Garrison, James L.; Carpenter, J. Russell
2012-01-01
The tracking of space objects requires frequent and accurate monitoring for collision avoidance. As even collision events with very low probability are important, accurate prediction of collisions require the representation of the full probability density function (PDF) of the random orbit state. Through representing the full PDF of the orbit state for orbit maintenance and collision avoidance, we can take advantage of the statistical information present in the heavy tailed distributions, more accurately representing the orbit states with low probability. The classical methods of orbit determination (i.e. Kalman Filter and its derivatives) provide state estimates based on only the second moments of the state and measurement errors that are captured by assuming a Gaussian distribution. Although the measurement errors can be accurately assumed to have a Gaussian distribution, errors with a non-Gaussian distribution could arise during propagation between observations. Moreover, unmodeled dynamics in the orbit model could introduce non-Gaussian errors into the process noise. A Particle Filter (PF) is proposed as a nonlinear filtering technique that is capable of propagating and estimating a more complete representation of the state distribution as an accurate approximation of a full PDF. The PF uses Monte Carlo runs to generate particles that approximate the full PDF representation. The PF is applied in the estimation and propagation of a highly eccentric orbit and the results are compared to the Extended Kalman Filter and Splitting Gaussian Mixture algorithms to demonstrate its proficiency.
Power spectrum and non-Gaussianities in anisotropic inflation
Dey, Anindya; Kovetz, Ely D.; Paban, Sonia E-mail: elykovetz@gmail.com
2014-06-01
We study the planar regime of curvature perturbations for single field inflationary models in an axially symmetric Bianchi I background. In a theory with standard scalar field action, the power spectrum for such modes has a pole as the planarity parameter goes to zero. We show that constraints from back reaction lead to a strong lower bound on the planarity parameter for high-momentum planar modes and use this bound to calculate the signal-to-noise ratio of the anisotropic power spectrum in the CMB, which in turn places an upper bound on the Hubble scale during inflation allowed in our model. We find that non-Gaussianities for these planar modes are enhanced for the flattened triangle and the squeezed triangle configurations, but show that the estimated values of the f{sub NL} parameters remain well below the experimental bounds from the CMB for generic planar modes (other, more promising signatures are also discussed). For a standard action, f{sub NL} from the squeezed configuration turns out to be larger compared to that from the flattened triangle configuration in the planar regime. However, in a theory with higher derivative operators, non-Gaussianities from the flattened triangle can become larger than the squeezed configuration in a certain limit of the planarity parameter.
Cosmic shear statistics in cosmologies with non-Gaussian initial conditions
NASA Astrophysics Data System (ADS)
Fedeli, C.; Moscardini, L.
2010-06-01
We computed the power spectrum of weak cosmic shear in models with non-Gaussian primordial density fluctuations. Cosmological initial conditions deviating from Gaussianity have recently attracted much attention in the literature, especially with respect to their effect on the formation of non-linear structures and because of the bounds that they can put on the inflationary epoch. The fully non-linear matter power spectrum was evaluated with the use of the physically motivated, semi-analytic halo model, where the mass function and linear halo bias were suitably corrected for non-Gaussian cosmologies. In agreement with previous work, we found that a level of non-Gaussianity compatible with cosmic microwave background bounds and with positive skewness produces an increase in power of the order of a few per cent at intermediate scales. We then used the matter power spectrum, together with observationally motivated background source redshift distributions in order to compute the cosmological weak-lensing power spectrum. We found that the degree of deviation from the power spectrum of the reference Gaussian model is small compared to the statistical error expected from even future weak-lensing surveys. However, summing the signal over a large range of multipoles can beat down the noise, bringing to a significant detection of non-Gaussianity at the level of few tens, when all other cosmological parameters are held fixed. Finally, we have shown that the constraints on the level of non-Gaussianity can be improved by ~ 20 per cent with the use of weak-lensing tomography.
Self-refocused slice selection by magic echo DANTE with 270° flipping Gaussian RF modulation
NASA Astrophysics Data System (ADS)
Masumoto, Hidefumi; Hashimoto, Takeyuki; Matsui, Shigeru
2010-04-01
The method of slice selection proposed for solid-state MRI by combining DANTE selective excitation with magic echo (ME) line narrowing requires a rephasing period ca. 0.6 times the DANTE excitation period. The added rephasing period results in a significant loss of sensitivity due to transverse relaxation. To solve the sensitivity problem, we make use of the self-refocusing effect of the 270° Gaussian-shaped soft pulse by introducing a 270° flipping Gaussian modulation to the ME DANTE method. This eliminates the rephasing period. The utility of the improved method is demonstrated by experiments performed on test samples of adamantane and polycarbonate.
Generating functionals and Gaussian approximations for interruptible delay reactions
NASA Astrophysics Data System (ADS)
Brett, Tobias; Galla, Tobias
2015-10-01
We develop a generating functional description of the dynamics of non-Markovian individual-based systems in which delay reactions can be terminated before completion. This generalizes previous work in which a path-integral approach was applied to dynamics in which delay reactions complete with certainty. We construct a more widely applicable theory, and from it we derive Gaussian approximations of the dynamics, valid in the limit of large, but finite, population sizes. As an application of our theory we study predator-prey models with delay dynamics due to gestation or lag periods to reach the reproductive age. In particular, we focus on the effects of delay on noise-induced cycles.
Gaussian interferometric power and Black box estimation of Unruh temperature
NASA Astrophysics Data System (ADS)
Wang, Jieci; Cao, Haixin; Jing, Jiliang
2016-10-01
We present a black box estimation paradigm of Unruh temperature in a relativistic bosonic continuous-variable setting. It is shown that the guaranteed precision for the estimation of Unruh temperature can be evaluated by the Gaussian interferometric power for a given probe state. We demonstrate that the amount of interferometric power is always beyond the entanglement type quantum correlations in a relativistic setting. It is found that due to the fact that Unruh radiation acts as a thermal bath on the probe system, it destroys available resources of the probe system and reduces the guaranteed precision of the estimation of Unruh temperature. We also find that the thermal noise induced by Unruh effect will generate interferometric power between accelerated Bob and his auxiliary partner anti-Bob, while it does not generate any correlation between inertial Alice and anti-Bob.
Gaussian-Beam Laser-Resonator Program
NASA Technical Reports Server (NTRS)
Cross, Patricia L.; Bair, Clayton H.; Barnes, Norman
1989-01-01
Gaussian Beam Laser Resonator Program models laser resonators by use of Gaussian-beam-propagation techniques. Used to determine radii of beams as functions of position in laser resonators. Algorithm used in program has three major components. First, ray-transfer matrix for laser resonator must be calculated. Next, initial parameters of beam calculated. Finally, propagation of beam through optical elements computed. Written in Microsoft FORTRAN (Version 4.01).
Application of Gaussian moment method to a gene autoregulation model of rational vector field
NASA Astrophysics Data System (ADS)
Kang, Yan-Mei; Chen, Xi
2016-07-01
We take a lambda expression autoregulation model driven by multiplicative and additive noises as example to extend the Gaussian moment method from nonlinear stochastic systems of polynomial vector field to noisy biochemical systems of rational polynomial vector field. As a direct application of the extended method, we also disclose the phenomenon of stochastic resonance. It is found that the transcription rate can inhibit the stochastic resonant effect, but the degradation rate may enhance the phenomenon. These observations should be helpful in understanding the functional role of noise in gene autoregulation.
Design criteria for noncoherent Gaussian channels with MFSK signaling and coding
NASA Technical Reports Server (NTRS)
Butman, S. A.; Levitt, B. K.; Bar-David, I.; Lyon, R. F.; Klass, M. J.
1976-01-01
This paper presents data and criteria to assess and guide the design of modems for coded noncoherent communication systems subject to practical system constraints of power, bandwidth, noise spectral density, coherence time, and number of orthogonal signals M. Three basic receiver types are analyzed for the noncoherent multifrequency-shift keying (MFSK) additive white Gaussian noise channel: hard decision, unquantized (optimum), and quantized (soft decision). Channel capacity and computational cutoff rate are computed for each type and presented as functions of the predetection signal-to-noise ratio and the number of orthogonal signals. This relates the channel constraints of power, bandwidth, coherence time, and noise power to the optimum choice of signal duration and signal number.
Eyyuboğlu, Halil T
2014-06-10
Using the random phase screen approach, we carry out a simulation analysis of the probability of error performance of Gaussian, annular Gaussian, cos Gaussian, and cosh Gaussian beams. In our scenario, these beams are intensity-modulated by the randomly generated binary symbols of an electrical message signal and then launched from the transmitter plane in equal powers. They propagate through a turbulent atmosphere modeled by a series of random phase screens. Upon arriving at the receiver plane, detection is performed in a circuitry consisting of a pin photodiode and a matched filter. The symbols detected are compared with the transmitted ones, errors are counted, and from there the probability of error is evaluated numerically. Within the range of source and propagation parameters tested, the lowest probability of error is obtained for the annular Gaussian beam. Our investigation reveals that there is hardly any difference between the aperture-averaged scintillations of the beams used, and the distinctive advantage of the annular Gaussian beam lies in the fact that the receiver aperture captures the maximum amount of power when this particular beam is launched from the transmitter plane.
Uncertainty in perception and the Hierarchical Gaussian Filter.
Mathys, Christoph D; Lomakina, Ekaterina I; Daunizeau, Jean; Iglesias, Sandra; Brodersen, Kay H; Friston, Karl J; Stephan, Klaas E
2014-01-01
In its full sense, perception rests on an agent's model of how its sensory input comes about and the inferences it draws based on this model. These inferences are necessarily uncertain. Here, we illustrate how the Hierarchical Gaussian Filter (HGF) offers a principled and generic way to deal with the several forms that uncertainty in perception takes. The HGF is a recent derivation of one-step update equations from Bayesian principles that rests on a hierarchical generative model of the environment and its (in)stability. It is computationally highly efficient, allows for online estimates of hidden states, and has found numerous applications to experimental data from human subjects. In this paper, we generalize previous descriptions of the HGF and its account of perceptual uncertainty. First, we explicitly formulate the extension of the HGF's hierarchy to any number of levels; second, we discuss how various forms of uncertainty are accommodated by the minimization of variational free energy as encoded in the update equations; third, we combine the HGF with decision models and demonstrate the inversion of this combination; finally, we report a simulation study that compared four optimization methods for inverting the HGF/decision model combination at different noise levels. These four methods (Nelder-Mead simplex algorithm, Gaussian process-based global optimization, variational Bayes and Markov chain Monte Carlo sampling) all performed well even under considerable noise, with variational Bayes offering the best combination of efficiency and informativeness of inference. Our results demonstrate that the HGF provides a principled, flexible, and efficient-but at the same time intuitive-framework for the resolution of perceptual uncertainty in behaving agents. PMID:25477800
Stochastic bifurcation in a model of love with colored noise
NASA Astrophysics Data System (ADS)
Yue, Xiaokui; Dai, Honghua; Yuan, Jianping
2015-07-01
In this paper, we wish to examine the stochastic bifurcation induced by multiplicative Gaussian colored noise in a dynamical model of love where the random factor is used to describe the complexity and unpredictability of psychological systems. First, the dynamics in deterministic love-triangle model are considered briefly including equilibrium points and their stability, chaotic behaviors and chaotic attractors. Then, the influences of Gaussian colored noise with different parameters are explored such as the phase plots, top Lyapunov exponents, stationary probability density function (PDF) and stochastic bifurcation. The stochastic P-bifurcation through a qualitative change of the stationary PDF will be observed and bifurcation diagram on parameter plane of correlation time and noise intensity is presented to find the bifurcation behaviors in detail. Finally, the top Lyapunov exponent is computed to determine the D-bifurcation when the noise intensity achieves to a critical value. By comparison, we find there is no connection between two kinds of stochastic bifurcation.
2006-01-01
Statistical image reconstruction methods based on maximum a posteriori (MAP) principle have been developed for emission tomography. The prior distribution of the unknown image plays an important role in MAP reconstruction. The most commonly used prior are Gaussian priors, whose logarithm has a quadratic form. Gaussian priors are relatively easy to analyze. It has been shown that the effect of a Gaussian prior can be approximated by linear filtering a maximum likelihood (ML) reconstruction. As a result, sharp edges in reconstructed images are not preserved. To preserve sharp transitions, non-Gaussian priors have been proposed. However, their effect on clinical tasks is less obvious. In this paper, we compare MAP reconstruction with Gaussian and non-Gaussian priors for lesion detection and region of interest quantification using computer simulation. We evaluate three representative priors: Gaussian prior, Huber prior, and Geman-McClure prior. We simulate imaging a prostate tumor using positron emission tomography (PET). The detectability of a known tumor in either a fixed background or a random background is measured using a channelized Hotelling observer. The bias-variance tradeoff curves are calculated for quantification of the total tumor activity. The results show that for the detection and quantification tasks, the Gaussian prior is as effective as non-Gaussian priors. PMID:23165056
A noise variance estimation approach for CT
NASA Astrophysics Data System (ADS)
Shen, Le; Jin, Xin; Xing, Yuxiang
2012-10-01
The Poisson-like noise model has been widely used for noise suppression and image reconstruction in low dose computed tomography. Various noise estimation and suppression approaches have been developed and studied to enhance the image quality. Among them, the recently proposed generalized Anscombe transform (GAT) has been utilized to stabilize the variance of Poisson-Gaussian noise. In this paper, we present a variance estimation approach using GAT. After the transform, the projection data is denoised conventionally with an assumption that the noise variance is uniformly equals to 1. The difference of the original and the denoised projection is treated as pure noise and the global variance σ2 can be estimated from the residual difference. Thus, the final denoising step with the estimated σ2 is performed. The proposed approach is verified on a cone-beam CT system and demonstrated to obtain a more accurate estimation of the actual parameter. We also examine FBP algorithm with the two-step noise suppression in the projection domain using the estimated noise variance. Reconstruction results with simulated and practical projection data suggest that the presented approach could be effective in practical imaging applications.
NASA Astrophysics Data System (ADS)
Yoo, Youngjin; Lee, SeongDeok; Choe, Wonhee; Kim, Chang-Yong
2007-02-01
Digital images captured from CMOS image sensors suffer Gaussian noise and impulsive noise. To efficiently reduce the noise in Image Signal Processor (ISP), we analyze noise feature for imaging pipeline of ISP where noise reduction algorithm is performed. The Gaussian noise reduction and impulsive noise reduction method are proposed for proper ISP implementation in Bayer domain. The proposed method takes advantage of the analyzed noise feature to calculate noise reduction filter coefficients. Thus, noise is adaptively reduced according to the scene environment. Since noise is amplified and characteristic of noise varies while the image sensor signal undergoes several image processing steps, it is better to remove noise in earlier stage on imaging pipeline of ISP. Thus, noise reduction is carried out in Bayer domain on imaging pipeline of ISP. The method is tested on imaging pipeline of ISP and images captured from Samsung 2M CMOS image sensor test module. The experimental results show that the proposed method removes noise while effectively preserves edges.
Propulsion system noise reduction
NASA Technical Reports Server (NTRS)
Feiler, C. E.; Heidelberg, L. J.; Karchmer, A. M.; Lansing, D. L.; Miller, B. A.; Rice, E. J.
1975-01-01
The progress in propulsion system noise reduction is reviewed. The noise technology areas discussed include: fan noise; advances in suppression including conventional acoustic treatment, high Mach number inlets, and wing shielding; engine core noise; flap noise from both under-the-wing and over-the-wing powered-lift systems; supersonic jet noise suppression; and the NASA program in noise prediction.
Stochastic bifurcations in a bistable Duffing-Van der Pol oscillator with colored noise.
Xu, Yong; Gu, Rencai; Zhang, Huiqing; Xu, Wei; Duan, Jinqiao
2011-05-01
This paper aims to investigate Gaussian colored-noise-induced stochastic bifurcations and the dynamical influence of correlation time and noise intensity in a bistable Duffing-Van der Pol oscillator. By using the stochastic averaging method, theoretically, one can obtain the stationary probability density function of amplitude for the Duffing-Van der Pol oscillator and can reveal interesting dynamics under the influence of Gaussian colored noise. Stochastic bifurcations are discussed through a qualitative change of the stationary probability distribution, which indicates that system parameters, noise intensity, and noise correlation time, respectively, can be treated as bifurcation parameters. They also imply that the effects of multiplicative noise are rather different from that of additive noise. The results of direct numerical simulation verify the effectiveness of the theoretical analysis. Moreover, the largest Lyapunov exponent calculations indicate that P and D bifurcations have no direct connection. PMID:21728638
Stochastic bifurcations in a bistable Duffing-Van der Pol oscillator with colored noise.
Xu, Yong; Gu, Rencai; Zhang, Huiqing; Xu, Wei; Duan, Jinqiao
2011-05-01
This paper aims to investigate Gaussian colored-noise-induced stochastic bifurcations and the dynamical influence of correlation time and noise intensity in a bistable Duffing-Van der Pol oscillator. By using the stochastic averaging method, theoretically, one can obtain the stationary probability density function of amplitude for the Duffing-Van der Pol oscillator and can reveal interesting dynamics under the influence of Gaussian colored noise. Stochastic bifurcations are discussed through a qualitative change of the stationary probability distribution, which indicates that system parameters, noise intensity, and noise correlation time, respectively, can be treated as bifurcation parameters. They also imply that the effects of multiplicative noise are rather different from that of additive noise. The results of direct numerical simulation verify the effectiveness of the theoretical analysis. Moreover, the largest Lyapunov exponent calculations indicate that P and D bifurcations have no direct connection.
Gaussian vs non-Gaussian turbulence: impact on wind turbine loads
NASA Astrophysics Data System (ADS)
Berg, J.; Mann, J.; Natarajan, A.; Patton, E. G.
2014-12-01
In wind energy applications the turbulent velocity field of the Atmospheric Boundary Layer (ABL) is often characterised by Gaussian probability density functions. When estimating the dynamical loads on wind turbines this has been the rule more than anything else. From numerous studies in the laboratory, in Direct Numerical Simulations, and from in-situ measurements of the ABL we know, however, that turbulence is not purely Gaussian: the smallest and fastest scales often exhibit extreme behaviour characterised by strong non-Gaussian statistics. In this contribution we want to investigate whether these non-Gaussian effects are important when determining wind turbine loads, and hence of utmost importance to the design criteria and lifetime of a wind turbine. We devise a method based on Principal Orthogonal Decomposition where non-Gaussian velocity fields generated by high-resolution pseudo-spectral Large-Eddy Simulation (LES) of the ABL are transformed so that they maintain the exact same second-order statistics including variations of the statistics with height, but are otherwise Gaussian. In that way we can investigate in isolation the question whether it is important for wind turbine loads to include non-Gaussian properties of atmospheric turbulence. As an illustration the Figure show both a non-Gaussian velocity field (left) from our LES, and its transformed Gaussian Counterpart (right). Whereas the horizontal velocity components (top) look close to identical, the vertical components (bottom) are not: the non-Gaussian case is much more fluid-like (like in a sketch by Michelangelo). The question is then: Does the wind turbine see this? Using the load simulation software HAWC2 with both the non-Gaussian and newly constructed Gaussian fields, respectively, we show that the Fatigue loads and most of the Extreme loads are unaltered when using non-Gaussian velocity fields. The turbine thus acts like a low-pass filter which average out the non-Gaussian behaviour on time
Planck 2015 results. XVII. Constraints on primordial non-Gaussianity
NASA Astrophysics Data System (ADS)
Planck Collaboration; Ade, P. A. R.; Aghanim, N.; Arnaud, M.; Arroja, F.; Ashdown, M.; Aumont, J.; Baccigalupi, C.; Ballardini, M.; Banday, A. J.; Barreiro, R. B.; Bartolo, N.; Basak, S.; Battaner, E.; Benabed, K.; Benoît, A.; Benoit-Lévy, A.; Bernard, J.-P.; Bersanelli, M.; Bielewicz, P.; Bock, J. J.; Bonaldi, A.; Bonavera, L.; Bond, J. R.; Borrill, J.; Bouchet, F. R.; Boulanger, F.; Bucher, M.; Burigana, C.; Butler, R. C.; Calabrese, E.; Cardoso, J.-F.; Catalano, A.; Challinor, A.; Chamballu, A.; Chiang, H. C.; Christensen, P. R.; Church, S.; Clements, D. L.; Colombi, S.; Colombo, L. P. L.; Combet, C.; 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.; Désert, F.-X.; Diego, J. M.; Dole, H.; Donzelli, S.; Doré, O.; Douspis, M.; Ducout, A.; Dupac, X.; Efstathiou, G.; Elsner, F.; Enßlin, T. A.; Eriksen, H. K.; Fergusson, J.; Finelli, F.; Forni, O.; Frailis, M.; Fraisse, A. A.; Franceschi, E.; Frejsel, A.; Galeotta, S.; Galli, S.; Ganga, K.; Gauthier, C.; Ghosh, T.; Giard, M.; Giraud-Héraud, Y.; Gjerløw, E.; González-Nuevo, J.; Górski, K. M.; Gratton, S.; Gregorio, A.; Gruppuso, A.; Gudmundsson, J. E.; Hamann, J.; Hansen, F. K.; Hanson, D.; Harrison, D. L.; Heavens, A.; Helou, G.; Henrot-Versillé, S.; Hernández-Monteagudo, C.; Herranz, D.; Hildebrandt, S. R.; Hivon, E.; Hobson, M.; Holmes, W. A.; Hornstrup, A.; Hovest, W.; Huang, Z.; Huffenberger, K. M.; Hurier, G.; Jaffe, A. H.; Jaffe, T. R.; Jones, W. C.; Juvela, M.; Keihänen, E.; Keskitalo, R.; Kim, J.; Kisner, T. S.; Knoche, J.; Kunz, M.; Kurki-Suonio, H.; Lacasa, F.; Lagache, G.; Lähteenmäki, A.; Lamarre, J.-M.; Lasenby, A.; Lattanzi, M.; Lawrence, C. R.; Leonardi, R.; Lesgourgues, J.; Levrier, F.; Lewis, A.; Liguori, M.; Lilje, P. B.; Linden-Vørnle, M.; López-Caniego, M.; Lubin, P. M.; Macías-Pérez, J. F.; Maggio, G.; Maino, D.; Mandolesi, N.; Mangilli, A.; Marinucci, D.; Maris, M.; Martin, P. G.; Martínez-González, E.; Masi, S.; Matarrese, S.; McGehee, P.; Meinhold, P. R.; Melchiorri, A.; Mendes, L.; Mennella, A.; Migliaccio, M.; Mitra, S.; Miville-Deschênes, M.-A.; Moneti, A.; Montier, L.; Morgante, G.; Mortlock, D.; Moss, A.; Münchmeyer, M.; Munshi, D.; Murphy, J. A.; Naselsky, P.; Nati, F.; Natoli, P.; Netterfield, C. B.; Nørgaard-Nielsen, H. U.; Noviello, F.; Novikov, D.; Novikov, I.; Oxborrow, C. A.; Paci, F.; Pagano, L.; Pajot, F.; Paoletti, D.; Pasian, F.; Patanchon, G.; Peiris, H. V.; Perdereau, O.; Perotto, L.; Perrotta, F.; Pettorino, V.; Piacentini, F.; Piat, M.; Pierpaoli, E.; Pietrobon, D.; Plaszczynski, S.; Pointecouteau, E.; Polenta, G.; Popa, L.; Pratt, G. W.; Prézeau, G.; Prunet, S.; Puget, J.-L.; Rachen, J. P.; Racine, B.; Rebolo, R.; Reinecke, M.; Remazeilles, M.; Renault, C.; Renzi, A.; Ristorcelli, I.; Rocha, G.; Rosset, C.; Rossetti, M.; Roudier, G.; Rubiño-Martín, J. A.; Rusholme, B.; Sandri, M.; Santos, D.; Savelainen, M.; Savini, G.; Scott, D.; Seiffert, M. D.; Shellard, E. P. S.; Shiraishi, M.; Smith, K.; Spencer, L. D.; Stolyarov, V.; Stompor, R.; Sudiwala, R.; Sunyaev, R.; Sutter, P.; Sutton, D.; Suur-Uski, A.-S.; Sygnet, J.-F.; Tauber, J. A.; Terenzi, L.; Toffolatti, L.; Tomasi, M.; Tristram, M.; Troja, A.; Tucci, M.; Tuovinen, J.; Valenziano, L.; Valiviita, J.; Van Tent, B.; Vielva, P.; Villa, F.; Wade, L. A.; Wandelt, B. D.; Wehus, I. K.; Yvon, D.; Zacchei, A.; Zonca, A.
2016-09-01
The Planck full mission cosmic microwave background (CMB) temperature and E-mode polarization maps are analysed to obtain constraints on primordial non-Gaussianity (NG). Using three classes of 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 from temperature alone ƒlocalNL = 2.5 ± 5.7, ƒequilNL= -16 ± 70, , and ƒorthoNL = -34 ± 32 (68% CL, statistical). Combining temperature and polarization data we obtain ƒlocalNL = 0.8 ± 5.0, ƒequilNL= -4 ± 43, and ƒorthoNL = -26 ± 21 (68% CL, statistical). 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 consistent with estimators based on measuring the Minkowski functionals of the CMB. The effect of time-domain de-glitching systematics on the bispectrum is negligible. In spite of these test outcomes we conservatively label the results including polarization data as preliminary, owing to a known mismatch of the noise model in simulations and the data. Beyond estimates of individual shape amplitudes, we present model-independent, three-dimensional reconstructions of the Planck CMB bispectrum and derive constraints on early universe scenarios that generate primordial NG, including general single-field models of inflation, axion inflation, initial state modifications, models producing parity-violating tensor bispectra, and directionally dependent vector models. We present a wide survey of scale-dependent feature and resonance models, accounting for the "look elsewhere" effect in estimating the statistical significance of features. We also look for isocurvature NG, and find no signal, but we obtain constraints that improve significantly with the inclusion of polarization. The primordial
An adaptive segment method for smoothing lidar signal based on noise estimation
NASA Astrophysics Data System (ADS)
Wang, Yuzhao; Luo, Pingping
2014-10-01
An adaptive segmentation smoothing method (ASSM) is introduced in the paper to smooth the signal and suppress the noise. In the ASSM, the noise is defined as the 3σ of the background signal. An integer number N is defined for finding the changing positions in the signal curve. If the difference of adjacent two points is greater than 3Nσ, the position is recorded as an end point of the smoothing segment. All the end points detected as above are recorded and the curves between them will be smoothed separately. In the traditional method, the end points of the smoothing windows in the signals are fixed. The ASSM creates changing end points in different signals and the smoothing windows could be set adaptively. The windows are always set as the half of the segmentations and then the average smoothing method will be applied in the segmentations. The Iterative process is required for reducing the end-point aberration effect in the average smoothing method and two or three times are enough. In ASSM, the signals are smoothed in the spacial area nor frequent area, that means the frequent disturbance will be avoided. A lidar echo was simulated in the experimental work. The echo was supposed to be created by a space-born lidar (e.g. CALIOP). And white Gaussian noise was added to the echo to act as the random noise resulted from environment and the detector. The novel method, ASSM, was applied to the noisy echo to filter the noise. In the test, N was set to 3 and the Iteration time is two. The results show that, the signal could be smoothed adaptively by the ASSM, but the N and the Iteration time might be optimized when the ASSM is applied in a different lidar.
From particle counting to Gaussian tomography
NASA Astrophysics Data System (ADS)
Parthasarathy, K. R.; Sengupta, Ritabrata
2015-12-01
The momentum and position observables in an n-mode boson Fock space Γ(ℂn) have the whole real line ℝ as their spectrum. But the total number operator N has a discrete spectrum ℤ+ = {0, 1, 2,…}. An n-mode Gaussian state in Γ(ℂn) is completely determined by the mean values of momentum and position observables and their covariance matrix which together constitute a family of n(2n + 3) real parameters. Starting with N and its unitary conjugates by the Weyl displacement operators and operators from a representation of the symplectic group Sp(2n) in Γ(ℂn), we construct n(2n + 3) observables with spectrum ℤ+ but whose expectation values in a Gaussian state determine all its mean and covariance parameters. Thus measurements of discrete-valued observables enable the tomography of the underlying Gaussian state and it can be done by using five one-mode and four two-mode Gaussian symplectic gates in single and pair mode wires of Γ(ℂn) = Γ(ℂ)⊗n. Thus the tomography protocol admits a simple description in a language similar to circuits in quantum computation theory. Such a Gaussian tomography applied to outputs of a Gaussian channel with coherent input states permit a tomography of the channel parameters. However, in our procedure the number of counting measurements exceeds the number of channel parameters slightly. Presently, it is not clear whether a more efficient method exists for reducing this tomographic complexity. As a byproduct of our approach an elementary derivation of the probability generating function of N in a Gaussian state is given. In many cases the distribution turns out to be infinitely divisible and its underlying Lévy measure can be obtained. However, we are unable to derive the exact distribution in all cases. Whether this property of infinite divisibility holds in general is left as an open problem.
NASA Astrophysics Data System (ADS)
Morales, Jose F.; Samtleben, Henning
2003-06-01
We review recent work on the holographic duals of type II and heterotic matrix string theories described by warped AdS3 supergravities. In particular, we compute the spectra of Kaluza-Klein primaries for type I, II supergravities on warped AdS3 × S7 and match them with the primary operators in the dual two-dimensional gauge theories. The presence of non-trivial warp factors and dilaton profiles requires a modification of the familiar dictionary between masses and 'scaling' dimensions of fields and operators. We present these modifications for the general case of domain wall/QFT correspondences between supergravities on warped AdSd+1 × Sq geometries and super Yang-Mills theories with 16 supercharges.
Community noise sources and noise control issues
NASA Technical Reports Server (NTRS)
Nihart, Gene L.
1992-01-01
The topics covered include the following: community noise sources and noise control issues; noise components for turbine bypass turbojet engine (TBE) turbojet; engine cycle selection and noise; nozzle development schedule; NACA nozzle design; NACA nozzle test results; nearly fully mixed (NFM) nozzle design; noise versus aspiration rate; peak noise test results; nozzle test in the Low Speed Aeroacoustic Facility (LSAF); and Schlieren pictures of NACA nozzle.
Community noise sources and noise control issues
NASA Astrophysics Data System (ADS)
Nihart, Gene L.
1992-04-01
The topics covered include the following: community noise sources and noise control issues; noise components for turbine bypass turbojet engine (TBE) turbojet; engine cycle selection and noise; nozzle development schedule; NACA nozzle design; NACA nozzle test results; nearly fully mixed (NFM) nozzle design; noise versus aspiration rate; peak noise test results; nozzle test in the Low Speed Aeroacoustic Facility (LSAF); and Schlieren pictures of NACA nozzle.
Mechanisms of particle clustering in Gaussian and non-Gaussian synthetic turbulence.
Nilsen, Christopher; Andersson, Helge I
2014-10-01
We use synthetic turbulence simulations to study how inertial particles cluster in a turbulent flow, for a wide range of Stokes numbers. Two different types of synthetic turbulence are used: one Gaussian, where the time evolution of the velocity field is a simple phase shift, and one non-Gaussian, where convection is used to evolve the velocity field in time. In both flow types we observe significant particle clustering over a wide range of scales and Stokes numbers. The clustering found at low Stokes numbers can be attributed to the vortex centrifuge effect, where heavy particles are expelled from regions dominated by vorticity. This mechanism is much more effective in the non-Gaussian turbulence, because local flow structures are convected with the particles. The preferential sampling of regions with low vorticity is almost negligible in the Gaussian turbulence. At higher Stokes numbers, caustics are formed in a very similar manner in both Gaussian and non-Gaussian synthetic turbulence. In non-Gaussian turbulence, heavy particles cluster in regions of low fluid kinetic energy, while the opposite is true in Gaussian turbulence. Our results show that synthetic simulations cannot correctly predict how the particle clustering correlates with local fluid flow properties, without including convection.
Reducing Noise by Repetition: Introduction to Signal Averaging
ERIC Educational Resources Information Center
Hassan, Umer; Anwar, Muhammad Sabieh
2010-01-01
This paper describes theory and experiments, taken from biophysics and physiological measurements, to illustrate the technique of signal averaging. In the process, students are introduced to the basic concepts of signal processing, such as digital filtering, Fourier transformation, baseline correction, pink and Gaussian noise, and the cross- and…
Iterative Gaussianization: from ICA to random rotations.
Laparra, Valero; Camps-Valls, Gustavo; Malo, Jesús
2011-04-01
Most signal processing problems involve the challenging task of multidimensional probability density function (PDF) estimation. In this paper, we propose a solution to this problem by using a family of rotation-based iterative Gaussianization (RBIG) transforms. The general framework consists of the sequential application of a univariate marginal Gaussianization transform followed by an orthonormal transform. The proposed procedure looks for differentiable transforms to a known PDF so that the unknown PDF can be estimated at any point of the original domain. In particular, we aim at a zero-mean unit-covariance Gaussian for convenience. RBIG is formally similar to classical iterative projection pursuit algorithms. However, we show that, unlike in PP methods, the particular class of rotations used has no special qualitative relevance in this context, since looking for interestingness is not a critical issue for PDF estimation. The key difference is that our approach focuses on the univariate part (marginal Gaussianization) of the problem rather than on the multivariate part (rotation). This difference implies that one may select the most convenient rotation suited to each practical application. The differentiability, invertibility, and convergence of RBIG are theoretically and experimentally analyzed. Relation to other methods, such as radial Gaussianization, one-class support vector domain description, and deep neural networks is also pointed out. The practical performance of RBIG is successfully illustrated in a number of multidimensional problems such as image synthesis, classification, denoising, and multi-information estimation. PMID:21349790
Propagation properties of cylindrical sinc Gaussian beam
NASA Astrophysics Data System (ADS)
Eyyuboğlu, Halil T.; Bayraktar, Mert
2016-09-01
We investigate the propagation properties of cylindrical sinc Gaussian beam in turbulent atmosphere. Since an analytic solution is hardly derivable, the study is carried out with the aid of random phase screens. Evolutions of the beam intensity profile, beam size and kurtosis parameter are analysed. It is found that on the source plane, cylindrical sinc Gaussian beam has a dark hollow appearance, where the side lobes also start to emerge with increase in width parameter and Gaussian source size. During propagation, beams with small width and Gaussian source size exhibit off-axis behaviour, losing the dark hollow shape, accumulating the intensity asymmetrically on one side, whereas those with large width and Gaussian source size retain dark hollow appearance even at long propagation distances. It is seen that the beams with large widths expand more in beam size than the ones with small widths. The structure constant values chosen do not seem to alter this situation. The kurtosis parameters of the beams having small widths are seen to be larger than the ones with the small widths. Again the choice of the structure constant does not change this trend.
Hydraulic Conductivity Fields: Gaussian or Not?
Meerschaert, Mark M.; Dogan, Mine; Van Dam, Remke L.; Hyndman, David W.; Benson, David A.
2013-01-01
Hydraulic conductivity (K) fields are used to parameterize groundwater flow and transport models. Numerical simulations require a detailed representation of the K field, synthesized to interpolate between available data. Several recent studies introduced high resolution K data (HRK) at the Macro Dispersion Experiment (MADE) site, and used ground-penetrating radar (GPR) to delineate the main structural features of the aquifer. This paper describes a statistical analysis of these data, and the implications for K field modeling in alluvial aquifers. Two striking observations have emerged from this analysis. The first is that a simple fractional difference filter can have a profound effect on data histograms, organizing non-Gaussian ln K data into a coherent distribution. The second is that using GPR facies allows us to reproduce the significantly non-Gaussian shape seen in real HRK data profiles, using a simulated Gaussian ln K field in each facies. This illuminates a current controversy in the literature, between those who favor Gaussian ln K models, and those who observe non-Gaussian ln K fields. Both camps are correct, but at different scales. PMID:24415806
Graphical calculus for Gaussian pure states
Menicucci, Nicolas C.; Flammia, Steven T.; Loock, Peter van
2011-04-15
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.
Adaptive alpha-trimmed mean filters under deviations from assumed noise model.
Oten, Remzi; de Figueiredo, Rui J P
2004-05-01
Alpha-trimmed mean filters are widely used for the restoration of signals and images corrupted by additive non-Gaussian noise. They are especially preferred if the underlying noise deviates from Gaussian with the impulsive noise components. The key design issue of these filters is to select its only parameter, alpha, optimally for a given noise type. In image restoration, adaptive filters utilize the flexibility of selecting alpha according to some local noise statistics. In the present paper, we first review the existing adaptive alpha-trimmed mean filter schemes. We then analyze the performance of these filters when the underlying noise distribution deviates from the Gaussian and does not satisfy the assumptions such as symmetry. Specifically, the clipping effect and the mixed noise cases are analyzed. We also present a new adaptive alpha-trimmed filter implementation that detects the nonsymmetry points locally and applies alpha-trimmed mean filter that trims out the outlier pixels such as edges or impulsive noise according to this local decision. Comparisons of the speed and filtering performances under deviations from symmetry and Gaussian assumptions show that the proposed filter is a very good alternative to the existing schemes. PMID:15376595
Realistic continuous-variable quantum teleportation with non-Gaussian resources
Dell'Anno, F.; De Siena, S.; Illuminati, F.
2010-01-15
We present a comprehensive investigation of nonideal continuous-variable quantum teleportation implemented with entangled non-Gaussian resources. We discuss in a unified framework the main decoherence mechanisms, including imperfect Bell measurements and propagation of optical fields in lossy fibers, applying the formalism of the characteristic function. By exploiting appropriate displacement strategies, we compute analytically the success probability of teleportation for input coherent states and two classes of non-Gaussian entangled resources: two-mode squeezed Bell-like states (that include as particular cases photon-added and photon-subtracted de-Gaussified states), and two-mode squeezed catlike states. We discuss the optimization procedure on the free parameters of the non-Gaussian resources at fixed values of the squeezing and of the experimental quantities determining the inefficiencies of the nonideal protocol. It is found that non-Gaussian resources enhance significantly the efficiency of teleportation and are more robust against decoherence than the corresponding Gaussian ones. Partial information on the alphabet of input states allows further significant improvement in the performance of the nonideal teleportation protocol.
Control of Environmental Noise
ERIC Educational Resources Information Center
Jensen, Paul
1973-01-01
Discusses the physical properties, sources, physiological effects, and legislation pertaining to noise, especially noise characteristics in the community. Indicates that noise reduction steps can be taken more intelligently after determination of the true noise sources and paths. (CC)
Laplace Approximation for Divisive Gaussian Processes for Nonstationary Regression.
Muñoz-González, Luis; Lázaro-Gredilla, Miguel; Figueiras-Vidal, Aníbal R
2016-03-01
The standard Gaussian Process regression (GP) is usually formulated under stationary hypotheses: The noise power is considered constant throughout the input space and the covariance of the prior distribution is typically modeled as depending only on the difference between input samples. These assumptions can be too restrictive and unrealistic for many real-world problems. Although nonstationarity can be achieved using specific covariance functions, they require a prior knowledge of the kind of nonstationarity, not available for most applications. In this paper we propose to use the Laplace approximation to make inference in a divisive GP model to perform nonstationary regression, including heteroscedastic noise cases. The log-concavity of the likelihood ensures a unimodal posterior and makes that the Laplace approximation converges to a unique maximum. The characteristics of the likelihood also allow to obtain accurate posterior approximations when compared to the Expectation Propagation (EP) approximations and the asymptotically exact posterior provided by a Markov Chain Monte Carlo implementation with Elliptical Slice Sampling (ESS), but at a reduced computational load with respect to both, EP and ESS.
Computed tomography perfusion imaging denoising using Gaussian process regression
NASA Astrophysics Data System (ADS)
Zhu, Fan; Carpenter, Trevor; Rodriguez Gonzalez, David; Atkinson, Malcolm; Wardlaw, Joanna
2012-06-01
Brain perfusion weighted images acquired using dynamic contrast studies have an important clinical role in acute stroke diagnosis and treatment decisions. However, computed tomography (CT) images suffer from low contrast-to-noise ratios (CNR) as a consequence of the limitation of the exposure to radiation of the patient. As a consequence, the developments of methods for improving the CNR are valuable. The majority of existing approaches for denoising CT images are optimized for 3D (spatial) information, including spatial decimation (spatially weighted mean filters) and techniques based on wavelet and curvelet transforms. However, perfusion imaging data is 4D as it also contains temporal information. Our approach using Gaussian process regression (GPR), which takes advantage of the temporal information, to reduce the noise level. Over the entire image, GPR gains a 99% CNR improvement over the raw images and also improves the quality of haemodynamic maps allowing a better identification of edges and detailed information. At the level of individual voxel, GPR provides a stable baseline, helps us to identify key parameters from tissue time-concentration curves and reduces the oscillations in the curve. GPR is superior to the comparable techniques used in this study.
Index Distribution of Gaussian Random Matrices
Majumdar, Satya N.; Nadal, Celine; Scardicchio, Antonello; Vivo, Pierpaolo
2009-11-27
We compute analytically, for large N, the probability distribution of the number of positive eigenvalues (the index N{sub +}) of a random NxN matrix belonging to Gaussian orthogonal (beta=1), unitary (beta=2) or symplectic (beta=4) ensembles. The distribution of the fraction of positive eigenvalues c=N{sub +}/N scales, for large N, as P(c,N){approx_equal}exp[-betaN{sup 2}PHI(c)] where the rate function PHI(c), symmetric around c=1/2 and universal (independent of beta), is calculated exactly. The distribution has non-Gaussian tails, but even near its peak at c=1/2 it is not strictly Gaussian due to an unusual logarithmic singularity in the rate function.
CMB non-gaussianity from vector fields
Peloso, Marco
2014-01-01
The Planck satellite has recently measured the CMB temperature anisotropies with unprecedented accuracy, and it has provided strong bounds on primordial non-gaussianity. Such bounds constrain models of inflation, and mechanisms that produce the primordial perturbations. We discuss the non-gaussian signatures from the interactions of the inflation φ with spin-1 fields. We study the two different cases in which the inflaton is (i) a pseudo-scalar field with a (φ)/(fa) F·F interaction with a vector field, and (ii) a scalar field with a f (φ)F² interaction. In the first case we obtain the strong limit f{sub a} ≥ 10¹⁶GeV on the decay constant. In the second case, specific choices of the function f (φ) can lead to a non-gaussianity with a characteristic shape not encountered in standard models of scalar field inflation, and which has also been constrained by Planck.
Majorization preservation of Gaussian bosonic channels
NASA Astrophysics Data System (ADS)
Jabbour, Michael G.; García-Patrón, Raúl; Cerf, Nicolas J.
2016-07-01
It is shown that phase-insensitive Gaussian bosonic channels are majorization-preserving over the set of passive states of the harmonic oscillator. This means that comparable passive states under majorization are transformed into equally comparable passive states by any phase-insensitive Gaussian bosonic channel. Our proof relies on a new preorder relation called Fock-majorization, which coincides with regular majorization for passive states but also induces another order relation in terms of mean boson number, thereby connecting the concepts of energy and disorder of a quantum state. The consequences of majorization preservation are discussed in the context of the broadcast communication capacity of Gaussian bosonic channels. Because most of our results are independent of the specific nature of the system under investigation, they could be generalized to other quantum systems and Hamiltonians, providing a new tool that may prove useful in quantum information theory and especially quantum thermodynamics.
Gaussian entanglement in the turbulent atmosphere
NASA Astrophysics Data System (ADS)
Bohmann, M.; Semenov, A. A.; Sperling, J.; Vogel, W.
2016-07-01
We provide a rigorous treatment of the entanglement properties of two-mode Gaussian states in atmospheric channels by deriving and analyzing the input-output relations for the corresponding entanglement test. A key feature of such turbulent channels is a nontrivial dependence of the transmitted continuous-variable entanglement on coherent displacements of the quantum state of the input field. Remarkably, this allows one to optimize the entanglement certification by modifying local coherent amplitudes using a finite, but optimal amount of squeezing. In addition, we propose a protocol which, in principle, renders it possible to transfer the Gaussian entanglement through any turbulent channel over arbitrary distances. Therefore, our approach provides the theoretical foundation for advanced applications of Gaussian entanglement in free-space quantum communication.
Analysis and removing noise from speech using wavelet transform
NASA Astrophysics Data System (ADS)
Tomala, Karel; Voznak, Miroslav; Partila, Pavol; Rezac, Filip; Safarik, Jakub
2013-05-01
The paper discusses the use of Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT) wavelet in removing noise from voice samples and evaluation of its impact on speech quality. One significant part of Quality of Service (QoS) in communication technology is the speech quality assessment. However, this part is seriously overlooked as telecommunication providers often focus on increasing network capacity, expansion of services offered and their enforcement in the market. Among the fundamental factors affecting the transmission properties of the communication chain is noise, either at the transmitter or the receiver side. A wavelet transform (WT) is a modern tool for signal processing. One of the most significant areas in which wavelet transforms are used is applications designed to suppress noise in signals. To remove noise from the voice sample in our experiment, we used the reference segment of the voice which was distorted by Gaussian white noise. An evaluation of the impact on speech quality was carried out by an intrusive objective algorithm Perceptual Evaluation of Speech Quality (PESQ). DWT and SWT transformation was applied to voice samples that were devalued by Gaussian white noise. Afterwards, we determined the effectiveness of DWT and SWT by means of objective algorithm PESQ. The decisive criterion for determining the quality of a voice sample once the noise had been removed was Mean Opinion Score (MOS) which we obtained in PESQ. The contribution of this work lies in the evaluation of efficiency of wavelet transformation to suppress noise in voice samples.
1/f noise outperforms white noise in sensitizing baroreflex function in the human brain.
Soma, Rika; Nozaki, Daichi; Kwak, Shin; Yamamoto, Yoshiharu
2003-08-15
We show that externally added 1/f noise more effectively sensitizes the baroreflex centers in the human brain than white noise. We examined the compensatory heart rate response to a weak periodic signal introduced via venous blood pressure receptors while adding 1/f or white noise with the same variance to the brain stem through bilateral cutaneous stimulation of the vestibular afferents. In both cases, this noisy galvanic vestibular stimulation optimized covariance between the weak input signals and the heart rate responses. However, the optimal level with 1/f noise was significantly lower than with white noise, suggesting a functional benefit of 1/f noise for neuronal information transfer in the brain. PMID:12935054
Chen, Yunjie; Zhan, Tianming; Zhang, Ji
2016-01-01
We propose a novel segmentation method based on regional and nonlocal information to overcome the impact of image intensity inhomogeneities and noise in human brain magnetic resonance images. With the consideration of the spatial distribution of different tissues in brain images, our method does not need preestimation or precorrection procedures for intensity inhomogeneities and noise. A nonlocal information based Gaussian mixture model (NGMM) is proposed to reduce the effect of noise. To reduce the effect of intensity inhomogeneity, the multigrid nonlocal Gaussian mixture model (MNGMM) is proposed to segment brain MR images in each nonoverlapping multigrid generated by using a new multigrid generation method. Therefore the proposed model can simultaneously overcome the impact of noise and intensity inhomogeneity and automatically classify 2D and 3D MR data into tissues of white matter, gray matter, and cerebral spinal fluid. To maintain the statistical reliability and spatial continuity of the segmentation, a fusion strategy is adopted to integrate the clustering results from different grid. The experiments on synthetic and clinical brain MR images demonstrate the superior performance of the proposed model comparing with several state-of-the-art algorithms. PMID:27648448
Chen, Yunjie; Zhan, Tianming; Zhang, Ji; Wang, Hongyuan
2016-01-01
We propose a novel segmentation method based on regional and nonlocal information to overcome the impact of image intensity inhomogeneities and noise in human brain magnetic resonance images. With the consideration of the spatial distribution of different tissues in brain images, our method does not need preestimation or precorrection procedures for intensity inhomogeneities and noise. A nonlocal information based Gaussian mixture model (NGMM) is proposed to reduce the effect of noise. To reduce the effect of intensity inhomogeneity, the multigrid nonlocal Gaussian mixture model (MNGMM) is proposed to segment brain MR images in each nonoverlapping multigrid generated by using a new multigrid generation method. Therefore the proposed model can simultaneously overcome the impact of noise and intensity inhomogeneity and automatically classify 2D and 3D MR data into tissues of white matter, gray matter, and cerebral spinal fluid. To maintain the statistical reliability and spatial continuity of the segmentation, a fusion strategy is adopted to integrate the clustering results from different grid. The experiments on synthetic and clinical brain MR images demonstrate the superior performance of the proposed model comparing with several state-of-the-art algorithms. PMID:27648448
Noise pollution resources compendium
NASA Technical Reports Server (NTRS)
1973-01-01
Abstracts of reports concerning noise pollution are presented. The abstracts are grouped in the following areas of activity: (1) sources of noise, (2) noise detection and measurement, (3) noise abatement and control, (4) physical effects of noise and (5) social effects of noise.
NASA Astrophysics Data System (ADS)
1983-01-01
SMART, Sound Modification and Regulated Temperature compound, is a liquid plastic mixture with exceptional energy and sound absorbing qualities. It is derived from a very elastic plastic which was an effective noise abatement material in the Apollo Guidance System. Discovered by a NASA employee, it is marketed by Environmental Health Systems, Inc. (EHS). The product has been successfully employed by a diaper company with noisy dryers and a sugar company with noisy blowers. The company also manufactures an audiometric test booth and acoustical office partitions.
Gaussian quantum operator representation for bosons
Corney, Joel F.; Drummond, Peter D.
2003-12-01
We introduce a Gaussian quantum operator representation, using the most general possible multimode Gaussian operator basis. The representation unifies and substantially extends existing phase-space representations of density matrices for Bose systems and also includes generalized squeezed-state and thermal bases. It enables first-principles dynamical or equilibrium calculations in quantum many-body systems, with quantum uncertainties appearing as dynamical objects. Any quadratic Liouville equation for the density operator results in a purely deterministic time evolution. Any cubic or quartic master equation can be treated using stochastic methods.
Inflationary prediction for primordial non-gaussianity.
Lyth, David H; Rodríguez, Yeinzon
2005-09-16
We extend the deltaN formalism so that it gives all of the stochastic properties of the primordial curvature perturbation zeta if the initial field perturbations are Gaussian. The calculation requires only the knowledge of some family of unperturbed universes. A formula is given for the normalization f(NL) of the bispectrum of zeta, which is the main signal of non-Gaussianity. Examples of the use of the formula are given, and its relation to cosmological perturbation theory is explained. PMID:16197063
Invariant measures on multimode quantum Gaussian states
Lupo, C.; Mancini, S.; De Pasquale, A.; Facchi, P.; Florio, G.; Pascazio, S.
2012-12-15
We derive the invariant measure on the manifold of multimode quantum Gaussian states, induced by the Haar measure on the group of Gaussian unitary transformations. To this end, by introducing a bipartition of the system in two disjoint subsystems, we use a parameterization highlighting the role of nonlocal degrees of freedom-the symplectic eigenvalues-which characterize quantum entanglement across the given bipartition. A finite measure is then obtained by imposing a physically motivated energy constraint. By averaging over the local degrees of freedom we finally derive the invariant distribution of the symplectic eigenvalues in some cases of particular interest for applications in quantum optics and quantum information.
A Gaussian-product stochastic Gent-McWilliams parameterization
NASA Astrophysics Data System (ADS)
Grooms, Ian
2016-10-01
The locally-averaged horizontal buoyancy flux by mesoscale eddies is computed from eddy-resolving quasigeostrophic simulations of ocean-mesoscale eddy dynamics. This flux has a very non-Gaussian distribution peaked at zero, not at the mean value. This non-Gaussian flux distribution arises because the flux is a product of zero-mean random variables: the eddy velocity and buoyancy. A framework for stochastic Gent-McWilliams (GM) parameterization is presented. Gaussian random field models for subgrid-scale velocity and buoyancy are developed. The product of these Gaussian random fields is used to construct a non-Gaussian stochastic parameterization of the horizontal subgrid-scale density flux, which leads to a non-Gaussian stochastic GM parameterization. This new non-Gaussian stochastic GM parameterization is tested in an idealized box ocean model, and compared to a Gaussian approach that simply multiplies the deterministic GM parameterization by a Gaussian random field. The non-Gaussian approach has a significant impact on both the mean and variability of the simulations, more so than the Gaussian approach; for example, the non-Gaussian simulation has a much larger net kinetic energy and a stronger overturning circulation than a comparable Gaussian simulation. Future directions for development of the stochastic GM parameterization and extensions of the Gaussian-product approach are discussed.
NASA Astrophysics Data System (ADS)
Ji, Se-Wan; Kim, M. S.; Nha, Hyunchul
2015-04-01
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.
KEPLER MISSION STELLAR AND INSTRUMENT NOISE PROPERTIES
Gilliland, Ronald L.; Chaplin, William J.; Elsworth, Yvonne P.; Miglio, Andrea; Dunham, Edward W.; Argabright, Vic S.; Borucki, William J.; Bryson, Stephen T.; Koch, David G.; Walkowicz, Lucianne M.; Basri, Gibor; Buzasi, Derek L.; Caldwell, Douglas A.; Jenkins, Jon M.; Van Cleve, Jeffrey; Welsh, William F.
2011-11-01
Kepler mission results are rapidly contributing to fundamentally new discoveries in both the exoplanet and asteroseismology fields. The data returned from Kepler are unique in terms of the number of stars observed, precision of photometry for time series observations, and the temporal extent of high duty cycle observations. As the first mission to provide extensive time series measurements on thousands of stars over months to years at a level hitherto possible only for the Sun, the results from Kepler will vastly increase our knowledge of stellar variability for quiet solar-type stars. Here, we report on the stellar noise inferred on the timescale of a few hours of most interest for detection of exoplanets via transits. By design the data from moderately bright Kepler stars are expected to have roughly comparable levels of noise intrinsic to the stars and arising from a combination of fundamental limitations such as Poisson statistics and any instrument noise. The noise levels attained by Kepler on-orbit exceed by some 50% the target levels for solar-type, quiet stars. We provide a decomposition of observed noise for an ensemble of 12th magnitude stars arising from fundamental terms (Poisson and readout noise), added noise due to the instrument and that intrinsic to the stars. The largest factor in the modestly higher than anticipated noise follows from intrinsic stellar noise. We show that using stellar parameters from galactic stellar synthesis models, and projections to stellar rotation, activity, and hence noise levels reproduce the primary intrinsic stellar noise features.
Quantum key distribution using continuous-variable non-Gaussian states
NASA Astrophysics Data System (ADS)
Borelli, L. F. M.; Aguiar, L. S.; Roversi, J. A.; Vidiella-Barranco, A.
2016-02-01
In this work, we present a quantum key distribution protocol using continuous-variable non-Gaussian states, homodyne detection and post-selection. The employed signal states are the photon added then subtracted coherent states (PASCS) in which one photon is added and subsequently one photon is subtracted from the field. We analyze the performance of our protocol, compared with a coherent state-based protocol, for two different attacks that could be carried out by the eavesdropper (Eve). We calculate the secret key rate transmission in a lossy line for a superior channel (beam-splitter) attack, and we show that we may increase the secret key generation rate by using the non-Gaussian PASCS rather than coherent states. We also consider the simultaneous quadrature measurement (intercept-resend) attack, and we show that the efficiency of Eve's attack is substantially reduced if PASCS are used as signal states.
Unified operator approach for deriving Hermite-Gaussian and Laguerre-Gaussian laser modes.
Enderlein, Jörg; Pampaloni, Francesco
2004-08-01
A unified operator approach is described for deriving Hermite-Gaussian and Laguerre-Gaussian laser beams by using as a starting point a plane-wave-spectrum representation of the electromagnetic field. We show that by using the plane-wave representation of the fundamental Gaussian mode as a seed function, all higher-order beam modes can be derived by acting with differential operators on this fundamental solution. The approach presented can be easily generalized to nonparaxial situations and to include vector effects of the electromagnetic field.
Unified operator approach for deriving Hermite-Gaussian and Laguerre-Gaussian laser modes
NASA Astrophysics Data System (ADS)
Enderlein, Jörg; Pampaloni, Francesco
2004-08-01
A unified operator approach is described for deriving Hermite-Gaussian and Laguerre-Gaussian laser beams by using as a starting point a plane-wave-spectrum representation of the electromagnetic field. We show that by using the plane-wave representation of the fundamental Gaussian mode as a seed function, all higher-order beam modes can be derived by acting with differential operators on this fundamental solution. The approach presented can be easily generalized to nonparaxial situations and to include vector effects of the electromagnetic field.
Ma, Rubao; Xu, Weichao; Zhang, Yun; Ye, Zhongfu
2014-01-01
This paper investigates the robustness properties of Pearson's rank-variate correlation coefficient (PRVCC) in scenarios where one channel is corrupted by impulsive noise and the other is impulsive noise-free. As shown in our previous work, these scenarios that frequently encountered in radar and/or sonar, can be well emulated by a particular bivariate contaminated Gaussian model (CGM). Under this CGM, we establish the asymptotic closed forms of the expectation and variance of PRVCC by means of the well known Delta method. To gain a deeper understanding, we also compare PRVCC with two other classical correlation coefficients, i.e., Spearman's rho (SR) and Kendall's tau (KT), in terms of the root mean squared error (RMSE). Monte Carlo simulations not only verify our theoretical findings, but also reveal the advantage of PRVCC by an example of estimating the time delay in the particular impulsive noise environment. PMID:25393286
Ma, Rubao; Xu, Weichao; Zhang, Yun; Ye, Zhongfu
2014-01-01
This paper investigates the robustness properties of Pearson's rank-variate correlation coefficient (PRVCC) in scenarios where one channel is corrupted by impulsive noise and the other is impulsive noise-free. As shown in our previous work, these scenarios that frequently encountered in radar and/or sonar, can be well emulated by a particular bivariate contaminated Gaussian model (CGM). Under this CGM, we establish the asymptotic closed forms of the expectation and variance of PRVCC by means of the well known Delta method. To gain a deeper understanding, we also compare PRVCC with two other classical correlation coefficients, i.e., Spearman's rho (SR) and Kendall's tau (KT), in terms of the root mean squared error (RMSE). Monte Carlo simulations not only verify our theoretical findings, but also reveal the advantage of PRVCC by an example of estimating the time delay in the particular impulsive noise environment.
A real-time multi-scale 2D Gaussian filter based on FPGA
NASA Astrophysics Data System (ADS)
Luo, Haibo; Gai, Xingqin; Chang, Zheng; Hui, Bin
2014-11-01
Multi-scale 2-D Gaussian filter has been widely used in feature extraction (e.g. SIFT, edge etc.), image segmentation, image enhancement, image noise removing, multi-scale shape description etc. However, their computational complexity remains an issue for real-time image processing systems. Aimed at this problem, we propose a framework of multi-scale 2-D Gaussian filter based on FPGA in this paper. Firstly, a full-hardware architecture based on parallel pipeline was designed to achieve high throughput rate. Secondly, in order to save some multiplier, the 2-D convolution is separated into two 1-D convolutions. Thirdly, a dedicate first in first out memory named as CAFIFO (Column Addressing FIFO) was designed to avoid the error propagating induced by spark on clock. Finally, a shared memory framework was designed to reduce memory costs. As a demonstration, we realized a 3 scales 2-D Gaussian filter on a single ALTERA Cyclone III FPGA chip. Experimental results show that, the proposed framework can computing a Multi-scales 2-D Gaussian filtering within one pixel clock period, is further suitable for real-time image processing. Moreover, the main principle can be popularized to the other operators based on convolution, such as Gabor filter, Sobel operator and so on.
Agricultural Education: Value Adding.
ERIC Educational Resources Information Center
Riesenberg, Lou E.; And Others
1989-01-01
This issue develops the theme of "Agricultural Education--Value Adding." The concept value adding has been a staple in the world of agricultural business for describing adding value to a commodity that would profit the producer and the local community. Agricultural education should add value to individuals and society to justify agricultural…
Cluster size distribution in Gaussian glasses
NASA Astrophysics Data System (ADS)
Novikov, S. V.
2011-03-01
A simple method for the estimation of the asymptotics of the cluster numbers in Gaussian glasses is described. Validity of the method was tested by the comparison with the exact analytic result for the non-correlated field and simulation data for the distribution of random energies in strongly spatially correlated dipolar glass model.
Diffusion of Super-Gaussian Profiles
ERIC Educational Resources Information Center
Rosenberg, C.-J.; Anderson, D.; Desaix, M.; Johannisson, P.; Lisak, M.
2007-01-01
The present analysis describes an analytically simple and systematic approximation procedure for modelling the free diffusive spreading of initially super-Gaussian profiles. The approach is based on a self-similar ansatz for the evolution of the diffusion profile, and the parameter functions involved in the modelling are determined by suitable…
NASA Astrophysics Data System (ADS)
Dybiec, Bartłomiej; Gudowska-Nowak, Ewa
2009-05-01
A standard approach to analysis of noise-induced effects in stochastic dynamics assumes a Gaussian character of the noise term describing interaction of the analyzed system with its complex surroundings. An additional assumption about the existence of timescale separation between the dynamics of the measured observable and the typical timescale of the noise allows external fluctuations to be modeled as temporally uncorrelated and therefore white. However, in many natural phenomena the assumptions concerning the above mentioned properties of 'Gaussianity' and 'whiteness' of the noise can be violated. In this context, in contrast to the spatiotemporal coupling characterizing general forms of non-Markovian or semi-Markovian Lévy walks, so called Lévy flights correspond to the class of Markov processes which can still be interpreted as white, but distributed according to a more general, infinitely divisible, stable and non-Gaussian law. Lévy noise-driven non-equilibrium systems are known to manifest interesting physical properties and have been addressed in various scenarios of physical transport exhibiting a superdiffusive behavior. Here we present a brief overview of our recent investigations aimed at understanding features of stochastic dynamics under the influence of Lévy white noise perturbations. We find that the archetypal phenomena of noise-induced ordering are robust and can be detected also in systems driven by memoryless, non-Gaussian, heavy-tailed fluctuations with infinite variance.
Coherence resonance induced by colored noise near Hopf bifurcation.
Ma, Juan; Xiao, Tiejun; Hou, Zhonghuai; Xin, Houwen
2008-12-01
Effects of colored noise near supercritical Hopf bifurcation, especially noise induced oscillation (NIO) and coherence resonance (CR), have been studied analytically in the Brusselator model, using the stochastic normal form method. Two types of colored noise are considered: one is the standard Gaussian colored noise generated by the Ornstein-Uhlenbeck (OU) process and the other is the so-called power-limited (PL) process. Depending on the noise intensity and noise type, it is found that the autocorrelation time, most probable radius and signal to noise ratio of the NIO may show nontrivial dependencies on the noise correlation time tau(c). Interestingly, for OU-type noise with intensity above a threshold, SNR is a bell-shaped function of tau(c), indicating enhancement of CR by noise correlation; and for PL-type noise, SNR may show double maxima when tau(c) is changed, demonstrating a new kind of multiresonance phenomenon. These theoretical predictions are well reproduced by numerical simulations. PMID:19123626
Coherence resonance induced by colored noise near Hopf bifurcation
NASA Astrophysics Data System (ADS)
Ma, Juan; Xiao, Tiejun; Hou, Zhonghuai; Xin, Houwen
2008-12-01
Effects of colored noise near supercritical Hopf bifurcation, especially noise induced oscillation (NIO) and coherence resonance (CR), have been studied analytically in the Brusselator model, using the stochastic normal form method. Two types of colored noise are considered: one is the standard Gaussian colored noise generated by the Ornstein-Uhlenbeck (OU) process and the other is the so-called power-limited (PL) process. Depending on the noise intensity and noise type, it is found that the autocorrelation time, most probable radius and signal to noise ratio of the NIO may show nontrivial dependencies on the noise correlation time τc. Interestingly, for OU-type noise with intensity above a threshold, SNR is a bell-shaped function of τc, indicating enhancement of CR by noise correlation; and for PL-type noise, SNR may show double maxima when τc is changed, demonstrating a new kind of multiresonance phenomenon. These theoretical predictions are well reproduced by numerical simulations.
Coherence resonance induced by colored noise near Hopf bifurcation.
Ma, Juan; Xiao, Tiejun; Hou, Zhonghuai; Xin, Houwen
2008-12-01
Effects of colored noise near supercritical Hopf bifurcation, especially noise induced oscillation (NIO) and coherence resonance (CR), have been studied analytically in the Brusselator model, using the stochastic normal form method. Two types of colored noise are considered: one is the standard Gaussian colored noise generated by the Ornstein-Uhlenbeck (OU) process and the other is the so-called power-limited (PL) process. Depending on the noise intensity and noise type, it is found that the autocorrelation time, most probable radius and signal to noise ratio of the NIO may show nontrivial dependencies on the noise correlation time tau(c). Interestingly, for OU-type noise with intensity above a threshold, SNR is a bell-shaped function of tau(c), indicating enhancement of CR by noise correlation; and for PL-type noise, SNR may show double maxima when tau(c) is changed, demonstrating a new kind of multiresonance phenomenon. These theoretical predictions are well reproduced by numerical simulations.
a Distributed Gaussian Discrete Variable Representation
NASA Astrophysics Data System (ADS)
Karabulut, Hasan
In this work a discrete variable representation (DVR) is constructed from a distributed Gaussian basis (DGB). A DGB is a finite or infinite chain of uniformly distributed Gaussians g_{n}(x) = e^{-c^2(x/d-n)^2} where n takes integer values. There are three main parts of this thesis. In the first part (Chapter III) the finite chain distributed Gaussian DVR (Finite Chain DG-DVR) is derived. In order to accomplish this, the distributed Gaussian orthogonal polynomials are introduced. The connection of these polynomials to Stieltjes-Wigert polynomials is shown. The recurrence relation for these orthogonal polynomials is derived. Tested recipes are given to calculate the quadrature points and weights and to construct the corresponding Lagrange functions which are analogs of Lagrange interpolation polynomials. The symmetries of quadrature points, weights, and Lagrange functions are derived. Limit cases ctoinfty and cto 0 are studied. In the second part (Chapter IV)the infinite chain limit DG-DVR is derived from a limit of the finite chain DG-DVR. The quadrature points and weights and the Lagrange functions are found in this limit and kinetic energy operator is constructed. It is shown that in the limit c to 0 the infinite chain DG-DVR reduces to Colbert and Miller's DVR. A discussion of ability of a distributed Gaussian basis to represent an arbitrary function is given. The results of this treatment yield a possible explanation of surprising accuracy of Colbert-Miller DVR. In the third part construction of the DG-DVR is given when one point is chosen arbitrarily. Some interesting identities and integral representations for the b _{n} and sigma_ {n} coefficients that are introduced in the second part are found.
Nguyen, Nha; Huang, Heng; Oraintara, Soontorn; Vo, An
2010-01-01
Motivation: Peaks are the key information in mass spectrometry (MS) which has been increasingly used to discover diseases-related proteomic patterns. Peak detection is an essential step for MS-based proteomic data analysis. Recently, several peak detection algorithms have been proposed. However, in these algorithms, there are three major deficiencies: (i) because the noise is often removed, the true signal could also be removed; (ii) baseline removal step may get rid of true peaks and create new false peaks; (iii) in peak quantification step, a threshold of signal-to-noise ratio (SNR) is usually used to remove false peaks; however, noise estimations in SNR calculation are often inaccurate in either time or wavelet domain. In this article, we propose new algorithms to solve these problems. First, we use bivariate shrinkage estimator in stationary wavelet domain to avoid removing true peaks in denoising step. Second, without baseline removal, zero-crossing lines in multi-scale of derivative Gaussian wavelets are investigated with mixture of Gaussian to estimate discriminative parameters of peaks. Third, in quantification step, the frequency, SD, height and rank of peaks are used to detect both high and small energy peaks with robustness to noise. Results: We propose a novel Gaussian Derivative Wavelet (GDWavelet) method to more accurately detect true peaks with a lower false discovery rate than existing methods. The proposed GDWavelet method has been performed on the real Surface-Enhanced Laser Desorption/Ionization Time-Of-Flight (SELDI-TOF) spectrum with known polypeptide positions and on two synthetic data with Gaussian and real noise. All experimental results demonstrate that our method outperforms other commonly used methods. The standard receiver operating characteristic (ROC) curves are used to evaluate the experimental results. Availability: http://ranger.uta.edu/∼heng/MS/GDWavelet.html or http://www.naaan.org/nhanguyen/archive.htm Contact: heng
Stochastic noise in atomic force microscopy.
Labuda, Aleksander; Lysy, Martin; Paul, William; Miyahara, Yoichi; Grütter, Peter; Bennewitz, Roland; Sutton, Mark
2012-09-01
Having reached the quantum and thermodynamic limits of detection, atomic force microscopy (AFM) experiments are routinely being performed at the fundamental limit of signal to noise. A critical understanding of the statistical properties of noise leads to more accurate interpretation of data, optimization of experimental protocols, advancements in instrumentation, and new measurement techniques. Furthermore, accurate simulation of cantilever dynamics requires knowledge of stochastic behavior of the system, as stochastic noise may exceed the deterministic signals of interest, and even dominate the outcome of an experiment. In this article, the power spectral density (PSD), used to quantify stationary stochastic processes, is introduced in the context of a thorough noise analysis of the light source used to detect cantilever deflections. The statistical properties of PSDs are then outlined for various stationary, nonstationary, and deterministic noise sources in the context of AFM experiments. Following these developments, a method for integrating PSDs to provide an accurate standard deviation of linear measurements is described. Lastly, a method for simulating stochastic Gaussian noise from any arbitrary power spectral density is presented. The result demonstrates that mechanical vibrations of the AFM can cause a logarithmic velocity dependence of friction and induce multiple slip events in the atomic stick-slip process, as well as predicts an artifactual temperature dependence of friction measured by AFM. PMID:23030863
Estimating noise and information for multispectral imagery
NASA Astrophysics Data System (ADS)
Aiazzi, Bruno; Alparone, Luciano; Barducci, Alessandro; Baronti, Stefano; Pippi, Ivan
2002-03-01
We focus on reliably estimating the information conveyed to a user by multispectral image data. The goal is establishing the extent to which an increase in spectral resolution can increase the amount of usable information. As a matter of fact, a trade- off exists between spatial and spectral resolution, due to physical constraints of sensors imaging with a prefixed SNR. After describing some methods developed for automatically estimating the variance of the noise introduced by multispectral imagers, lossless data compression is exploited to measure the useful information content of the multispectral data. In fact, the bit rate achieved by the reversible compression process takes into account both the contribution of the 'observation' noise, i.e., information regarded as statistical uncertainty, whose relevance is null to a user, and the intrinsic information of hypothetically noise free multispectral data. An entropic model of the image source is defined and, once the standard deviation of the noise, assumed to be white and Gaussian, has been preliminarily estimated, such a model is inverted to yield an estimate of the information content of the noise-free source from the code rate. Results of both noise and information assessment are reported and discussed on synthetic noisy images and on Landsat thematic mapper (TM) data.
Noise-assisted estimation of attractor invariants
NASA Astrophysics Data System (ADS)
Restrepo, Juan F.; Schlotthauer, Gastón
2016-07-01
In this article, the noise-assisted correlation integral (NCI) is proposed. The purpose of the NCI is to estimate the invariants of a dynamical system, namely the correlation dimension (D ), the correlation entropy (K2), and the noise level (σ ). This correlation integral is induced by using random noise in a modified version of the correlation algorithm, i.e., the noise-assisted correlation algorithm. We demonstrate how the correlation integral by Grassberger et al. and the Gaussian kernel correlation integral (GCI) by Diks can be thought of as special cases of the NCI. A third particular case is the U -correlation integral proposed herein, from which we derived coarse-grained estimators of the correlation dimension (DmU), the correlation entropy (KmU), and the noise level (σmU). Using time series from the Henon map and the Mackey-Glass system, we analyze the behavior of these estimators under different noise conditions and data lengths. The results show that the estimators DmU and σmU behave in a similar manner to those based on the GCI. However, for the calculation of K2, the estimator KmU outperforms its GCI-based counterpart. On the basis of the behavior of these estimators, we have proposed an automatic algorithm to find D ,K2, and σ from a given time series. The results show that by using this approach, we are able to achieve statistically reliable estimations of those invariants.
Diagnosis and impacts of non-Gaussianity of innovations in data assimilation
NASA Astrophysics Data System (ADS)
Pires, Carlos A.; Talagrand, Olivier; Bocquet, Marc
2010-09-01
Most of the atmospheric and oceanic data assimilation (DA) schemes rely on the Best Linear Unbiased Estimator (BLUE), which is sub-optimal if errors of assimilated data are non-Gaussian, thus calling for a full Bayesian data assimilation. This paper contributes to the study of the non-Gaussianity of errors in the observational space. Possible sources of non-Gaussianity range from the inherent statistical skewness and positiveness of some physical observables (e.g. moisture, chemical species), the nonlinearity, both of the data assimilation models and of the observation operators among others. Deviations from Gaussianity can be justified from a priori hypotheses or inferred from statistical diagnostics of innovations (observation minus background), leading to consistency relationships between the error statistics. From samples of observations and backgrounds as well as their specified error variances, we evaluate some measures of the innovation non-Gaussianity, such as the skewness, kurtosis and negentropy. Under the assumption of additive errors and by relating statistical moments from both data errors and innovations, we identify potential sources of the innovation non-Gaussianity. These sources range from: (1) univariate error non-Gaussianity, (2), nonlinear correlations between errors, (3) spatio-temporal variability of error variances (heteroscedasticity) and (4) multiplicative noise. Observational and background errors are often assumed independent. This leads to variance-dependent bounds for the skewness and the kurtosis of errors. From innovation statistics, we assess the potential DA impact of some scenarios of non-Gaussian errors. This impact is measured through the mean square difference between the BLUE and the Minimum Variance Unbiased Estimator (MVUE), obtained with univariate observations and background estimates. In order to accomplish this, we compute maximum entropy probability density functions (pdfs) of the errors, constrained by the first four
Characterization of impulse noise and analysis of its effect upon correlation receivers
NASA Technical Reports Server (NTRS)
Houts, R. C.; Moore, J. D.
1971-01-01
A noise model is formulated to describe the impulse noise in many digital systems. A simplified model, which assumes that each noise burst contains a randomly weighted version of the same basic waveform, is used to derive the performance equations for a correlation receiver. The expected number of bit errors per noise burst is expressed as a function of the average signal energy, signal-set correlation coefficient, bit time, noise-weighting-factor variance and probability density function, and a time range function which depends on the crosscorrelation of the signal-set basis functions and the noise waveform. A procedure is established for extending the results for the simplified noise model to the general model. Unlike the performance results for Gaussian noise, it is shown that for impulse noise the error performance is affected by the choice of signal-set basis functions and that Orthogonal signaling is not equivalent to On-Off signaling with the same average energy.
Spin noise in the anisotropic central spin model
NASA Astrophysics Data System (ADS)
Hackmann, Johannes; Anders, Frithjof B.
2014-01-01
Spin-noise measurements can serve as a direct probe for the microscopic decoherence mechanism of an electronic spin in semiconductor quantum dots (QDs). We have calculated the spin-noise spectrum in the anisotropic central spin model using a Chebyshev expansion technique which exactly accounts for the dynamics up to an arbitrary long but fixed time in a finite-size system. In the isotropic case, describing QD charge with a single electron, the short-time dynamics is in good agreement with quasistatic approximations for the thermodynamic limit. The spin-noise spectrum, however, shows strong deviations at low frequencies with a power-law behavior of ω-3/4 corresponding to a t-1/4 decay at intermediate and long times. In the Ising limit, applicable to QDs with heavy-hole spins, the spin-noise spectrum exhibits a threshold behavior of (ω-ωL)-1/2 above the Larmor frequency ωL=gμBB. In the generic anisotropic central spin model we have found a crossover from a Gaussian type of spin-noise spectrum to a more Ising-type spectrum with increasing anisotropy in a finite magnetic field. In order to make contact with experiments, we present ensemble averaged spin-noise spectra for QD ensembles charged with single electrons or holes. The Gaussian-type noise spectrum evolves to a more Lorentzian shape spectrum with increasing spread of characteristic time scales and g factors of the individual QDs.
THE HALO MASS FUNCTION FROM EXCURSION SET THEORY. III. NON-GAUSSIAN FLUCTUATIONS
Maggiore, Michele; Riotto, Antonio
2010-07-01
We compute the effect of primordial non-Gaussianity on the halo mass function, using excursion set theory. In the presence of non-Gaussianity, the stochastic evolution of the smoothed density field, as a function of the smoothing scale, is non-Markovian and beside 'local' terms that generalize Press-Schechter (PS) theory, there are also 'memory' terms, whose effect on the mass function can be computed using the formalism developed in the first paper of this series. We find that, when computing the effect of the three-point correlator on the mass function, a PS-like approach which consists in neglecting the cloud-in-cloud problem and in multiplying the final result by a fudge factor {approx_equal}2, is in principle not justified. When computed correctly in the framework of excursion set theory, in fact, the 'local' contribution vanishes (for all odd-point correlators the contribution of the image Gaussian cancels the PS contribution rather than adding up), and the result comes entirely from non-trivial memory terms which are absent in PS theory. However it turns out that, in the limit of large halo masses, where the effect of non-Gaussianity is more relevant, these memory terms give a contribution which is the same as that computed naively with PS theory, plus subleading terms depending on derivatives of the three-point correlator. We finally combine these results with the diffusive barrier model developed in the second paper of this series, and we find that the resulting mass function reproduces recent N-body simulations with non-Gaussian initial conditions, without the introduction of any ad hoc parameter.
Quantum metrology. Fisher information and entanglement of non-Gaussian spin states.
Strobel, Helmut; Muessel, Wolfgang; Linnemann, Daniel; Zibold, Tilman; Hume, David B; Pezzè, Luca; Smerzi, Augusto; Oberthaler, Markus K
2014-07-25
Entanglement is the key quantum resource for improving measurement sensitivity beyond classical limits. However, the production of entanglement in mesoscopic atomic systems has been limited to squeezed states, described by Gaussian statistics. Here, we report on the creation and characterization of non-Gaussian many-body entangled states. We develop a general method to extract the Fisher information, which reveals that the quantum dynamics of a classically unstable system creates quantum states that are not spin squeezed but nevertheless entangled. The extracted Fisher information quantifies metrologically useful entanglement, which we confirm by Bayesian phase estimation with sub-shot-noise sensitivity. These methods are scalable to large particle numbers and applicable directly to other quantum systems.
Bingi, Jayachandra; Murukeshan, Vadakke Matham
2015-01-01
Laser speckle pattern is a granular structure formed due to random coherent wavelet interference and generally considered as noise in optical systems including photolithography. Contrary to this, in this paper, we use the speckle pattern to generate predictable and controlled Gaussian random structures and quasi-random structures photo-lithographically. The random structures made using this proposed speckle lithography technique are quantified based on speckle statistics, radial distribution function (RDF) and fast Fourier transform (FFT). The control over the speckle size, density and speckle clustering facilitates the successful fabrication of black silicon with different surface structures. The controllability and tunability of randomness makes this technique a robust method for fabricating predictable 2D Gaussian random structures and black silicon structures. These structures can enhance the light trapping significantly in solar cells and hence enable improved energy harvesting. Further, this technique can enable efficient fabrication of disordered photonic structures and random media based devices.
Parameterization of cloud lidar backscattering profiles by means of asymmetrical Gaussians.
Guasta, M D; Morandi, M; Stefanutti, L
1995-06-20
A fitting procedure for cloud lidar data processing is shown that is based on the computation of the first three moments of the vertical-backscattering (or -extinction) profile. Single-peak clouds or single cloud layers are approximated to asymmetrical Gaussians. The algorithm is particularly stable with respect to noise and processing errors, and it is much faster than the equivalent least-squares approach. Multilayer clouds can easily be treated as a sum of single asymmetrical Gaussian peaks. The method is suitable for cloud-shape parametrization in noisy lidar signatures (like those expected from satellite lidars). It also permits an improvement of cloud radiative-property computations that are based on huge lidar data sets for which storage and careful examination of single lidar profiles can't be carried out.
Bingi, Jayachandra; Murukeshan, Vadakke Matham
2015-01-01
Laser speckle pattern is a granular structure formed due to random coherent wavelet interference and generally considered as noise in optical systems including photolithography. Contrary to this, in this paper, we use the speckle pattern to generate predictable and controlled Gaussian random structures and quasi-random structures photo-lithographically. The random structures made using this proposed speckle lithography technique are quantified based on speckle statistics, radial distribution function (RDF) and fast Fourier transform (FFT). The control over the speckle size, density and speckle clustering facilitates the successful fabrication of black silicon with different surface structures. The controllability and tunability of randomness makes this technique a robust method for fabricating predictable 2D Gaussian random structures and black silicon structures. These structures can enhance the light trapping significantly in solar cells and hence enable improved energy harvesting. Further, this technique can enable efficient fabrication of disordered photonic structures and random media based devices. PMID:26679513
Indirect combustion noise of auxiliary power units
NASA Astrophysics Data System (ADS)
Tam, Christopher K. W.; Parrish, Sarah A.; Xu, Jun; Schuster, Bill
2013-08-01
Recent advances in noise suppression technology have significantly reduced jet and fan noise from commercial jet engines. This leads many investigators in the aeroacoustics community to suggest that core noise could well be the next aircraft noise barrier. Core noise consists of turbine noise and combustion noise. There is direct combustion noise generated by the combustion processes, and there is indirect combustion noise generated by the passage of combustion hot spots, or entropy waves, through constrictions in an engine. The present work focuses on indirect combustion noise. Indirect combustion noise has now been found in laboratory experiments. The primary objective of this work is to investigate whether indirect combustion noise is also generated in jet and other engines. In a jet engine, there are numerous noise sources. This makes the identification of indirect combustion noise a formidable task. Here, our effort concentrates exclusively on auxiliary power units (APUs). This choice is motivated by the fact that APUs are relatively simple engines with only a few noise sources. It is, therefore, expected that the chance of success is higher. Accordingly, a theoretical model study of the generation of indirect combustion noise in an Auxiliary Power Unit (APU) is carried out. The cross-sectional areas of an APU from the combustor to the turbine exit are scaled off to form an equivalent nozzle. A principal function of a turbine in an APU is to extract mechanical energy from the flow stream through the exertion of a resistive force. Therefore, the turbine is modeled by adding a negative body force to the momentum equation. This model is used to predict the ranges of frequencies over which there is a high probability for indirect combustion noise generation. Experimental spectra of internal pressure fluctuations and far-field noise of an RE220 APU are examined to identify anomalous peaks. These peaks are possible indirection combustion noise. In the case of the
Compression station upgrades include advanced noise reduction
Dunning, V.R.; Sherikar, S.
1998-10-01
Since its inception in the mid-`80s, AlintaGas` Dampier to Bunbury natural gas pipeline has been constantly undergoing a series of upgrades to boost capacity and meet other needs. Extending northward about 850 miles from near Perth to the northwest shelf, the 26-inch line was originally served by five compressor stations. In the 1989-91 period, three new compressor stations were added to increase capacity and a ninth station was added in 1997. Instead of using noise-path-treatment mufflers to reduce existing noise, it was decided to use noise-source-treatment technology to prevent noise creation in the first place. In the field, operation of these new noise-source treatment attenuators has been very quiet. If there was any thought earlier of guaranteed noise-level verification, it is not considered a priority now. It`s also anticipated that as AlintaGas proceeds with its pipeline and compressor station upgrade program, similar noise-source treatment equipment will be employed and retrofitted into older stations where the need to reduce noise and potential radiant-heat exposure is indicated.
NASA Astrophysics Data System (ADS)
Fidell, Sandy
The primary effects of community noise on residential populations are speech interference, sleep disturbance, and annoyance. This chapter focuses on transportation noise in general and on aircraft noise in particular because aircraft noise is one of the most prominent community noise sources, because airport/community controversies are often the most contentious and widespread, and because industrial and other specialized formsofcommunitynoise generally posemorelocalized problems.
NASA Astrophysics Data System (ADS)
Xiang, Shao-Hua; Wen, Wei; Zhao, Yu-Jing; Song, Ke-Hui
2016-06-01
We characterize the non-Gaussianity of continuous-variable quantum states in terms of the cumulant theory and derive the exact formula of the cumulant of any order for such states. Exploiting the fourth-order cumulant method, we evaluate the quantum non-Gaussianity of two-mode single-photon squeezed Bell states and investigate their dynamics under the influence of two different types of decoherence models. It is shown that in a two-reservoir model, all the fourth-order cumulants of these states are very fragile, while in single-reservoir model, the fourth-order cumulants of one such state are insensitive to thermal noise, showing the time-invariant non-Gaussianity.
NASA Technical Reports Server (NTRS)
Mcgregor, D. N.; Tasoulis, G.; Kinal, G. V.
1980-01-01
The performance of Viterbi decoding in a non-Gaussian environment is investigated using a nonlinear quantization strategy. The channel model consists of a convolutionally encoded BPSK signal transmitted to a satellite where it is corrupted with additive white Gaussian noise and pulsed radio frequency interference (RFI). The resultant signal is then passed through a satellite nonlinearity and transmitted to a ground station where it is coherently detected. Interleaving is assumed in order to make the channel memoryless. The presence of RFI makes the channel statistics non-Gaussian, leading to a nonlinear log-likelihood function. A near optimum quantization scheme is found by maximizing a channel parameter, or by matching the quantizer to the log-likelihood function in a mean square error sense. Bit error rate performance improvement is achieved by using such nonlinear quantization.
Wilson, Scott; Bowyer, Andrea; Harrap, Stephen B
2015-01-01
The clinical characterization of cardiovascular dynamics during hemodialysis (HD) has important pathophysiological implications in terms of diagnostic, cardiovascular risk assessment, and treatment efficacy perspectives. Currently the diagnosis of significant intradialytic systolic blood pressure (SBP) changes among HD patients is imprecise and opportunistic, reliant upon the presence of hypotensive symptoms in conjunction with coincident but isolated noninvasive brachial cuff blood pressure (NIBP) readings. Considering hemodynamic variables as a time series makes a continuous recording approach more desirable than intermittent measures; however, in the clinical environment, the data signal is susceptible to corruption due to both impulsive and Gaussian-type noise. Signal preprocessing is an attractive solution to this problem. Prospectively collected continuous noninvasive SBP data over the short-break intradialytic period in ten patients was preprocessed using a novel median hybrid filter (MHF) algorithm and compared with 50 time-coincident pairs of intradialytic NIBP measures from routine HD practice. The median hybrid preprocessing technique for continuously acquired cardiovascular data yielded a dynamic regression without significant noise and artifact, suitable for high-level profiling of time-dependent SBP behavior. Signal accuracy is highly comparable with standard NIBP measurement, with the added clinical benefit of dynamic real-time hemodynamic information. PMID:25678827
Wilson, Scott; Bowyer, Andrea; Harrap, Stephen B
2015-01-01
The clinical characterization of cardiovascular dynamics during hemodialysis (HD) has important pathophysiological implications in terms of diagnostic, cardiovascular risk assessment, and treatment efficacy perspectives. Currently the diagnosis of significant intradialytic systolic blood pressure (SBP) changes among HD patients is imprecise and opportunistic, reliant upon the presence of hypotensive symptoms in conjunction with coincident but isolated noninvasive brachial cuff blood pressure (NIBP) readings. Considering hemodynamic variables as a time series makes a continuous recording approach more desirable than intermittent measures; however, in the clinical environment, the data signal is susceptible to corruption due to both impulsive and Gaussian-type noise. Signal preprocessing is an attractive solution to this problem. Prospectively collected continuous noninvasive SBP data over the short-break intradialytic period in ten patients was preprocessed using a novel median hybrid filter (MHF) algorithm and compared with 50 time-coincident pairs of intradialytic NIBP measures from routine HD practice. The median hybrid preprocessing technique for continuously acquired cardiovascular data yielded a dynamic regression without significant noise and artifact, suitable for high-level profiling of time-dependent SBP behavior. Signal accuracy is highly comparable with standard NIBP measurement, with the added clinical benefit of dynamic real-time hemodynamic information.
Missing ordinal patterns in correlated noises
NASA Astrophysics Data System (ADS)
Carpi, Laura C.; Saco, Patricia M.; Rosso, O. A.
2010-05-01
Recent research aiming at the distinction between deterministic or stochastic behavior in observational time series has looked into the properties of the “ordinal patterns” [C. Bandt, B. Pompe, Phys. Rev. Lett. 88 (2002) 174102]. In particular, new insight has been obtained considering the emergence of the so-called “forbidden ordinal patterns” [J.M. Amigó, S. Zambrano, M.A. F Sanjuán, Europhys. Lett. 79 (2007) 50001]. It was shown that deterministic one-dimensional maps always have forbidden ordinal patterns, in contrast with time series generated by an unconstrained stochastic process in which all the patterns appear with probability one. Techniques based on the comparison of this property in an observational time series and in white Gaussian noise were implemented. However, the comparison with correlated stochastic processes was not considered. In this paper we used the concept of “missing ordinal patterns” to study their decay rate as a function of the time series length in three stochastic processes with different degrees of correlation: fractional Brownian motion, fractional Gaussian noise and, noises with f power spectrum. We show that the decay rate of “missing ordinal patterns” in these processes depend on their correlation structures. We finally discuss the implications of the present results for the use of these properties as a tool for distinguishing deterministic from stochastic processes.
Simple noise reduction for diffusion weighted images.
Konishi, Yuto; Kanazawa, Yuki; Usuda, Takatoshi; Matsumoto, Yuki; Hayashi, Hiroaki; Matsuda, Tsuyoshi; Ueno, Junji; Harada, Masafumi
2016-07-01
Our purpose in this study was to reduce the noise in order to improve the SNR of Dw images with high b-value by using two correction schemes. This study was performed with use of phantoms made from water and sucrose at different concentrations, which were 10, 30, and 50 weight percent (wt%). In noise reduction for Dw imaging of the phantoms, we compared two correction schemes that are based on the Rician distribution and the Gaussian distribution. The highest error values for each concentration with use of the Rician distribution scheme were 7.3 % for 10 wt%, 2.4 % for 30 wt%, and 0.1 % for 50 wt%. The highest error values for each concentration with use of the Gaussian distribution scheme were 20.3 % for 10 wt%, 11.6 % for 30 wt%, and 3.4 % for 50 wt%. In Dw imaging, the noise reduction makes it possible to apply the correction scheme of Rician distribution. PMID:26984734
Quantum reading of digital memory with non-Gaussian entangled light
NASA Astrophysics Data System (ADS)
Tej, J. Prabhu; Devi, A. R. Usha; Rajagopal, A. K.
2013-05-01
It has been shown recently S. Pirandola [Phys. Rev. Lett.PRLTAO0031-900710.1103/PhysRevLett.106.090504 106, 090504 (2011)] that entangled light with Einstein-Podolsky-Rosen correlations retrieves information from digital memory better than any classical light. In identifying this, a model of digital memory with each cell consisting of a reflecting medium with two reflectivities (each memory cell encoding the binary numbers 0 or 1) is employed. The readout of binary memory essentially corresponds to discrimination of two bosonic attenuator channels characterized by different reflectivities. The model requires an entire mathematical paraphernalia of a continuous variable Gaussian setting for its analysis when arbitrary values of reflectivities 0≤r0,r1≤1 are considered. Here we restrict ourselves to a basic quantum readout mechanism with two different families of non-Gaussian entangled states of light, in which the binary channels to be discriminated are (i) ideal memory characterized by reflectivity r1=1 (identity channel) and (ii) a thermal noise channel—where the signal light illuminating the memory location gets completely lost (r0=0) and only a white thermal noise hitting the upper side of the memory reaches the decoder. We compare the quantum reading efficiency of two families of non-Gaussian entangled light [(m,m') family of path-entangled photon states and entangled state obtained by mixing a single photon with coherent light in a 50:50 beam splitter] with any classical source of light in this model. We identify that the classes of non-Gaussian entangled transmitters studied here offer significantly better reading performance than any classical transmitters of light in the regime of low signal intensity. We also demonstrate that the (m,m') family of entangled light exhibits better reading performance than NOON states.
NASA Astrophysics Data System (ADS)
Ammon, Martin; Erdmenger, Johanna; Meyer, René; O'Bannon, Andy; Wrase, Timm
2009-11-01
Aharony, Bergman, Jafferis, and Maldacena have proposed that the low-energy description of multiple M2-branes at a Bbb C4/Bbb Zk singularity is a (2+1)-dimensional Script N = 6 supersymmetric U(Nc) × U(Nc) Chern-Simons matter theory, the ABJM theory. In the large-Nc limit, its holographic dual is supergravity in AdS4 × S7/Bbb Zk. We study various ways to add fields that transform in the fundamental representation of the gauge groups, i.e. flavor fields, to the ABJM theory. We work in a probe limit and perform analyses in both the supergravity and field theory descriptions. In the supergravity description we find a large class of supersymmetric embeddings of probe flavor branes. In the field theory description, we present a general method to determine the couplings of the flavor fields to the fields of the ABJM theory. We then study four examples in detail: codimension-zero Script N = 3 supersymmetric flavor, described in supergravity by Kaluza-Klein monopoles or D6-branes; codimension-one Script N = (0,6) supersymmetric chiral flavor, described by D8-branes; codimension-one Script N = (3,3) supersymmetric non-chiral flavor, described by M5/D4-branes; codimension-two Script N = 4 supersymmetric flavor, described by M2/D2-branes. Finally we discuss special physical equivalences between brane embeddings in M-theory, and their interpretation in the field theory description.
Continuous-variable entanglement distillation of non-Gaussian mixed states
Dong Ruifang; Lassen, Mikael; Heersink, Joel; Marquardt, Christoph; Leuchs, Gerd; Andersen, Ulrik L.
2010-07-15
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.
Image noise removal using Kalman-Filter on dark frame
NASA Astrophysics Data System (ADS)
Liu, Tao; Zhao, Ju-feng; Feng, Hua-jun; Xu, Zhi-hai; Chen, Hui-fang
2011-08-01
Dark frame is mixture of fixed pattern noise (FPN), multiplicative Gaussian noise and signal-independent noise, which appear in exposed image at the same time. Due to the increase of the operate temperature inside imaging system and the circuit parameters' minor drifts, FPN of each pixel varies from frame to frame slowly and non-uniformly. In this paper, the dark frame is modeled and then the equations of Kalman-filter is deduced to estimate the FPN level. We introduce the noise influence factor (NIF) to evaluate the influence of FPN noise on each pixel. The reasonable weight for each pixel can set adaptively by means of NIF. Denoised image can be got after weighted subtraction dark frame from the image data on pixels one by one.
Large non-gaussianity in axion inflation.
Barnaby, Neil; Peloso, Marco
2011-05-01
The inflationary paradigm has enjoyed phenomenological success; however, a compelling particle physics realization is still lacking. Axions are among the best-motivated inflaton candidates, since the flatness of their potential is naturally protected by a shift symmetry. We reconsider the cosmological perturbations in axion inflation, consistently accounting for the coupling to gauge fields cΦFF, which is generically present in these models. This coupling leads to production of gauge quanta, which provide a new source of inflaton fluctuations, δΦ. For c≥10(2)M(p)(-1), these dominate over the vacuum fluctuations, and non-Gaussianity exceeds the current observational bound. This regime is typical for concrete realizations that admit a UV completion; hence, large non-Gaussianity is easily obtained in minimal and natural realizations of inflation.
Quantum Fidelity for Arbitrary Gaussian States
NASA Astrophysics Data System (ADS)
Banchi, Leonardo; Braunstein, Samuel L.; Pirandola, Stefano
2015-12-01
We derive a computable analytical formula for the quantum fidelity between two arbitrary multimode Gaussian states which is simply expressed in terms of their first- and second-order statistical moments. We also show how such a formula can be written in terms of symplectic invariants and used to derive closed forms for a variety of basic quantities and tools, such as the Bures metric, the quantum Fisher information, and various fidelity-based bounds. Our result can be used to extend the study of continuous-variable protocols, such as quantum teleportation and cloning, beyond the current one-mode or two-mode analyses, and paves the way to solve general problems in quantum metrology and quantum hypothesis testing with arbitrary multimode Gaussian resources.
Fock expansion of multimode pure Gaussian states
Cariolaro, Gianfranco; Pierobon, Gianfranco
2015-12-15
The Fock expansion of multimode pure Gaussian states is derived starting from their representation as displaced and squeezed multimode vacuum states. The approach is new and appears to be simpler and more general than previous ones starting from the phase-space representation given by the characteristic or Wigner function. Fock expansion is performed in terms of easily evaluable two-variable Hermite–Kampé de Fériet polynomials. A relatively simple and compact expression for the joint statistical distribution of the photon numbers in the different modes is obtained. In particular, this result enables one to give a simple characterization of separable and entangled states, as shown for two-mode and three-mode Gaussian states.
A Fast Incremental Gaussian Mixture Model
Pinto, Rafael Coimbra; Engel, Paulo Martins
2015-01-01
This work builds upon previous efforts in online incremental learning, namely the Incremental Gaussian Mixture Network (IGMN). The IGMN is capable of learning from data streams in a single-pass by improving its model after analyzing each data point and discarding it thereafter. Nevertheless, it suffers from the scalability point-of-view, due to its asymptotic time complexity of O(NKD3) for N data points, K Gaussian components and D dimensions, rendering it inadequate for high-dimensional data. In this work, we manage to reduce this complexity to O(NKD2) by deriving formulas for working directly with precision matrices instead of covariance matrices. The final result is a much faster and scalable algorithm which can be applied to high dimensional tasks. This is confirmed by applying the modified algorithm to high-dimensional classification datasets. PMID:26444880
Gaussian quadrature for multiple orthogonal polynomials
NASA Astrophysics Data System (ADS)
Coussement, Jonathan; van Assche, Walter
2005-06-01
We study multiple orthogonal polynomials of type I and type II, which have orthogonality conditions with respect to r measures. These polynomials are connected by their recurrence relation of order r+1. First we show a relation with the eigenvalue problem of a banded lower Hessenberg matrix Ln, containing the recurrence coefficients. As a consequence, we easily find that the multiple orthogonal polynomials of type I and type II satisfy a generalized Christoffel-Darboux identity. Furthermore, we explain the notion of multiple Gaussian quadrature (for proper multi-indices), which is an extension of the theory of Gaussian quadrature for orthogonal polynomials and was introduced by Borges. In particular, we show that the quadrature points and quadrature weights can be expressed in terms of the eigenvalue problem of Ln.
Quantum Fidelity for Arbitrary Gaussian States.
Banchi, Leonardo; Braunstein, Samuel L; Pirandola, Stefano
2015-12-31
We derive a computable analytical formula for the quantum fidelity between two arbitrary multimode Gaussian states which is simply expressed in terms of their first- and second-order statistical moments. We also show how such a formula can be written in terms of symplectic invariants and used to derive closed forms for a variety of basic quantities and tools, such as the Bures metric, the quantum Fisher information, and various fidelity-based bounds. Our result can be used to extend the study of continuous-variable protocols, such as quantum teleportation and cloning, beyond the current one-mode or two-mode analyses, and paves the way to solve general problems in quantum metrology and quantum hypothesis testing with arbitrary multimode Gaussian resources. PMID:26764978
Edge Detection By Differences Of Gaussians
NASA Astrophysics Data System (ADS)
Marthon, Ph.; Thiesse, B.; Bruel, A.
1986-06-01
The Differences of Gaussians (DOGs) are of fundamental importance in edge detection. They belong to the human vision system as shown by Enroth-Cugell and Robson [ENR66]. The zero-crossings of their outputs mark the loci of the intensity changes. The set of descriptions from different operator sizes forms the input for later visual processes, such as stereopsis and motion analysis. We show that DOGs uniformly converge to the Laplacian of a Gaussian (ΔG2,σ) when both the inhibitory and excitatory variables converge to σ. Spatial and spectral properties of DOGs and ΔGs are compared: width and height of their central positive regions, bandiwidths... Finally, DOGs' responses to some features such as ideal edge, right angle corner, general corner..., are presented and magnitudes of error on edge position are given.
NASA Astrophysics Data System (ADS)
Adamo, Tim; Skinner, David; Williams, Jack
2016-08-01
We consider the application of twistor theory to five-dimensional anti-de Sitter space. The twistor space of AdS5 is the same as the ambitwistor space of the four-dimensional conformal boundary; the geometry of this correspondence is reviewed for both the bulk and boundary. A Penrose transform allows us to describe free bulk fields, with or without mass, in terms of data on twistor space. Explicit representatives for the bulk-to-boundary propagators of scalars and spinors are constructed, along with twistor action functionals for the free theories. Evaluating these twistor actions on bulk-to-boundary propagators is shown to produce the correct two-point functions.
NASA Astrophysics Data System (ADS)
Bena, Iosif; Heurtier, Lucien; Puhm, Andrea
2016-05-01
It was argued in [1] that the five-dimensional near-horizon extremal Kerr (NHEK) geometry can be embedded in String Theory as the infrared region of an infinite family of non-supersymmetric geometries that have D1, D5, momentum and KK monopole charges. We show that there exists a method to embed these geometries into asymptotically- {AdS}_3× {S}^3/{{Z}}_N solutions, and hence to obtain infinite families of flows whose infrared is NHEK. This indicates that the CFT dual to the NHEK geometry is the IR fixed point of a Renormalization Group flow from a known local UV CFT and opens the door to its explicit construction.
Non-Markovianity of Gaussian Channels.
Torre, G; Roga, W; Illuminati, F
2015-08-14
We introduce a necessary and sufficient criterion for the non-Markovianity of Gaussian quantum dynamical maps based on the violation of divisibility. The criterion is derived by defining a general vectorial representation of the covariance matrix which is then exploited to determine the condition for the complete positivity of partial maps associated with arbitrary time intervals. Such construction does not rely on the Choi-Jamiolkowski representation and does not require optimization over states.
Microwave Realization of the Gaussian Symplectic Ensemble
NASA Astrophysics Data System (ADS)
Rehemanjiang, A.; Allgaier, M.; Joyner, C. H.; Müller, S.; Sieber, M.; Kuhl, U.; Stöckmann, H.-J.
2016-08-01
Following an idea by Joyner et al. [Europhys. Lett. 107, 50004 (2014)], a microwave graph with an antiunitary symmetry T obeying T2=-1 is realized. The Kramers doublets expected for such systems are clearly identified and can be lifted by a perturbation which breaks the antiunitary symmetry. The observed spectral level spacings distribution of the Kramers doublets is in agreement with the predictions from the Gaussian symplectic ensemble expected for chaotic systems with such a symmetry.
Consistency relations for non-Gaussianity
NASA Astrophysics Data System (ADS)
Li, Miao; Wang, Yi
2008-09-01
We investigate consistency relations for non-Gaussianity. We provide a model-independent dynamical proof for the consistency relation for three-point correlation functions from the Hamiltonian and field redefinition. This relation can be applied to single-field inflation, multi-field inflation and the curvaton scenario. This relation can also be generalized to n-point correlation functions up to arbitrary order in perturbation theory and with arbitrary number of loops.
Computational aspects of Gaussian beam migration
Hale, D.
1992-01-01
The computational efficiency of Gaussian beam migration depends on the solution of two problems: (1) computation of complex-valued beam times and amplitudes in Cartesian (x,z) coordinates, and (2) limiting computations to only those (x,z) coordinates within a region where beam amplitudes are significant. The first problem can be reduced to a particular instance of a class of closest-point problems in computational geometry, for which efficient solutions, such as the Delaunay triangulation, are well known. Delaunay triangulation of sampled points along a ray enables the efficient location of that point on the raypath that is closest to any point (x,z) at which beam times and amplitudes are required. Although Delaunay triangulation provides an efficient solution to this closest point problem, a simpler solution, also presented in this paper, may be sufficient and more easily extended for use in 3-D Gaussian beam migration. The second problem is easily solved by decomposing the subsurface image into a coarse grid of square cells. Within each cell, simple and efficient loops over (x,z) coordinates may be used. Because the region in which beam amplitudes are significant may be difficult to represent with simple loops over (x,z) coordinates, I use recursion to move from cell to cell, until entire region defined by the beam has been covered. Benchmark tests of a computer program implementing these solutions suggest that the cost of Gaussian hewn migration is comparable to that of migration via explicit depth extrapolation in the frequency-space domain. For the data sizes and computer programs tested here, the explicit method was faster. However, as data size was increased, the computation time for Gaussian beam migration grew more slowly than that for the explicit method.
Computational aspects of Gaussian beam migration
Hale, D.
1992-08-01
The computational efficiency of Gaussian beam migration depends on the solution of two problems: (1) computation of complex-valued beam times and amplitudes in Cartesian (x,z) coordinates, and (2) limiting computations to only those (x,z) coordinates within a region where beam amplitudes are significant. The first problem can be reduced to a particular instance of a class of closest-point problems in computational geometry, for which efficient solutions, such as the Delaunay triangulation, are well known. Delaunay triangulation of sampled points along a ray enables the efficient location of that point on the raypath that is closest to any point (x,z) at which beam times and amplitudes are required. Although Delaunay triangulation provides an efficient solution to this closest point problem, a simpler solution, also presented in this paper, may be sufficient and more easily extended for use in 3-D Gaussian beam migration. The second problem is easily solved by decomposing the subsurface image into a coarse grid of square cells. Within each cell, simple and efficient loops over (x,z) coordinates may be used. Because the region in which beam amplitudes are significant may be difficult to represent with simple loops over (x,z) coordinates, I use recursion to move from cell to cell, until entire region defined by the beam has been covered. Benchmark tests of a computer program implementing these solutions suggest that the cost of Gaussian hewn migration is comparable to that of migration via explicit depth extrapolation in the frequency-space domain. For the data sizes and computer programs tested here, the explicit method was faster. However, as data size was increased, the computation time for Gaussian beam migration grew more slowly than that for the explicit method.
Entropic Fluctuations in Gaussian Dynamical Systems
NASA Astrophysics Data System (ADS)
Jakšić, V.; Pillet, C.-A.; Shirikyan, A.
2016-06-01
We study nonequilibrium statistical mechanics of a Gaussian dynamical system and compute in closed form the large deviation functionals describing the fluctuations of the entropy production observable with respect to the reference state and the nonequilibrium steady state. The entropy production observable of this model is an unbounded function on the phase space, and its large deviation functionals have a surprisingly rich structure. We explore this structure in some detail.
Gaussian Confinement in a Jkj Decay Model
NASA Astrophysics Data System (ADS)
da Silva, Mario L. L.; Hadjimichef, Dimiter; Vasconcellos, Cesar A. Z.
In microscopic decay models, one attempts to describe hadron strong decays in terms of quark and gluon degrees of freedom. We begin by assuming that strong decays are driven by the same interquark Hamiltonian which determines the spectrum, and that it incorporates gaussian confinement. An A → BC decay matrix element of the JKJ Hamiltonian involves a pair-production current matrix elements times a scatering matrix element. Diagrammatically this corresponds to an interaction between an initial line and produced pair.
Non-Markovianity of Gaussian Channels.
Torre, G; Roga, W; Illuminati, F
2015-08-14
We introduce a necessary and sufficient criterion for the non-Markovianity of Gaussian quantum dynamical maps based on the violation of divisibility. The criterion is derived by defining a general vectorial representation of the covariance matrix which is then exploited to determine the condition for the complete positivity of partial maps associated with arbitrary time intervals. Such construction does not rely on the Choi-Jamiolkowski representation and does not require optimization over states. PMID:26317700
Extended Decentralized Linear-Quadratic-Gaussian Control
NASA Technical Reports Server (NTRS)
Carpenter, J. Russell
2000-01-01
A straightforward extension of a solution to the decentralized linear-Quadratic-Gaussian problem is proposed that allows its use for commonly encountered classes of problems that are currently solved with the extended Kalman filter. This extension allows the system to be partitioned in such a way as to exclude the nonlinearities from the essential algebraic relationships that allow the estimation and control to be optimally decentralized.
Microwave Realization of the Gaussian Symplectic Ensemble.
Rehemanjiang, A; Allgaier, M; Joyner, C H; Müller, S; Sieber, M; Kuhl, U; Stöckmann, H-J
2016-08-01
Following an idea by Joyner et al. [Europhys. Lett. 107, 50004 (2014)], a microwave graph with an antiunitary symmetry T obeying T^{2}=-1 is realized. The Kramers doublets expected for such systems are clearly identified and can be lifted by a perturbation which breaks the antiunitary symmetry. The observed spectral level spacings distribution of the Kramers doublets is in agreement with the predictions from the Gaussian symplectic ensemble expected for chaotic systems with such a symmetry. PMID:27541466
Shadows, currents, and AdS fields
Metsaev, R. R.
2008-11-15
Conformal totally symmetric arbitrary spin currents and shadow fields in flat space-time of dimension greater than or equal to four are studied. A gauge invariant formulation for such currents and shadow fields is developed. Gauge symmetries are realized by involving the Stueckelberg fields. A realization of global conformal boost symmetries is obtained. Gauge invariant differential constraints for currents and shadow fields are obtained. AdS/CFT correspondence for currents and shadow fields and the respective normalizable and non-normalizable solutions of massless totally symmetric arbitrary spin AdS fields are studied. The bulk fields are considered in a modified de Donder gauge that leads to decoupled equations of motion. We demonstrate that leftover on shell gauge symmetries of bulk fields correspond to gauge symmetries of boundary currents and shadow fields, while the modified de Donder gauge conditions for bulk fields correspond to differential constraints for boundary conformal currents and shadow fields. Breaking conformal symmetries, we find interrelations between the gauge invariant formulation of the currents and shadow fields, and the gauge invariant formulation of massive fields.
A neural-network based estimator to search for primordial non-Gaussianity in Planck CMB maps
NASA Astrophysics Data System (ADS)
Novaes, C. P.; Bernui, A.; Ferreira, I. S.; Wuensche, C. A.
2015-09-01
We present an upgraded combined estimator, based on Minkowski Functionals and Neural Networks, with excellent performance in detecting primordial non-Gaussianity in simulated maps that also contain a weighted mixture of Galactic contaminations, besides real pixel's noise from Planck cosmic microwave background radiation data. We rigorously test the efficiency of our estimator considering several plausible scenarios for residual non-Gaussianities in the foreground-cleaned Planck maps, with the intuition to optimize the training procedure of the Neural Network to discriminate between contaminations with primordial and secondary non-Gaussian signatures. We look for constraints of primordial local non-Gaussianity at large angular scales in the foreground-cleaned Planck maps. For the SMICA map we found fNL = 33 ± 23, at 1σ confidence level, in excellent agreement with the WMAP-9yr and Planck results. In addition, for the other three Planck maps we obtain similar constraints with values in the interval fNL in [33, 41], concomitant with the fact that these maps manifest distinct features in reported analyses, like having different pixel's noise intensities.
Non-Gaussianity from axionic curvaton
Kawasaki, Masahiro; Kobayashi, Takeshi; Takahashi, Fuminobu E-mail: takeshi@cita.utoronto.ca
2013-03-01
We study non-Gaussianity of density perturbations generated by an axionic curvaton, focusing on the case that the curvaton sits near the hilltop of the potential during inflation. Such hilltop curvatons can generate a red-tilted density perturbation spectrum without invoking large-field inflation. We show that, even when the curvaton dominates the Universe, the non-Gaussianity parameter f{sub NL} is positive and mildly increases towards the hilltop of the curvaton potential, and that f{sub NL} = O(10) is a general and robust prediction of such hilltop axionic curvatons. In particular, we find that the non-Gaussianity parameter is bounded as f{sub NL}∼<30–40 for a range of the scalar spectral index, n{sub s} = 0.94–0.99, and that f{sub NL} = 20–40 is realized for the curvaton mass m{sub σ} = 10–10{sup 6} GeV and the decay constant f = 10{sup 12}–10{sup 17} GeV. One of the plausible candidates for the axionic curvaton is an imaginary component of a modulus field with mass of order 10–100 TeV and decay constant of 10{sup 16–17}GeV. We also discuss extreme cases where the curvaton drives a second inflation and find that f{sub NL} is typically smaller compared to non-inflating cases.
Unitarily localizable entanglement of Gaussian states
Serafini, Alessio; Adesso, Gerardo; Illuminati, Fabrizio
2005-03-01
We consider generic (mxn)-mode bipartitions of continuous-variable systems, and study the associated bisymmetric multimode Gaussian states. They are defined as (m+n)-mode Gaussian states invariant under local mode permutations on the m-mode and n-mode subsystems. We prove that such states are equivalent, under local unitary transformations, to the tensor product of a two-mode state and of m+n-2 uncorrelated single-mode states. The entanglement between the m-mode and the n-mode blocks can then be completely concentrated on a single pair of modes by means of local unitary operations alone. This result allows us to prove that the PPT (positivity of the partial transpose) condition is necessary and sufficient for the separability of (m+n)-mode bisymmetric Gaussian states. We determine exactly their negativity and identify a subset of bisymmetric states whose multimode entanglement of formation can be computed analytically. We consider explicit examples of pure and mixed bisymmetric states and study their entanglement scaling with the number of modes.
Exact Results for `Bouncing' Gaussian Wave Packets
NASA Astrophysics Data System (ADS)
Belloni, M.; Doncheski, M. A.; Robinett, R. W.
2005-01-01
We consider time-dependent Gaussian wave packet solutions of the Schrödinger equation, with arbitrary initial central position, x0, and momentum, p0, for an otherwise free particle, but with an infinite wall at x = 0, so-called bouncing wave packets. We show how difference or mirror solutions of the form ψ(x,t) - ψ(-x,t) can, in this case, be normalized exactly, allowing for the evaluation of a number of time-dependent expectation values and other quantities in closed form. For example, we calculate langp2rangt explicitly which illustrates how the free-particle kinetic (and hence total energy) is affected by the presence of the distant boundary. We also discuss the time dependence of the expectation values of position, langxrangt, and momentum, langprangt, and their relation to the impulsive force during the `collision' with the wall. Finally, the x0, p0 → 0 limit is shown to reduce a special case of a non-standard free-particle Gaussian solution. The addition of this example to the literature then expands of the relatively small number of Gaussian solutions to quantum mechanical problems with familiar classical analogs (free particle, uniform acceleration, harmonic oscillator, unstable oscillator, and uniform magnetic field) available in closed form.
Resonant non-Gaussianity with equilateral properties
Gwyn, Rhiannon; Rummel, Markus; Westphal, Alexander E-mail: markus.rummel@desy.de
2013-04-01
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 resonant N-point functions give a constraint of f{sub NL}∼
Control charts for non-Gaussian distributions
NASA Astrophysics Data System (ADS)
Babus, Florina; Kobi, Abdessamad; Tiplica, Th.; Bacivarov, Ioan; Bacivarov, Angelica
2007-05-01
Traditional statistical process control (SPC) techniques applied in the industrial processes field consider often that the distribution ofdata is Gaussian. The estimation ofparameters, the detection ofthe out oforder situations and the control of the followed characteristics are easy to achieve for the normal populations. In reality, whatever the origin of a characteristic (large series productions for components, mechanical parts of OE communication systems, etc. ) the curve of distributions of the measured values is generally far from being normal. The simple approximation to the Gauss distribution and the use of the classical control methods sometimes induces serious errors. In this paper, a study on the statistical control of non Gaussian populations is presented. Particularly we discuss the Rayleigh and the Weibull distribution as being representatives in (SPC for some category of data. The X control charts with variable limits are tested. Experimental simulations are presented for different parameters of the two distributions. The results confirm the methodology and encourage the research in the field of non Gaussian processes.
Estimation of noise parameters in dynamical system identification with Kalman filters.
Kwasniok, Frank
2012-09-01
A method is proposed for determining dynamical and observational noise parameters in state and parameter identification from time series using Kalman filters. The noise covariances are estimated in a secondary optimization by maximizing the predictive likelihood of the data. The approach is based on internal consistency; for the correct noise parameters, the uncertainty projected by the Kalman filter matches the actual predictive uncertainty. The method is able to disentangle dynamical and observational noise. The algorithm is demonstrated for the linear, extended, and unscented Kalman filters using an Ornstein-Uhlenbeck process, the noise-driven Lorenz system, and van der Pol oscillator as well as a paleoclimatic ice-core record as examples. The approach is also applicable to the ensemble Kalman filter and can be readily extended to non-Gaussian estimation frameworks such as Gaussian-sum filters and particle filters.
Local Gaussian operations can enhance continuous-variable entanglement distillation
Zhang Shengli; Loock, Peter van
2011-12-15
Entanglement distillation is a fundamental building block in long-distance quantum communication. Though known to be useless on their own for distilling Gaussian entangled states, local Gaussian operations may still help to improve non-Gaussian entanglement distillation schemes. Here we show that by applying local squeezing operations both the performance and the efficiency of existing distillation protocols can be enhanced. We find that such an enhancement through local Gaussian unitaries can be obtained even when the initially shared Gaussian entangled states are mixed, as, for instance, after their distribution through a lossy-fiber communication channel.
NASA Technical Reports Server (NTRS)
Hodge, D. C.; Garinther, G. R.
1973-01-01
Noise and blast environments are described, providing a definition of units and techniques of noise measurement and giving representative booster-launch and spacecraft noise data. The effects of noise on hearing sensitivity and performance are reviewed, and community response to noise exposure is discussed. Physiological, or nonauditory, effects of noise exposure are also treated, as are design criteria and methods for minimizing the noise effects of hearing sensitivity and communications. The low level sound detection and speech reception are included, along with subjective and behavioral responses to noise.
Cabin Noise Control for Twin Engine General Aviation Aircraft
NASA Technical Reports Server (NTRS)
Vaicaitis, R.; Slazak, M.
1982-01-01
An analytical model based on modal analysis was developed to predict the noise transmission into a twin-engine light aircraft. The model was applied to optimize the interior noise to an A-weighted level of 85 dBA. To achieve the required noise attenuation, add-on treatments in the form of honeycomb panels, damping tapes, acoustic blankets, septum barriers and limp trim panels were added to the existing structure. The added weight of the noise control treatment is about 1.1 percent of the total gross take-off weight of the aircraft.
Parameter estimation for compact binary inspirals with a simple noise realization
NASA Astrophysics Data System (ADS)
Kim, Jeongcho; Kim, Chunglee; Lee, Hyung Won
2016-05-01
In the context of parameter estimation of gravitational waves (GWs), detector noise is assumed to be Gaussian and stationary. In reality, many electric glitches, which are neither Gaussian nor stationary, were observed and reported in publications by the LSC-Virgo collabotation. Proper noise reduction is important in GW data analysis, as these glitches would limit, if not downgrade, the quality of parameter estimation. In this work, we investigate the accuracy of results obtained by Markov Chain Monte Carlo (MCMC) parameter estimation for compact binary inspirals with the LIGO-Virgo network when non-Gaussian, stationary noise is remained in data of each interferometer. Spiky, delta function-like glitches, which are stationary, do not affect correlations between parameters. However, most likely values of chirp mass and distance seem to be shifted by the specific frequencies and amplitudes of glitches.
Roundoff noise analysis for digital signal power processors using Welch's power spectrum estimation
NASA Technical Reports Server (NTRS)
Chi, Chong-Yung; Long, David; Li, Fuk-Kwok
1987-01-01
The noise due to finite-word-length effects is analyzed for digital-signal power processors using Welch's power-spectrum estimation technique to measure the power of Gaussian random signals over a frequency band of interest. The input of the digital signal processor contains a finite-length time interval in which the true Gaussian signal is contaminated by Gaussian noise. The roundoff noise-to-signal ratio in the measurement of the signal power is derived, and computer simulations which validate the analytical results are presented. These results can be used in tradeoff studies of hardware design, such as the number of bits required at each processing stage. The results presented in this paper are currently being used in the design of a digital Doppler processor (Chi et al., 1986) for a radar scatterometer.
Non-Markovian continuous-time quantum walks on lattices with dynamical noise
NASA Astrophysics Data System (ADS)
Benedetti, Claudia; Buscemi, Fabrizio; Bordone, Paolo; Paris, Matteo G. A.
2016-04-01
We address the dynamics of continuous-time quantum walks on one-dimensional disordered lattices inducing dynamical noise in the system. Noise is described as time-dependent fluctuations of the tunneling amplitudes between adjacent sites, and attention is focused on non-Gaussian telegraph noise, going beyond the usual assumption of fast Gaussian noise. We observe the emergence of two different dynamical behaviors for the walker, corresponding to two opposite noise regimes: slow noise (i.e., strong coupling with the environment) confines the walker into few lattice nodes, while fast noise (weak coupling) induces a transition between quantum and classical diffusion over the lattice. A phase transition between the two dynamical regimes may be observed by tuning the ratio between the autocorrelation time of the noise and the coupling between the walker and the external environment generating the noise. We also address the non-Markovianity of the quantum map by assessing its memory effects, as well as evaluating the information backflow to the system. Our results suggest that the non-Markovian character of the evolution is linked to the dynamical behavior in the slow noise regime, and that fast noise induces a Markovian dynamics for the walker.
Kameyama, Shumpei; Ando, Toshiyuki; Asaka, Kimio; Hirano, Yoshihito
2010-09-20
We present a semianalytic pulsed coherent laser radar (CLR) equation for coaxial and apertured systems. It combines the conventional CLR equation, numerical Fresnel integration (NFI), and nearest Gaussian approximation, using correction factors that correspond to beam truncation. The range dependence of the signal-to-noise ratio obtained by this semianalytic equation was found to agree well with the precise NFI solution for not only the focal range, but also the near-field range. Furthermore, the optimum beam truncation condition depending on the atmospheric refractive index structure constant is shown. The derived equation is useful for precisely predicting the CLR performance simply by its semianalytic expression.
Stochastic geometry and topology of non-Gaussian fields
Beuman, Thomas H.; Turner, Ari M.; Vitelli, Vincenzo
2012-01-01
Gaussian random fields pervade all areas of science. However, it is often the departures from Gaussianity that carry the crucial signature of the nonlinear mechanisms at the heart of diverse phenomena, ranging from structure formation in condensed matter and cosmology to biomedical imaging. The standard test of non-Gaussianity is to measure higher-order correlation functions. In the present work, we take a different route. We show how geometric and topological properties of Gaussian fields, such as the statistics of extrema, are modified by the presence of a non-Gaussian perturbation. The resulting discrepancies give an independent way to detect and quantify non-Gaussianities. In our treatment, we consider both local and nonlocal mechanisms that generate non-Gaussian fields, both statically and dynamically through nonlinear diffusion. PMID:23169625
Jet Noise Physics and Modeling Using First-principles Simulations
NASA Technical Reports Server (NTRS)
Freund, Jonathan B.
2003-01-01
An extensive analysis of our jet DNS database has provided for the first time the complex correlations that are the core of many statistical jet noise models, including MGBK. We have also for the first time explicitly computed the noise from different components of a commonly used noise source as proposed in many modeling approaches. Key findings are: (1) While two-point (space and time) velocity statistics are well-fitted by decaying exponentials, even for our low-Reynolds-number jet, spatially integrated fourth-order space/retarded-time correlations, which constitute the noise "source" in MGBK, are instead well-fitted by Gaussians. The width of these Gaussians depends (by a factor of 2) on which components are considered. This is counter to current modeling practice, (2) A standard decomposition of the Lighthill source is shown by direct evaluation to be somewhat artificial since the noise from these nominally separate components is in fact highly correlated. We anticipate that the same will be the case for the Lilley source, and (3) The far-field sound is computed in a way that explicitly includes all quadrupole cancellations, yet evaluating the Lighthill integral for only a small part of the jet yields a far-field noise far louder than that from the whole jet due to missing nonquadrupole cancellations. Details of this study are discussed in a draft of a paper included as appendix A.
Noise distribution and denoising of current density images
Beheshti, Mohammadali; Foomany, Farbod H.; Magtibay, Karl; Jaffray, David A.; Krishnan, Sridhar; Nanthakumar, Kumaraswamy; Umapathy, Karthikeyan
2015-01-01
Abstract. Current density imaging (CDI) is a magnetic resonance (MR) imaging technique that could be used to study current pathways inside the tissue. The current distribution is measured indirectly as phase changes. The inherent noise in the MR imaging technique degrades the accuracy of phase measurements leading to imprecise current variations. The outcome can be affected significantly, especially at a low signal-to-noise ratio (SNR). We have shown the residual noise distribution of the phase to be Gaussian-like and the noise in CDI images approximated as a Gaussian. This finding matches experimental results. We further investigated this finding by performing comparative analysis with denoising techniques, using two CDI datasets with two different currents (20 and 45 mA). We found that the block-matching and three-dimensional (BM3D) technique outperforms other techniques when applied on current density (J). The minimum gain in noise power by BM3D applied to J compared with the next best technique in the analysis was found to be around 2 dB per pixel. We characterize the noise profile in CDI images and provide insights on the performance of different denoising techniques when applied at two different stages of current density reconstruction. PMID:26158100
NASA Astrophysics Data System (ADS)
Adesso, Gerardo; Serafini, Alessio; Illuminati, Fabrizio
2006-03-01
We present a complete analysis of the multipartite entanglement of three-mode Gaussian states of continuous-variable systems. We derive standard forms which characterize the covariance matrix of pure and mixed three-mode Gaussian states up to local unitary operations, showing that the local entropies of pure Gaussian states are bound to fulfill a relationship which is stricter than the general Araki-Lieb inequality. Quantum correlations can be quantified by a proper convex roof extension of the squared logarithmic negativity, the continuous-variable tangle, or contangle. We review and elucidate in detail the proof that in multimode Gaussian states the contangle satisfies a monogamy inequality constraint [G. Adesso and F. Illuminati, New J. Phys8, 15 (2006)]. The residual contangle, emerging from the monogamy inequality, is an entanglement monotone under Gaussian local operations and classical communications and defines a measure of genuine tripartite entanglements. We determine the analytical expression of the residual contangle for arbitrary pure three-mode Gaussian states and study in detail the distribution of quantum correlations in such states. This analysis yields that pure, symmetric states allow for a promiscuous entanglement sharing, having both maximum tripartite entanglement and maximum couplewise entanglement between any pair of modes. We thus name these states GHZ/W states of continuous-variable systems because they are simultaneous continuous-variable counterparts of both the GHZ and the W states of three qubits. We finally consider the effect of decoherence on three-mode Gaussian states, studying the decay of the residual contangle. The GHZ/W states are shown to be maximally robust against losses and thermal noise.
Adesso, Gerardo; Serafini, Alessio; Illuminati, Fabrizio
2006-03-15
We present a complete analysis of the multipartite entanglement of three-mode Gaussian states of continuous-variable systems. We derive standard forms which characterize the covariance matrix of pure and mixed three-mode Gaussian states up to local unitary operations, showing that the local entropies of pure Gaussian states are bound to fulfill a relationship which is stricter than the general Araki-Lieb inequality. Quantum correlations can be quantified by a proper convex roof extension of the squared logarithmic negativity, the continuous-variable tangle, or contangle. We review and elucidate in detail the proof that in multimode Gaussian states the contangle satisfies a monogamy inequality constraint [G. Adesso and F. Illuminati, New J. Phys8, 15 (2006)]. The residual contangle, emerging from the monogamy inequality, is an entanglement monotone under Gaussian local operations and classical communications and defines a measure of genuine tripartite entanglements. We determine the analytical expression of the residual contangle for arbitrary pure three-mode Gaussian states and study in detail the distribution of quantum correlations in such states. This analysis yields that pure, symmetric states allow for a promiscuous entanglement sharing, having both maximum tripartite entanglement and maximum couplewise entanglement between any pair of modes. We thus name these states GHZ/W states of continuous-variable systems because they are simultaneous continuous-variable counterparts of both the GHZ and the W states of three qubits. We finally consider the effect of decoherence on three-mode Gaussian states, studying the decay of the residual contangle. The GHZ/W states are shown to be maximally robust against losses and thermal noise.
Airframe Noise Prediction Using the Sngr Method
NASA Astrophysics Data System (ADS)
Chen, Rongqian; Wu, Yizhao; Xia, Jian
In this paper, the Stochastic Noise Generation and Radiation method (SNGR) is used to predict airframe noise. The SNGR method combines a stochastic model with Computational Fluid Dynamics (CFD), and it can give acceptable noise results while the computation cost is relatively low. In the method, the time-averaged mean flow field is firstly obtained by solving Reynolds Averaged Navier-Stokes equations (RANS), and a stochastic velocity is generated based on the obtained information. Then the turbulent field is used to generate the source for the Acoustic Perturbation Equations (APEs) that simulate the noise propagation. For numerical methods, timeaveraged RANS equations are solved by finite volume method, and the turbulent model is K - ɛ model; APEs are solved by finite difference method, and the numerical scheme is the Dispersion-Relation-Preserving (DRP) scheme, with explicit optimized 5-stage Rung-Kutta scheme time step. In order to test the APE solver, propagation of a Gaussian pulse in a uniform mean flow is firstly simulated and compared with the analytical solution. Then, using the method, the trailing edge noise of NACA0012 airfoil is calculated. The results are compared with reference data, and good agreements are demonstrated.
Gaussian process regression for sensor networks under localization uncertainty
Jadaliha, M.; Xu, Yunfei; Choi, Jongeun; Johnson, N.S.; Li, Weiming
2013-01-01
In this paper, we formulate Gaussian process regression with observations under the localization uncertainty due to the resource-constrained sensor networks. In our formulation, effects of observations, measurement noise, localization uncertainty, and prior distributions are all correctly incorporated in the posterior predictive statistics. The analytically intractable posterior predictive statistics are proposed to be approximated by two techniques, viz., Monte Carlo sampling and Laplace's method. Such approximation techniques have been carefully tailored to our problems and their approximation error and complexity are analyzed. Simulation study demonstrates that the proposed approaches perform much better than approaches without considering the localization uncertainty properly. Finally, we have applied the proposed approaches on the experimentally collected real data from a dye concentration field over a section of a river and a temperature field of an outdoor swimming pool to provide proof of concept tests and evaluate the proposed schemes in real situations. In both simulation and experimental results, the proposed methods outperform the quick-and-dirty solutions often used in practice.
Noise-induced sensitization of human brain
NASA Astrophysics Data System (ADS)
Yamamoto, Yoshiharu; Hidaka, Ichiro; Nozaki, Daichi; Iso-o, Noriko; Soma, Rika; Kwak, Shin
2002-11-01
In the past decade, it has been recognized that noise can enhance the response of nonlinear systems to weak signals, via a mechanism known as stochastic resonance (SR). Particularly, the concept of SR has generated considerable interest in sensory biology, because it has been shown in several experimental studies that noise can assist neural systems in detecting weak signals which could not be detected in its absence. Recently, we have shown a similar type of noise-induced sensitization of human brain; externally added noise to the brain stem baroreflex centers sensitized their responses in maintaining adequate blood perfusion to the brain itself. Furthermore, the addition of noise has also shown to be useful in compensating for dysfunctions of the baroreflex centers in certain neurological diseases. It is concluded that the statistical physics concept of SR could be useful in sensitizing human brain in health and disease.
Assessing the efficacy of active noise reduction
NASA Astrophysics Data System (ADS)
Rylands, Julia M.
Active noise reduction (ANR) is an electronic technique, based on reverse phase cancellation, for reducing low frequency noise reaching an operators ears. This report discussed the basic concept, its capabilities and some approaches to assessing its efficacy. The technique provides a great enhancement to hearing protection and also enhances signal detection and communications capabilities. Tests of detectibility of pure tones at frequencies ranging up to 1750 Hz using ANR systems which had maximum noise attenuation between 300 and 600 Hz and masking noise typical of the SeaKing helicopter showed that improvements in detection performance extend up to 1000 Hz. ANR systems also offer improved speech intelligibility in high noise environments by reducing the upward spread of masking and adding speech pre-emphasis.
Aircraft and airport noise control prospective outlook
Shapiro, N.
1982-01-01
In a perspective look at aircraft and airport noise control over the past ten years or more - or more is added here because the Federal Aviation Regulation Part 36 of 1969 is a more significant milestone for the air transportation system than is the Noise Control Act of 1972 - we see an appreciable reduction in the noise emitted by newly designed and newly produced airplanes, particularly those powered by the new high bypass engines, but only, at best, a moderate alleviation of airport noise. The change in airport noise exposure was the consequence of the introduction of some new, quieter airplanes into the airlines fleets and some operational modifications or restrictions at the airports.
Noise, Health, and Architecture.
ERIC Educational Resources Information Center
Beranek, Leo L.
There is reasonable agreement that hearing impairment is related to noise exposure. This hearing loss due to noise is considered a serious health injury, but there is still difficulty in delineating the importance of noise related to people's general non-auditory well-being and health. Beside hearing loss, noise inhibits satisfactory speech…
NASA Technical Reports Server (NTRS)
Yu, Yung H.; Schmitz, Frederic H.; Morse, Andrew H.
1991-01-01
Progress in aeroacoustical theory and experiments reviewed. Report summarizes continuing U.S. Army programs of research into causes of noise generated by helicopters. Topics of study include high-speed impulsive noise, blade/vortex-interaction noise, and low-frequency harmonic noise.
Active Noise Control for Dishwasher noise
NASA Astrophysics Data System (ADS)
Lee, Nokhaeng; Park, Youngjin
2016-09-01
The dishwasher is a useful home appliance and continually used for automatically washing dishes. It's commonly placed in the kitchen with built-in style for practicality and better use of space. In this environment, people are easily exposed to dishwasher noise, so it is an important issue for the consumers, especially for the people living in open and narrow space. Recently, the sound power levels of the noise are about 40 - 50 dBA. It could be achieved by removal of noise sources and passive means of insulating acoustical path. For more reduction, such a quiet mode with the lower speed of cycle has been introduced, but this deteriorates the washing capacity. Under this background, we propose active noise control for dishwasher noise. It is observed that the noise is propagating mainly from the lower part of the front side. Control speakers are placed in the part for the collocation. Observation part of estimating sound field distribution and control part of generating the anti-noise are designed for active noise control. Simulation result shows proposed active noise control scheme could have a potential application for dishwasher noise reduction.
NASA Astrophysics Data System (ADS)
Pires, Carlos A. L.; Perdigão, Rui A. P.
2016-04-01
Hydroclimatic spatiotemporal distributions exhibit significant non-Gaussianity with particular emphasis to overweight extremes, rendering their diagnostic and inference suboptimal with traditional statistical techniques. In order to overcome that limitation, we introduce and discuss a set of information-theoretic methodologies for statistical diagnostic and inference issued from exploratory variables of the general atmospheric and oceanic circulation in the cases of non-Gaussian joint probability distributions. Moreover, the nonlinear information among various large-scale ocean-atmospheric processes is explored, bringing out added predictability to elusive weather and hydrologic extremes relative to the current state of the art in nonlinear geophysics. The methodologies are illustrated with the analysis and prediction of resonant ocean-atmospheric thermodynamic anomaly spells underneath high-profile floods and droughts.
Optimizing Electromagnetically Induced Transparency Signals with Laguerre-Gaussian Beams
NASA Astrophysics Data System (ADS)
Holtfrerich, Matthew; Akin, Tom; Krzyzewski, Sean; Marino, Alberto; Abraham, Eric
2016-05-01
We have performed electromagnetically induced transparency in ultracold Rubidium atoms using a Laguerre-Gaussian laser mode as the control beam. Laguerre-Gaussian modes are characterized by a ring type transverse intensity profile and carry intrinsic orbital angular momentum. This angular momentum carried by the control beam can be utilized in optical computing applications which is unavailable to the more common Gaussian laser field. Specifically, we use a Laguerre-Gaussian control beam with a Gaussian probe to show that the linewidth of the transmission spectrum can be narrowed when compared to a Gaussian control beam that has the same peak intensity. We present data extending this work to compare control fields in both the Gaussian and Laguerre-Gaussian modes with constant total power. We have made efforts to find the optical overlap that best minimizes the transmission linewidth while also maintaining signal contrast. This was done by changing the waist size of the control beam with respect to the probe. The best results were obtained when the waist of a Laguerre-Gaussian control beam is equal to the waist of the Gaussian probe resulting in narrow linewidth features.
Non-Gaussian Diffusion Imaging for Enhanced Contrast of Brain Tissue Affected by Ischemic Stroke
Geffroy, Françoise; Le Bihan, Denis; Shah, N. Jon
2014-01-01
Recent diffusion MRI studies of stroke in humans and animals have shown that the quantitative parameters characterising the degree of non-Gaussianity of the diffusion process are much more sensitive to ischemic changes than the apparent diffusion coefficient (ADC) considered so far as the “gold standard”. The observed changes exceeded that of the ADC by a remarkable factor of 2 to 3. These studies were based on the novel non-Gaussian methods, such as diffusion kurtosis imaging (DKI) and log-normal distribution function imaging (LNDFI). As shown in our previous work investigating the animal stroke model, a combined analysis using two methods, DKI and LNDFI provides valuable complimentary information. In the present work, we report the application of three non-Gaussian diffusion models to quantify the deviations from the Gaussian behaviour in stroke induced by transient middle cerebral artery occlusion in rat brains: the gamma-distribution function (GDF), the stretched exponential model (SEM), and the biexponential model. The main goal was to compare the sensitivity of various non-Gaussian metrics to ischemic changes and to investigate if a combined application of several models will provide added value in the assessment of stroke. We have shown that two models, GDF and SEM, exhibit a better performance than the conventional method and allow for a significantly enhanced visualization of lesions. Furthermore, we showed that valuable information regarding spatial properties of stroke lesions can be obtained. In particular, we observed a stratified cortex structure in the lesions that were well visible in the maps of the GDF and SEM metrics, but poorly distinguishable in the ADC-maps. Our results provided evidence that cortical layers tend to be differently affected by ischemic processes. PMID:24586610
Non-Gaussian Photon Probability Distribution
NASA Astrophysics Data System (ADS)
Solomon, Benjamin T.
2010-01-01
This paper investigates the axiom that the photon's probability distribution is a Gaussian distribution. The Airy disc empirical evidence shows that the best fit, if not exact, distribution is a modified Gamma mΓ distribution (whose parameters are α = r, βr/√u ) in the plane orthogonal to the motion of the photon. This modified Gamma distribution is then used to reconstruct the probability distributions along the hypotenuse from the pinhole, arc from the pinhole, and a line parallel to photon motion. This reconstruction shows that the photon's probability distribution is not a Gaussian function. However, under certain conditions, the distribution can appear to be Normal, thereby accounting for the success of quantum mechanics. This modified Gamma distribution changes with the shape of objects around it and thus explains how the observer alters the observation. This property therefore places additional constraints to quantum entanglement experiments. This paper shows that photon interaction is a multi-phenomena effect consisting of the probability to interact Pi, the probabilistic function and the ability to interact Ai, the electromagnetic function. Splitting the probability function Pi from the electromagnetic function Ai enables the investigation of the photon behavior from a purely probabilistic Pi perspective. The Probabilistic Interaction Hypothesis is proposed as a consistent method for handling the two different phenomena, the probability function Pi and the ability to interact Ai, thus redefining radiation shielding, stealth or cloaking, and invisibility as different effects of a single phenomenon Pi of the photon probability distribution. Sub wavelength photon behavior is successfully modeled as a multi-phenomena behavior. The Probabilistic Interaction Hypothesis provides a good fit to Otoshi's (1972) microwave shielding, Schurig et al. (2006) microwave cloaking, and Oulton et al. (2008) sub wavelength confinement; thereby providing a strong case that
Monthly streamflow forecasting using Gaussian Process Regression
NASA Astrophysics Data System (ADS)
Sun, Alexander Y.; Wang, Dingbao; Xu, Xianli
2014-04-01
Streamflow forecasting plays a critical role in nearly all aspects of water resources planning and management. In this work, Gaussian Process Regression (GPR), an effective kernel-based machine learning algorithm, is applied to probabilistic streamflow forecasting. GPR is built on Gaussian process, which is a stochastic process that generalizes multivariate Gaussian distribution to infinite-dimensional space such that distributions over function values can be defined. The GPR algorithm provides a tractable and flexible hierarchical Bayesian framework for inferring the posterior distribution of streamflows. The prediction skill of the algorithm is tested for one-month-ahead prediction using the MOPEX database, which includes long-term hydrometeorological time series collected from 438 basins across the U.S. from 1948 to 2003. Comparisons with linear regression and artificial neural network models indicate that GPR outperforms both regression methods in most cases. The GPR prediction of MOPEX basins is further examined using the Budyko framework, which helps to reveal the close relationships among water-energy partitions, hydrologic similarity, and predictability. Flow regime modification and the resulting loss of predictability have been a major concern in recent years because of climate change and anthropogenic activities. The persistence of streamflow predictability is thus examined by extending the original MOPEX data records to 2012. Results indicate relatively strong persistence of streamflow predictability in the extended period, although the low-predictability basins tend to show more variations. Because many low-predictability basins are located in regions experiencing fast growth of human activities, the significance of sustainable development and water resources management can be even greater for those regions.
NASA Technical Reports Server (NTRS)
Clauson, J.; Heuser, J.
1981-01-01
The Applications Data Service (ADS) is a system based on an electronic data communications network which will permit scientists to share the data stored in data bases at universities and at government and private installations. It is designed to allow users to readily locate and access high quality, timely data from multiple sources. The ADS Pilot program objectives and the current plans for accomplishing those objectives are described.
Boson sampling from a Gaussian state.
Lund, A P; Laing, A; Rahimi-Keshari, S; Rudolph, T; O'Brien, J L; Ralph, T C
2014-09-01
We pose a randomized boson-sampling problem. Strong evidence exists that such a problem becomes intractable on a classical computer as a function of the number of bosons. We describe a quantum optical processor that can solve this problem efficiently based on a Gaussian input state, a linear optical network, and nonadaptive photon counting measurements. All the elements required to build such a processor currently exist. The demonstration of such a device would provide empirical evidence that quantum computers can, indeed, outperform classical computers and could lead to applications. PMID:25238340
Non-gaussianity from broken symmetries
Kolb, Edward W.; Riotto, Antonio; Vallinotto, Alberto; /Chicago U. /Fermilab
2005-11-01
Recently we studied inflation models in which the inflation potential is characterized by an underlying approximate global symmetry. In the first work we pointed out that in such a model curvature perturbations are generated after the end of the slow-roll phase of inflation. In this work we develop further the observational implications of the model and compute the degree of non-Gaussianity predicted in the scenario. We find that the corresponding nonlinearity parameter, F{sub NL}, can be as large as 10{sup 2}.
Negative Gaussian curvature from induced metric changes
NASA Astrophysics Data System (ADS)
Modes, Carl D.; Warner, Mark
2015-07-01
We revisit the light or heat-induced changes in topography of initially flat sheets of a solid that elongate or contract along patterned in-plane director fields. For radial or azimuthal directors, negative Gaussian curvature is generated—so-called "anticones." We show that azimuthal material displacements are required for the distorted state to be stretch free and bend minimizing. The resultant shapes are smooth and asterlike and can become reentrant in the azimuthal coordinate for large deformations. We show that care is needed when considering elastomers rather than glasses, although the former offer huge deformations.
Video compressive sensing using Gaussian mixture models.
Yang, Jianbo; Yuan, Xin; Liao, Xuejun; Llull, Patrick; Brady, David J; Sapiro, Guillermo; Carin, Lawrence
2014-11-01
A Gaussian mixture model (GMM)-based algorithm is proposed for video reconstruction from temporally compressed video measurements. The GMM is used to model spatio-temporal video patches, and the reconstruction can be efficiently computed based on analytic expressions. The GMM-based inversion method benefits from online adaptive learning and parallel computation. We demonstrate the efficacy of the proposed inversion method with videos reconstructed from simulated compressive video measurements, and from a real compressive video camera. We also use the GMM as a tool to investigate adaptive video compressive sensing, i.e., adaptive rate of temporal compression.
Signal Partitioning Algorithm for Highly Efficient Gaussian Mixture Modeling in Mass Spectrometry
Polanski, Andrzej; Marczyk, Michal; Pietrowska, Monika; Widlak, Piotr; Polanska, Joanna
2015-01-01
Mixture - modeling of mass spectra is an approach with many potential applications including peak detection and quantification, smoothing, de-noising, feature extraction and spectral signal compression. However, existing algorithms do not allow for automated analyses of whole spectra. Therefore, despite highlighting potential advantages of mixture modeling of mass spectra of peptide/protein mixtures and some preliminary results presented in several papers, the mixture modeling approach was so far not developed to the stage enabling systematic comparisons with existing software packages for proteomic mass spectra analyses. In this paper we present an efficient algorithm for Gaussian mixture modeling of proteomic mass spectra of different types (e.g., MALDI-ToF profiling, MALDI-IMS). The main idea is automated partitioning of protein mass spectral signal into fragments. The obtained fragments are separately decomposed into Gaussian mixture models. The parameters of the mixture models of fragments are then aggregated to form the mixture model of the whole spectrum. We compare the elaborated algorithm to existing algorithms for peak detection and we demonstrate improvements of peak detection efficiency obtained by using Gaussian mixture modeling. We also show applications of the elaborated algorithm to real proteomic datasets of low and high resolution. PMID:26230717
Probabilistic inference using linear Gaussian importance sampling for hybrid Bayesian networks
NASA Astrophysics Data System (ADS)
Sun, Wei; Chang, K. C.
2005-05-01
Probabilistic inference for Bayesian networks is in general NP-hard using either exact algorithms or approximate methods. However, for very complex networks, only the approximate methods such as stochastic sampling could be used to provide a solution given any time constraint. There are several simulation methods currently available. They include logic sampling (the first proposed stochastic method for Bayesian networks, the likelihood weighting algorithm) the most commonly used simulation method because of its simplicity and efficiency, the Markov blanket scoring method, and the importance sampling algorithm. In this paper, we first briefly review and compare these available simulation methods, then we propose an improved importance sampling algorithm called linear Gaussian importance sampling algorithm for general hybrid model (LGIS). LGIS is aimed for hybrid Bayesian networks consisting of both discrete and continuous random variables with arbitrary distributions. It uses linear function and Gaussian additive noise to approximate the true conditional probability distribution for continuous variable given both its parents and evidence in a Bayesian network. One of the most important features of the newly developed method is that it can adaptively learn the optimal important function from the previous samples. We test the inference performance of LGIS using a 16-node linear Gaussian model and a 6-node general hybrid model. The performance comparison with other well-known methods such as Junction tree (JT) and likelihood weighting (LW) shows that LGIS-GHM is very promising.
Robust Lee local statistic filter for removal of mixed multiplicative and impulse noise
NASA Astrophysics Data System (ADS)
Ponomarenko, Nikolay N.; Lukin, Vladimir V.; Egiazarian, Karen O.; Astola, Jaakko T.
2004-05-01
A robust version of Lee local statistic filter able to effectively suppress the mixed multiplicative and impulse noise in images is proposed. The performance of the proposed modification is studied for a set of test images, several values of multiplicative noise variance, Gaussian and Rayleigh probability density functions of speckle, and different characteris-tics of impulse noise. The advantages of the designed filter in comparison to the conventional Lee local statistic filter and some other filters able to cope with mixed multiplicative+impulse noise are demonstrated.
SU-E-QI-17: Dependence of 3D/4D PET Quantitative Image Features On Noise
Oliver, J; Budzevich, M; Zhang, G; Latifi, K; Dilling, T; Balagurunathan, Y; Gu, Y; Grove, O; Feygelman, V; Gillies, R; Moros, E; Lee, H.
2014-06-15
Purpose: Quantitative imaging is a fast evolving discipline where a large number of features are extracted from images; i.e., radiomics. Some features have been shown to have diagnostic, prognostic and predictive value. However, they are sensitive to acquisition and processing factors; e.g., noise. In this study noise was added to positron emission tomography (PET) images to determine how features were affected by noise. Methods: Three levels of Gaussian noise were added to 8 lung cancer patients PET images acquired in 3D mode (static) and using respiratory tracking (4D); for the latter images from one of 10 phases were used. A total of 62 features: 14 shape, 19 intensity (1stO), 18 GLCM textures (2ndO; from grey level co-occurrence matrices) and 11 RLM textures (2ndO; from run-length matrices) features were extracted from segmented tumors. Dimensions of GLCM were 256×256, calculated using 3D images with a step size of 1 voxel in 13 directions. Grey levels were binned into 256 levels for RLM and features were calculated in all 13 directions. Results: Feature variation generally increased with noise. Shape features were the most stable while RLM were the most unstable. Intensity and GLCM features performed well; the latter being more robust. The most stable 1stO features were compactness, maximum and minimum length, standard deviation, root-mean-squared, I30, V10-V90, and entropy. The most stable 2ndO features were entropy, sum-average, sum-entropy, difference-average, difference-variance, difference-entropy, information-correlation-2, short-run-emphasis, long-run-emphasis, and run-percentage. In general, features computed from images from one of the phases of 4D scans were more stable than from 3D scans. Conclusion: This study shows the need to characterize image features carefully before they are used in research and medical applications. It also shows that the performance of features, and thereby feature selection, may be assessed in part by noise analysis.
Helicopter rotor trailing edge noise. [noise prediction
NASA Technical Reports Server (NTRS)
Schlinker, R. H.; Amier, R. K.
1981-01-01
A two dimensional section of a helicopter main rotor blade was tested in an acoustic wind tunnel at close to full-scale Reynolds numbers to obtain boundary layer data and acoustic data for use in developing an acoustic scaling law and testing a first principles trailing edge noise theory. Results were extended to the rotating frame coordinate system to develop a helicopter rotor trailing edge noise prediction. Comparisons of the calculated noise levels with helicopter flyover spectra demonstrate that trailing edge noise contributes significantly to the total helicopter noise spectrum at high frequencies. This noise mechanism is expected to control the minimum rotor noise. In the case of noise radiation from a local blade segment, the acoustic directivity pattern is predicted by the first principles trailing edge noise theory. Acoustic spectra are predicted by a scaling law which includes Mach number, boundary layer thickness and observer position. Spectrum shape and sound pressure level are also predicted by the first principles theory but the analysis does not predict the Strouhal value identifying the spectrum peak.
Gaussian and non-Gaussian inverse modeling of groundwater flow using copulas and random mixing
NASA Astrophysics Data System (ADS)
Bárdossy, András.; Hörning, Sebastian
2016-06-01
This paper presents a new copula-based methodology for Gaussian and non-Gaussian inverse modeling of groundwater flow. The presented approach is embedded in a Monte Carlo framework and it is based on the concept of mixing spatial random fields where a spatial copula serves as spatial dependence function. The target conditional spatial distribution of hydraulic transmissivities is obtained as a linear combination of unconditional spatial fields. The corresponding weights of this linear combination are chosen such that the combined field has the prescribed spatial variability, and honors all the observations of hydraulic transmissivities. The constraints related to hydraulic head observations are nonlinear. In order to fulfill these constraints, a connected domain in the weight space, inside which all linear constraints are fulfilled, is identified. This domain is defined analytically and includes an infinite number of conditional fields (i.e., conditioned on the observed hydraulic transmissivities), and the nonlinear constraints can be fulfilled via minimization of the deviation of the modeled and the observed hydraulic heads. This procedure enables the simulation of a great number of solutions for the inverse problem, allowing a reasonable quantification of the associated uncertainties. The methodology can be used for fields with Gaussian copula dependence, and fields with specific non-Gaussian copula dependence. Further, arbitrary marginal distributions can be considered.
Evading Vacuum Noise: Wigner Projections or Husimi Samples?
NASA Astrophysics Data System (ADS)
Müller, C. R.; Peuntinger, C.; Dirmeier, T.; Khan, I.; Vogl, U.; Marquardt, Ch.; Leuchs, G.; Sánchez-Soto, L. L.; Teo, Y. S.; Hradil, Z.; Řeháček, J.
2016-08-01
The accuracy in determining the quantum state of a system depends on the type of measurement performed. Homodyne and heterodyne detection are the two main schemes in continuous-variable quantum information. The former leads to a direct reconstruction of the Wigner function of the state, whereas the latter samples its Husimi Q function. We experimentally demonstrate that heterodyne detection outperforms homodyne detection for almost all Gaussian states, the details of which depend on the squeezing strength and thermal noise.
Evading Vacuum Noise: Wigner Projections or Husimi Samples?
Müller, C R; Peuntinger, C; Dirmeier, T; Khan, I; Vogl, U; Marquardt, Ch; Leuchs, G; Sánchez-Soto, L L; Teo, Y S; Hradil, Z; Řeháček, J
2016-08-12
The accuracy in determining the quantum state of a system depends on the type of measurement performed. Homodyne and heterodyne detection are the two main schemes in continuous-variable quantum information. The former leads to a direct reconstruction of the Wigner function of the state, whereas the latter samples its Husimi Q function. We experimentally demonstrate that heterodyne detection outperforms homodyne detection for almost all Gaussian states, the details of which depend on the squeezing strength and thermal noise. PMID:27563944
NASA Astrophysics Data System (ADS)
Sato, Masanori; Nishimichi, Takahiro
2013-06-01
We study how well the Gaussian approximation is valid for computing the covariance matrices of the convergence power and bispectrum in weak gravitational lensing analyses. We focus on its impact on the cosmological parameter estimations by comparing the results with and without non-Gaussian error contribution in the covariance matrix. We numerically derive the covariance matrix as well as the cosmology dependence of the spectra from a large set of N-body simulations performed for various cosmologies and carry out Fisher matrix forecasts for tomographic weak lensing surveys with three source redshifts. After showing the consistency of the power and bispectra measured from our simulations with the state-of-the-art fitting formulas, we investigate the covariance matrix assuming a typical ongoing survey across 1500deg2 with the mean source number density of 30arcmin-2 at the mean redshift zs=1.0. Although the shape noise contributes a significant fraction to the total error budget and it mitigates the impact of the non-Gaussian error for this source number density, we find that the non-Gaussian error degrades the cumulative signal-to-noise ratio up to the maximum multipole of 2000 by a factor of about 2 (3) in the power (bi-) spectrum analysis. Its impact on the final cosmological parameter forecast with 6 parameters can be as large as 15% in the size of the one-dimensional statistical error. This can be a problem in future wide and deep weak lensing surveys for precision cosmology. We also show how much the dark energy figure of merit is affected by the non-Gaussian error contribution and demonstrate an optimal survey design with a fixed observational time.
NASA Astrophysics Data System (ADS)
Chen, Ying; Lo, Joseph Y.; Baker, Jay A.; Dobbins, James T., III
2006-03-01
Breast cancer is a major problem and the most common cancer among women. The nature of conventional mammpgraphy makes it very difficult to distinguish a cancer from overlying breast tissues. Digital Tomosynthesis refers to a three-dimensional imaging technique that allows reconstruction of an arbitrary set of planes in the breast from limited-angle series of projection images as the x-ray source moves. Several tomosynthesis algorithms have been proposed, including Matrix Inversion Tomosynthesis (MITS) and Filtered Back Projection (FBP) that have been investigated in our lab. MITS shows better high frequency response in removing out-of-plane blur, while FBP shows better low frequency noise propertities. This paper presents an effort to combine MITS and FBP for better breast tomosynthesis reconstruction. A high-pass Gaussian filter was designed and applied to three-slice "slabbing" MITS reconstructions. A low-pass Gaussian filter was designed and applied to the FBP reconstructions. A frequency weighting parameter was studied to blend the high-passed MITS with low-passed FBP frequency components. Four different reconstruction methods were investigated and compared with human subject images: 1) MITS blended with Shift-And-Add (SAA), 2) FBP alone, 3) FBP with applied Hamming and Gaussian Filters, and 4) Gaussian Frequency Blending (GFB) of MITS and FBP. Results showed that, compared with FBP, Gaussian Frequency Blending (GFB) has better performance for high frequency content such as better reconstruction of micro-calcifications and removal of high frequency noise. Compared with MITS, GFB showed more low frequency breast tissue content.
Kim, Hyeon Sik; Cho, Sang-Geon; Kim, Ju Han; Kwon, Seong Young; Lee, Byeong-il; Bom, Hee-Seung
2014-01-01
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 (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. 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. PMID:27408866
Hierarchical similarity transformations between Gaussian mixtures.
Rigas, George; Nikou, Christophoros; Goletsis, Yorgos; Fotiadis, Dimitrios I
2013-11-01
In this paper, we propose a method to estimate the density of a data space represented by a geometric transformation of an initial Gaussian mixture model. The geometric transformation is hierarchical, and it is decomposed into two steps. At first, the initial model is assumed to undergo a global similarity transformation modeled by translation, rotation, and scaling of the model components. Then, to increase the degrees of freedom of the model and allow it to capture fine data structures, each individual mixture component may be transformed by another, local similarity transformation, whose parameters are distinct for each component of the mixture. In addition, to constrain the order of magnitude of the local transformation (LT) with respect to the global transformation (GT), zero-mean Gaussian priors are imposed onto the local parameters. The estimation of both GT and LT parameters is obtained through the expectation maximization framework. Experiments on artificial data are conducted to evaluate the proposed model, with varying data dimensionality, number of model components, and transformation parameters. In addition, the method is evaluated using real data from a speech recognition task. The obtained results show a high model accuracy and demonstrate the potential application of the proposed method to similar classification problems. PMID:24808615
Rogue Waves in Near Gaussian Sea States
NASA Astrophysics Data System (ADS)
Osborne, Alfred R.
2015-04-01
The field of nonlinear waves often emphasizes the importance of small amplitude modulations in the nonlinear Schroedinger equation (NLS). The Akhmediev and Peregrine breather trains are examples which manifest themselves from the usual linear instability analyses of NLS. In reality, however, oceanic sea states generated by wind waves are very nearly Gaussian processes and so the modulus of the Hilbert transform envelope is approximately Rayleigh distributed (with of course the possibility of a large amplitude tail) and is therefore never a small amplitude modulation. How can we then reconcile our usual perceptions with this fact? What are indeed the solutions of the nonlinear Schroedinger equation non Gaussianity have on the actual types of solutions that are likely to occur in the real ocean? I discuss how finite gap theory for NLS allows us to answer these and many more questions about rogue sea states. I analyze data from various laboratory and oceanic experiments to illustrate the method. Finally, I discuss whether breather trains such as Akhmediev, Peregrine and Ma-Kuznetsov can actually occur in ocean wave data.
Scaling Multidimensional Inference for Structured Gaussian Processes.
Gilboa, Elad; Saatçi, Yunus; Cunningham, John P
2013-09-30
Exact Gaussian process (GP) regression has O(N^3) runtime for data size N, making it intractable for large N. Many algorithms for improving GP scaling approximate the covariance with lower rank matrices. Other work has exploited structure inherent in particular covariance functions, including GPs with implied Markov structure, and inputs on a lattice (both enable O(N) or O(N log N) runtime). However, these GP advances have not been well extended to the multidimensional input setting, despite the preponderance of multidimensional applications. This paper introduces and tests three novel extensions of structured GPs to multidimensional inputs, for models with additive and multiplicative kernels. First we present a new method for inference in additive GPs, showing a novel connection between the classic backfitting method and the Bayesian framework. We extend this model using two advances: a variant of projection pursuit regression, and a Laplace approximation for non-Gaussian observations. Lastly, for multiplicative kernel structure, we present a novel method for GPs with inputs on a multidimensional grid. We illustrate the power of these three advances on several datasets, achieving performance equal to or very close to the naive GP at orders of magnitude less cost.
Scaling Multidimensional Inference for Structured Gaussian Processes.
Gilboa, Elad; Saatçi, Yunus; Cunningham, John P
2015-02-01
Exact Gaussian process (GP) regression has O(N(3)) runtime for data size N, making it intractable for large N . Many algorithms for improving GP scaling approximate the covariance with lower rank matrices. Other work has exploited structure inherent in particular covariance functions, including GPs with implied Markov structure, and inputs on a lattice (both enable O(N) or O(N log N) runtime). However, these GP advances have not been well extended to the multidimensional input setting, despite the preponderance of multidimensional applications. This paper introduces and tests three novel extensions of structured GPs to multidimensional inputs, for models with additive and multiplicative kernels. First we present a new method for inference in additive GPs, showing a novel connection between the classic backfitting method and the Bayesian framework. We extend this model using two advances: a variant of projection pursuit regression, and a Laplace approximation for non-Gaussian observations. Lastly, for multiplicative kernel structure, we present a novel method for GPs with inputs on a multidimensional grid. We illustrate the power of these three advances on several data sets, achieving performance equal to or very close to the naive GP at orders of magnitude less cost.
Harmonic Pinnacles in the Discrete Gaussian Model
NASA Astrophysics Data System (ADS)
Lubetzky, Eyal; Martinelli, Fabio; Sly, Allan
2016-06-01
The 2 D Discrete Gaussian model gives each height function {η : {mathbb{Z}^2tomathbb{Z}}} a probability proportional to {exp(-β mathcal{H}(η))}, where {β} is the inverse-temperature and {mathcal{H}(η) = sum_{x˜ y}(η_x-η_y)^2} sums over nearest-neighbor bonds. We consider the model at large fixed {β}, where it is flat unlike its continuous analog (the Discrete Gaussian Free Field). We first establish that the maximum height in an {L× L} box with 0 boundary conditions concentrates on two integers M, M + 1 with {M˜ √{(1/2πβ)log Lloglog L}}. The key is a large deviation estimate for the height at the origin in {mathbb{Z}2}, dominated by "harmonic pinnacles", integer approximations of a harmonic variational problem. Second, in this model conditioned on {η≥ 0} (a floor), the average height rises, and in fact the height of almost all sites concentrates on levels H, H + 1 where {H˜ M/√{2}}. This in particular pins down the asymptotics, and corrects the order, in results of Bricmont et al. (J. Stat. Phys. 42(5-6):743-798, 1986), where it was argued that the maximum and the height of the surface above a floor are both of order {√{log L}}. Finally, our methods extend to other classical surface models (e.g., restricted SOS), featuring connections to p-harmonic analysis and alternating sign matrices.
Noise-induced synchronization in spin torque nano oscillators
NASA Astrophysics Data System (ADS)
Nakada, K.; Yakata, S.; Kimura, T.
2012-04-01
We have numerically studied the stochastic magnetization dynamics of a pair of spin torque nano oscillators (STNOs) under noisy current injection by using the Landau-Lifshitz-Gilbert-Slonczewski (LLGS) equation with a macro-spin approximation. Common noisy current injection into both STNOs is found to induce the phase synchronizations, where two STNOs show in-phase or anti-phase locked precession depending on the sequences of Gaussian white noise. The noise-induced synchronization could be a possible application for controlling the output power in the array of the STNOs.
Stochastic bifurcation in noise-driven lasers and Hopf oscillators.
Wieczorek, Sebastian
2009-03-01
This paper considers nonlinear dynamics in an ensemble of uncoupled lasers, each being a limit-cycle oscillator, which are driven by the same external white Gaussian noise. As the external-noise strength increases, there is an onset of synchronization and then subsequent loss of synchrony. Local analysis of the laser equations shows that synchronization becomes unstable via stochastic bifurcation to chaos, defined as a passing of the largest Lyapunov exponent through zero. The locus of this bifurcation is calculated in the three-dimensional parameter space defined by the Hopf parameter, amount of amplitude-phase coupling, and external-noise strength. Numerical comparison between the laser system and the normal form of Hopf bifurcation uncovers a square-root law for this stochastic bifurcation as well as strong enhancement in noise-induced chaos due to the laser's relaxation oscillation.
Noise-induced pattern formation in a semiconductor nanostructure.
Stegemann, G; Balanov, A G; Schöll, E
2005-01-01
We investigate the influence of noise upon the dynamics of the current density distribution in a model of a semiconductor nanostructure, namely, a double barrier resonant tunneling diode. We fix the parameters of the device below the Hopf bifurcation, where the only stable state of the system is a spatially inhomogeneous "filamentary" steady state. We show that the addition of weak Gaussian white noise to the system gives rise to spatially inhomogeneous oscillations that can be quite coherent. As the noise intensity grows, the oscillations tend to become more and more spatially homogeneous, while simultaneously the temporal correlation of the oscillations decreases. Thus, while on one hand noise destroys temporal coherence, on the other hand it enhances the spatial coherence of the current density pattern. PMID:15697712
Relaxation oscillations in a laser with a Gaussian mirror.
Mossakowska-Wyszyńska, Agnieszka; Witoński, Piotr; Szczepański, Paweł
2002-03-20
We present an analysis of the relaxation oscillations in a laser with a Gaussian mirror by taking into account the three-dimensional spatial field distribution of the laser modes and the spatial hole burning effect. In particular, we discuss the influence of the Gaussian mirror peak reflectivity and a Gaussian parameter on the damping rate and frequency of the relaxation oscillation for two different laser structures, i.e., with a classically unstable resonator and a classically stable resonator. PMID:11921794
NASA Astrophysics Data System (ADS)
Newman, J. S.; Beattie, K. R.
1985-03-01
This report summarizes the effects of aviation noise in many areas, ranging from human annoyance to impact on real estate values. It also synthesizes the findings of literature on several topics. Included in the literature were many original studies carried out under FAA and other Federal funding over the past two decades. Efforts have been made to present the critical findings and conclusions of pertinent research, providing, when possible, a bottom line conclusion, criterion or perspective. Issues related to aviation noise are highlighted, and current policy is presented. Specific topic addressed include: annoyance; Hearing and hearing loss; noise metrics; human response to noise; speech interference; sleep interference; non-auditory health effects of noise; effects of noise on wild and domesticated animals; low frequency acoustical energy; impulsive noise; time of day weightings; noise contours; land use compatibility; and real estate values. This document is designed for a variety of users, from the individual completely unfamiliar with aviation noise to experts in the field.
Noise reduction from magnetic resonance images using nonseperable transforms
NASA Astrophysics Data System (ADS)
Nezhadarya, Ehsan; Shamsollahi, Mohammad Bagher
2006-03-01
Multi-scale transforms have got a lot of applications in image processing, in recent years. Wavelet transform is a powerful multiscale transform for denoising noisy signals and images, but the usual two-dimensional separable wavelets are sub-optimal. These separable wavelet transforms can successfully identify zero dimensional singularities in images, but can weakly identify one dimensional singularities such as edges, curves and lines. In this sense, non-separable transforms such as Ridgelet and Curvelet transforms are proposed by Candes and Donoho. The coefficients produced by these non-separable transforms have shown to be sparser than wavelet coefficients. This fact results in better denoising capabilities than wavelet transform. These new non-separable transforms can identify direction in lines and curves, because of special structure of their basis elements. Basically, Magnetic Resonance images are probable to have Rician noise. In some special cases, this kind of noise can be supposed to be white Gaussian noise. In this paper, a new method for denoising MR images is proposed. This method is based on Monoscale Ridgelet transform. It is shown that this two transform can successfully denoise MR images embedded in white Gaussian noise. The results are better in comparison with usual wavelet denoising methods, based on both visual perception and signal-to-noise ratio.
Image interpolation and denoising for division of focal plane sensors using Gaussian processes.
Gilboa, Elad; Cunningham, John P; Nehorai, Arye; Gruev, Viktor
2014-06-16
Image interpolation and denoising are important techniques in image processing. These methods are inherent to digital image acquisition as most digital cameras are composed of a 2D grid of heterogeneous imaging sensors. Current polarization imaging employ four different pixelated polarization filters, commonly referred to as division of focal plane polarization sensors. The sensors capture only partial information of the true scene, leading to a loss of spatial resolution as well as inaccuracy of the captured polarization information. Interpolation is a standard technique to recover the missing information and increase the accuracy of the captured polarization information. Here we focus specifically on Gaussian process regression as a way to perform a statistical image interpolation, where estimates of sensor noise are used to improve the accuracy of the estimated pixel information. We further exploit the inherent grid structure of this data to create a fast exact algorithm that operates in ����(N(3/2)) (vs. the naive ���� (N³)), thus making the Gaussian process method computationally tractable for image data. This modeling advance and the enabling computational advance combine to produce significant improvements over previously published interpolation methods for polarimeters, which is most pronounced in cases of low signal-to-noise ratio (SNR). We provide the comprehensive mathematical model as well as experimental results of the GP interpolation performance for division of focal plane polarimeter. PMID:24977618
Depolarizing differential Mueller matrix of homogeneous media under Gaussian fluctuation hypothesis.
Devlaminck, Vincent
2015-10-01
In this paper, we address the issue of the existence of a solution of depolarizing differential Mueller matrix for a homogeneous medium. Such a medium is characterized by linear changes of its differential optical properties with z the thickness of the medium. We show that, under a short correlation distance assumption, it is possible to derive such linear solution, and we clarify this solution in the particular case where the random fluctuation processes associated to the optical properties are Gaussian white noise-like. A solution to the problem of noncommutativity of a previously proposed model [J. Opt. Soc. Am.30, 2196 (2013)JOSAAH0030-394110.1364/JOSAA.30.002196] is given by assuming a random permutation of the order of the layers and by averaging all the differential matrices resulting from these permutations. It is shown that the underlying assumption in this case is exactly the Gaussian white noise assumption. Finally, a recently proposed approach [Opt. Lett.39, 4470 (2014)OPLEDP0146-959210.1364/OL.39.004470] for analysis of the statistical properties related to changes in optical properties is revisited, and the experimental conditions of application of these results are specified. PMID:26479926
Non-Gaussian space-variant resolution modelling for list-mode reconstruction
NASA Astrophysics Data System (ADS)
Cloquet, C.; Sureau, F. C.; Defrise, M.; Van Simaeys, G.; Trotta, N.; Goldman, S.
2010-09-01
Partial volume effect is an important source of bias in PET images that can be lowered by accounting for the point spread function (PSF) of the scanner. We measured such a PSF in various points of a clinical PET scanner and modelled it as a product of matrices acting in image space, taking the asymmetrical, shift-varying and non-Gaussian character of the PSF into account (AMP modelling), and we integrated this accurate image space modelling into a conventional list-mode OSEM algorithm (EM-AMP reconstruction). We showed on the one hand that when a sufficiently high number of iterations are considered, the AMP modelling lead to better recovery coefficients at reduced background noise compared to reconstruction where no or only partial resolution modelling is performed, and on the other hand that for a small number of iterations, a Gaussian modelling gave the best recovery coefficients. Moreover, we have demonstrated that a deconvolution based on the AMP system response model leads to the same recovery coefficients as the corresponding EM-AMP reconstruction, but at the expense of an increased background noise.
Depolarizing differential Mueller matrix of homogeneous media under Gaussian fluctuation hypothesis.
Devlaminck, Vincent
2015-10-01
In this paper, we address the issue of the existence of a solution of depolarizing differential Mueller matrix for a homogeneous medium. Such a medium is characterized by linear changes of its differential optical properties with z the thickness of the medium. We show that, under a short correlation distance assumption, it is possible to derive such linear solution, and we clarify this solution in the particular case where the random fluctuation processes associated to the optical properties are Gaussian white noise-like. A solution to the problem of noncommutativity of a previously proposed model [J. Opt. Soc. Am.30, 2196 (2013)JOSAAH0030-394110.1364/JOSAA.30.002196] is given by assuming a random permutation of the order of the layers and by averaging all the differential matrices resulting from these permutations. It is shown that the underlying assumption in this case is exactly the Gaussian white noise assumption. Finally, a recently proposed approach [Opt. Lett.39, 4470 (2014)OPLEDP0146-959210.1364/OL.39.004470] for analysis of the statistical properties related to changes in optical properties is revisited, and the experimental conditions of application of these results are specified.
Noise figure of hybrid optical parametric amplifiers.
Marhic, Michel E
2012-12-17
Following a fiber optical parametric amplifier, used as a wavelength converter or in the phase-sensitive mode, by a phase-insensitive amplifier (PIA) can significantly reduce four-wave mixing between signals in broadband systems. We derive the quantum mechanical noise figures (NF) for these two hybrid configurations, and show that adding the PIA only leads to a moderate increase in NF.
Airport noise impact reduction through operations
NASA Technical Reports Server (NTRS)
Deloach, R.
1981-01-01
The effects of various aeronautical, operational, and land-use noise impact reduction alternatives are assessed for a major midwestern airport. Specifically, the relative effectiveness of adding sound absorbing material to aircraft engines, imposing curfews, and treating houses with acoustic insulation are examined.
Lens design based on lens form parameters using Gaussian brackets
NASA Astrophysics Data System (ADS)
Yuan, Xiangyu; Cheng, Xuemin
2014-11-01
The optical power distribution and the symmetry of the lens components are two important attributes that decide the ultimate lens performance and characteristics. Lens form parameters W and S are the key criteria describing the two attributes mentioned above. Lens components with smaller W and S will have a good nature of aberration balance and perform well in providing good image quality. Applying the Gaussian brackets, the two lens form parameters and the Seidel Aberration Coefficients are reconstructed. An initial lens structure can be analytically described by simultaneous equations of Seidel Aberration Coefficients and third-order aberration theory. Adding the constraints of parameters W and S in the solving process, a solution with a proper image quality and aberration distribution is achieved. The optical properties and image quality of the system based on the parameters W and S are also analyzed in this article. In the method, the aberration distribution can be controlled to some extent in the beginning of design, so that we can reduce some workload of optimization later.
Computer Interfacing to Laboratory Instruments: How to Minimize Noise Interferences.
ERIC Educational Resources Information Center
Karpinski, Mary
1987-01-01
Discusses the problems of increased noise levels when using microcomputers as interfaces to chemistry laboratory instruments. Describes how to properly connect a laboratory instrument to a microcomputer's A/D converter board. Suggests how to obtain an analog signal free of interference noise. (TW)
Méndez-Balbuena, Ignacio; Huidobro, Nayeli; Silva, Mayte; Flores, Amira; Trenado, Carlos; Quintanar, Luis; Arias-Carrión, Oscar; Kristeva, Rumyana; Manjarrez, Elias
2015-10-01
The present investigation documents the electrophysiological occurrence of multisensory stochastic resonance in the human visual pathway elicited by tactile noise. We define multisensory stochastic resonance of brain evoked potentials as the phenomenon in which an intermediate level of input noise of one sensory modality enhances the brain evoked response of another sensory modality. Here we examined this phenomenon in visual evoked potentials (VEPs) modulated by the addition of tactile noise. Specifically, we examined whether a particular level of mechanical Gaussian noise applied to the index finger can improve the amplitude of the VEP. We compared the amplitude of the positive P100 VEP component between zero noise (ZN), optimal noise (ON), and high mechanical noise (HN). The data disclosed an inverted U-like graph for all the subjects, thus demonstrating the occurrence of a multisensory stochastic resonance in the P100 VEP.
Huidobro, Nayeli; Silva, Mayte; Flores, Amira; Trenado, Carlos; Quintanar, Luis; Arias-Carrión, Oscar; Kristeva, Rumyana
2015-01-01
The present investigation documents the electrophysiological occurrence of multisensory stochastic resonance in the human visual pathway elicited by tactile noise. We define multisensory stochastic resonance of brain evoked potentials as the phenomenon in which an intermediate level of input noise of one sensory modality enhances the brain evoked response of another sensory modality. Here we examined this phenomenon in visual evoked potentials (VEPs) modulated by the addition of tactile noise. Specifically, we examined whether a particular level of mechanical Gaussian noise applied to the index finger can improve the amplitude of the VEP. We compared the amplitude of the positive P100 VEP component between zero noise (ZN), optimal noise (ON), and high mechanical noise (HN). The data disclosed an inverted U-like graph for all the subjects, thus demonstrating the occurrence of a multisensory stochastic resonance in the P100 VEP. PMID:26156387
Méndez-Balbuena, Ignacio; Huidobro, Nayeli; Silva, Mayte; Flores, Amira; Trenado, Carlos; Quintanar, Luis; Arias-Carrión, Oscar; Kristeva, Rumyana; Manjarrez, Elias
2015-10-01
The present investigation documents the electrophysiological occurrence of multisensory stochastic resonance in the human visual pathway elicited by tactile noise. We define multisensory stochastic resonance of brain evoked potentials as the phenomenon in which an intermediate level of input noise of one sensory modality enhances the brain evoked response of another sensory modality. Here we examined this phenomenon in visual evoked potentials (VEPs) modulated by the addition of tactile noise. Specifically, we examined whether a particular level of mechanical Gaussian noise applied to the index finger can improve the amplitude of the VEP. We compared the amplitude of the positive P100 VEP component between zero noise (ZN), optimal noise (ON), and high mechanical noise (HN). The data disclosed an inverted U-like graph for all the subjects, thus demonstrating the occurrence of a multisensory stochastic resonance in the P100 VEP. PMID:26156387
The method of narrow-band audio classification based on universal noise background model
NASA Astrophysics Data System (ADS)
Rui, Rui; Bao, Chang-chun
2013-03-01
Audio classification is the basis of content-based audio analysis and retrieval. The conventional classification methods mainly depend on feature extraction of audio clip, which certainly increase the time requirement for classification. An approach for classifying the narrow-band audio stream based on feature extraction of audio frame-level is presented in this paper. The audio signals are divided into speech, instrumental music, song with accompaniment and noise using the Gaussian mixture model (GMM). In order to satisfy the demand of actual environment changing, a universal noise background model (UNBM) for white noise, street noise, factory noise and car interior noise is built. In addition, three feature schemes are considered to optimize feature selection. The experimental results show that the proposed algorithm achieves a high accuracy for audio classification, especially under each noise background we used and keep the classification time less than one second.
Decoherence of coupled Josephson charge qubits due to partially correlated low-frequency noise
Hu, Yong; Zhou, Zheng-Wei; Cai, Jian-Ming; Guo, Guang-Can
2007-05-15
Josephson charge qubits are promising candidates for scalable quantum computing. However, their performances are strongly degraded by decoherence due to low-frequency background noise, typically with a 1/f spectrum. In this paper, we investigate the decoherence process of two Cooper pair boxes (CPBs) coupled via a capacitor. Going beyond the common and uncorrelated noise models and the Bloch-Redfield formalism of previous works, we study the coupled system's quadratic dephasing under the condition of partially correlated noise sources. Based on reported experiments and generally accepted noise mechanisms, we introduce a reasonable assumption for the noise correlation, with which the calculation of multiqubit decoherence can be simplified to a problem on the single-qubit level. For the conventional Gaussian 1/f noise case, our results demonstrate that the quadratic dephasing rates are not very sensitive to the spatial correlation of the noises. Furthermore, we discuss the feasibility and efficiency of dynamical decoupling in the coupled CPBs.
Techniques and software tools for estimating ultrasonic signal-to-noise ratios
NASA Astrophysics Data System (ADS)
Chiou, Chien-Ping; Margetan, Frank J.; McKillip, Matthew; Engle, Brady J.; Roberts, Ronald A.
2016-02-01
At Iowa State University's Center for Nondestructive Evaluation (ISU CNDE), the use of models to simulate ultrasonic inspections has played a key role in R&D efforts for over 30 years. To this end a series of wave propagation models, flaw response models, and microstructural backscatter models have been developed to address inspection problems of interest. One use of the combined models is the estimation of signal-to-noise ratios (S/N) in circumstances where backscatter from the microstructure (grain noise) acts to mask sonic echoes from internal defects. Such S/N models have been used in the past to address questions of inspection optimization and reliability. Under the sponsorship of the National Science Foundation's Industry/University Cooperative Research Center at ISU, an effort was recently initiated to improve existing research-grade software by adding graphical user interface (GUI) to become user friendly tools for the rapid estimation of S/N for ultrasonic inspections of metals. The software combines: (1) a Python-based GUI for specifying an inspection scenario and displaying results; and (2) a Fortran-based engine for computing defect signal and backscattered grain noise characteristics. The latter makes use of several models including: the Multi-Gaussian Beam Model for computing sonic fields radiated by commercial transducers; the Thompson-Gray Model for the response from an internal defect; the Independent Scatterer Model for backscattered grain noise; and the Stanke-Kino Unified Model for attenuation. The initial emphasis was on reformulating the research-grade code into a suitable modular form, adding the graphical user interface and performing computations rapidly and robustly. Thus the initial inspection problem being addressed is relatively simple. A normal-incidence pulse/echo immersion inspection is simulated for a curved metal component having a non-uniform microstructure, specifically an equiaxed, untextured microstructure in which the average
Realistic camera noise modeling with application to improved HDR synthesis
NASA Astrophysics Data System (ADS)
Goossens, Bart; Luong, Hiêp; Aelterman, Jan; Pižurica, Aleksandra; Philips, Wilfried
2012-12-01
Due to the ongoing miniaturization of digital camera sensors and the steady increase of the "number of megapixels", individual sensor elements of the camera become more sensitive to noise, even deteriorating the final image quality. To go around this problem, sophisticated processing algorithms in the devices, can help to maximally exploit the knowledge on the sensor characteristics (e.g., in terms of noise), and offer a better image reconstruction. Although a lot of research focuses on rather simplistic noise models, such as stationary additive white Gaussian noise, only limited attention has gone to more realistic digital camera noise models. In this article, we first present a digital camera noise model that takes several processing steps in the camera into account, such as sensor signal amplification, clipping, post-processing,.. We then apply this noise model to the reconstruction problem of high dynamic range (HDR) images from a small set of low dynamic range (LDR) exposures of a static scene. In literature, HDR reconstruction is mostly performed by computing a weighted average, in which the weights are directly related to the observer pixel intensities of the LDR image. In this work, we derive a Bayesian probabilistic formulation of a weighting function that is near-optimal in the MSE sense (or SNR sense) of the reconstructed HDR image, by assuming exponentially distributed irradiance values. We define the weighting function as the probability that the observed pixel intensity is approximately unbiased. The weighting function can be directly computed based on the noise model parameters, which gives rise to different symmetric and asymmetric shapes when electronic noise or photon noise is dominant. We also explain how to deal with the case that some of the noise model parameters are unknown and explain how the camera response function can be estimated using the presented noise model. Finally, experimental results are provided to support our findings.
Study of the intensity noise and intensity modulation in a of hybrid soliton pulsed source
Dogru, Nuran; Oziazisi, M Sadetin
2005-10-31
The relative intensity noise (RIN) and small-signal intensity modulation (IM) of a hybrid soliton pulsed source (HSPS) with a linearly chirped Gaussian apodised fibre Bragg grating (FBG) are considered in the electric-field approximation. The HSPS is described by solving the dynamic coupled-mode equations. It is shown that consideration of the carrier density noise in the HSPS in addition to the spontaneous noise is necessary to analyse accurately noise in the mode-locked HSPS. It is also shown that the resonance peak spectral splitting (RPSS) of the IM near the frequency inverse to the round-trip time of light in the external cavity can be eliminated by selecting an appropriate linear chirp rate in the Gaussian apodised FBG. (laser applications and other topics in quantum electronics)
Capacity of a bosonic memory channel with Gauss-Markov noise
Schaefer, Joachim; Daems, David; Karpov, Evgueni; Cerf, Nicolas J.
2009-12-15
We address the classical capacity of a quantum bosonic memory channel with additive noise, subject to an input energy constraint. The memory is modeled by correlated noise emerging from a Gauss-Markov process. Under reasonable assumptions, we show that the optimal modulation results from a 'quantum water-filling' solution above a certain input energy threshold, similar to the optimal modulation for parallel classical Gaussian channels. We also derive analytically the optimal multimode input state above this threshold, which enables us to compute the capacity of this memory channel in the limit of an infinite number of modes. The method can also be applied to a more general noise environment which is constructed by a stationary Gauss process. The extension of our results to the case of broadband bosonic channels with colored Gaussian noise should also be straightforward.
Reconstructing signals from noisy data with unknown signal and noise covariance.
Oppermann, Niels; Robbers, Georg; Ensslin, Torsten A
2011-10-01
We derive a method to reconstruct Gaussian signals from linear measurements with Gaussian noise. This new algorithm is intended for applications in astrophysics and other sciences. The starting point of our considerations is the principle of minimum Gibbs free energy, which was previously used to derive a signal reconstruction algorithm handling uncertainties in the signal covariance. We extend this algorithm to simultaneously uncertain noise and signal covariances using the same principles in the derivation. The resulting equations are general enough to be applied in many different contexts. We demonstrate the performance of the algorithm by applying it to specific example situations and compare it to algorithms not allowing for uncertainties in the noise covariance. The results show that the method we suggest performs very well under a variety of circumstances and is indeed qualitatively superior to the other methods in cases where uncertainty in the noise covariance is present.
The behavior of quantization spectra as a function of signal-to-noise ratio
NASA Technical Reports Server (NTRS)
Flanagan, M. J.
1991-01-01
An expression for the spectrum of quantization error in a discrete-time system whose input is a sinusoid plus white Gaussian noise is derived. This quantization spectrum consists of two components: a white-noise floor and spurious harmonics. The dithering effect of the input Gaussian noise in both components of the spectrum is considered. Quantitative results in a discrete Fourier transform (DFT) example show the behavior of spurious harmonics as a function of the signal-to-noise ratio (SNR). These results have strong implications for digital reception and signal analysis systems. At low SNRs, spurious harmonics decay exponentially on a log-log scale, and the resulting spectrum is white. As the SNR increases, the spurious harmonics figure prominently in the output spectrum. A useful expression is given that roughly bounds the magnitude of a spurious harmonic as a function of the SNR.
Quantitative analysis of LISA pathfinder test-mass noise
NASA Astrophysics Data System (ADS)
Ferraioli, Luigi; Congedo, Giuseppe; Hueller, Mauro; Vitale, Stefano; Hewitson, Martin; Nofrarias, Miquel; Armano, Michele
2011-12-01
LISA Pathfinder (LPF) is a mission aiming to test the critical technology for the forthcoming space-based gravitational-wave detectors. The main scientific objective of the LPF mission is to demonstrate test masses free falling with residual accelerations below 3×10-14ms-2/Hz at 1 mHz. Reaching such an ambitious target will require a significant amount of system optimization and characterization, which will in turn require accurate and quantitative noise analysis procedures. In this paper, we discuss two main problems associated with the analysis of the data from LPF: i) excess noise detection and ii) noise parameter identification. The mission is focused on the low-frequency region ([0.1, 10] mHz) of the available signal spectrum. In such a region, the signal is dominated by the force noise acting on test masses. At the same time, the mission duration is limited to 90 days and typical data segments will be 24 hours in length. Considering those constraints, noise analysis is expected to deal with a limited amount of non-Gaussian data, since the spectrum statistics will be far from Gaussian and the lowest available frequency is limited by the data length. In this paper, we analyze the details of the expected statistics for spectral data and develop two suitable excess noise estimators. One is based on the statistical properties of the integrated spectrum, the other is based on the Kolmogorov-Smirnov test. The sensitivity of the estimators is discussed theoretically for independent data, then the algorithms are tested on LPF synthetic data. The test on realistic LPF data allows the effect of spectral data correlations on the efficiency of the different noise excess estimators to be highlighted. It also reveals the versatility of the Kolmogorov-Smirnov approach, which can be adapted to provide reasonable results on correlated data from a modified version of the standard equations for the inversion of the test statistic. Closely related to excess noise detection, the
Multi-Compartment T2 Relaxometry Using a Spatially Constrained Multi-Gaussian Model
Raj, Ashish; Pandya, Sneha; Shen, Xiaobo; LoCastro, Eve; Nguyen, Thanh D.; Gauthier, Susan A.
2014-01-01
The brain’s myelin content can be mapped by T2-relaxometry, which resolves multiple differentially relaxing T2 pools from multi-echo MRI. Unfortunately, the conventional fitting procedure is a hard and numerically ill-posed problem. Consequently, the T2 distributions and myelin maps become very sensitive to noise and are frequently difficult to interpret diagnostically. Although regularization can improve stability, it is generally not adequate, particularly at relatively low signal to noise ratio (SNR) of around 100–200. The purpose of this study was to obtain a fitting algorithm which is able to overcome these difficulties and generate usable myelin maps from noisy acquisitions in a realistic scan time. To this end, we restrict the T2 distribution to only 3 distinct resolvable tissue compartments, modeled as Gaussians: myelin water, intra/extra-cellular water and a slow relaxing cerebrospinal fluid compartment. We also impose spatial smoothness expectation that volume fractions and T2 relaxation times of tissue compartments change smoothly within coherent brain regions. The method greatly improves robustness to noise, reduces spatial variations, improves definition of white matter fibers, and enhances detection of demyelinating lesions. Due to efficient design, the additional spatial aspect does not cause an increase in processing time. The proposed method was applied to fast spiral acquisitions on which conventional fitting gives uninterpretable results. While these fast acquisitions suffer from noise and inhomogeneity artifacts, our preliminary results indicate the potential of spatially constrained 3-pool T2 relaxometry. PMID:24896833
Non-Gaussian Photon Probability Distribution
Solomon, Benjamin T.
2010-01-28
This paper investigates the axiom that the photon's probability distribution is a Gaussian distribution. The Airy disc empirical evidence shows that the best fit, if not exact, distribution is a modified Gamma mGAMMA distribution (whose parameters are alpha = r, betar/sq root(u)) in the plane orthogonal to the motion of the photon. This modified Gamma distribution is then used to reconstruct the probability distributions along the hypotenuse from the pinhole, arc from the pinhole, and a line parallel to photon motion. This reconstruction shows that the photon's probability distribution is not a Gaussian function. However, under certain conditions, the distribution can appear to be Normal, thereby accounting for the success of quantum mechanics. This modified Gamma distribution changes with the shape of objects around it and thus explains how the observer alters the observation. This property therefore places additional constraints to quantum entanglement experiments. This paper shows that photon interaction is a multi-phenomena effect consisting of the probability to interact P{sub i}, the probabilistic function and the ability to interact A{sub i}, the electromagnetic function. Splitting the probability function P{sub i} from the electromagnetic function A{sub i} enables the investigation of the photon behavior from a purely probabilistic P{sub i} perspective. The Probabilistic Interaction Hypothesis is proposed as a consistent method for handling the two different phenomena, the probability function P{sub i} and the ability to interact A{sub i}, thus redefining radiation shielding, stealth or cloaking, and invisibility as different effects of a single phenomenon P{sub i} of the photon probability distribution. Sub wavelength photon behavior is successfully modeled as a multi-phenomena behavior. The Probabilistic Interaction Hypothesis provides a good fit to Otoshi's (1972) microwave shielding, Schurig et al.(2006) microwave cloaking, and Oulton et al.(2008) sub
Adding a lens Improves spinning speed characterization.
Mihaliuk, Eugene; Gullion, Terry
2015-11-01
Highly stable sample rotation is important in many solid-state NMR experiments. Whether the necessary stability is achieved is not always clear. Typically only an average frequency over some time interval (often relatively long and unknown) is available from the spinning speed controller readout, which is not representative of the short-term variations of instantaneous rotation frequency. The necessity of the relatively slow measurement of spinning speed is a consequence of phase noise in the tachometer, which prevents speed measurement to be both rapid and precise at the same time. We show that adding a lens to the tachometer, without any other changes in the probe, reduces phase noise by nearly an order of magnitude and allows improved measurement of the spinning speed.
Noise-assisted estimation of attractor invariants.
Restrepo, Juan F; Schlotthauer, Gastón
2016-07-01
In this article, the noise-assisted correlation integral (NCI) is proposed. The purpose of the NCI is to estimate the invariants of a dynamical system, namely the correlation dimension (D), the correlation entropy (K_{2}), and the noise level (σ). This correlation integral is induced by using random noise in a modified version of the correlation algorithm, i.e., the noise-assisted correlation algorithm. We demonstrate how the correlation integral by Grassberger et al. and the Gaussian kernel correlation integral (GCI) by Diks can be thought of as special cases of the NCI. A third particular case is the U-correlation integral proposed herein, from which we derived coarse-grained estimators of the correlation dimension (D_{m}^{U}), the correlation entropy (K_{m}^{U}), and the noise level (σ_{m}^{U}). Using time series from the Henon map and the Mackey-Glass system, we analyze the behavior of these estimators under different noise conditions and data lengths. The results show that the estimators D_{m}^{U} and σ_{m}^{U} behave in a similar manner to those based on the GCI. However, for the calculation of K_{2}, the estimator K_{m}^{U} outperforms its GCI-based counterpart. On the basis of the behavior of these estimators, we have proposed an automatic algorithm to find D,K_{2}, and σ from a given time series. The results show that by using this approach, we are able to achieve statistically reliable estimations of those invariants.
Noise-assisted estimation of attractor invariants.
Restrepo, Juan F; Schlotthauer, Gastón
2016-07-01
In this article, the noise-assisted correlation integral (NCI) is proposed. The purpose of the NCI is to estimate the invariants of a dynamical system, namely the correlation dimension (D), the correlation entropy (K_{2}), and the noise level (σ). This correlation integral is induced by using random noise in a modified version of the correlation algorithm, i.e., the noise-assisted correlation algorithm. We demonstrate how the correlation integral by Grassberger et al. and the Gaussian kernel correlation integral (GCI) by Diks can be thought of as special cases of the NCI. A third particular case is the U-correlation integral proposed herein, from which we derived coarse-grained estimators of the correlation dimension (D_{m}^{U}), the correlation entropy (K_{m}^{U}), and the noise level (σ_{m}^{U}). Using time series from the Henon map and the Mackey-Glass system, we analyze the behavior of these estimators under different noise conditions and data lengths. The results show that the estimators D_{m}^{U} and σ_{m}^{U} behave in a similar manner to those based on the GCI. However, for the calculation of K_{2}, the estimator K_{m}^{U} outperforms its GCI-based counterpart. On the basis of the behavior of these estimators, we have proposed an automatic algorithm to find D,K_{2}, and σ from a given time series. The results show that by using this approach, we are able to achieve statistically reliable estimations of those invariants. PMID:27575128
Noise-driven optical absorption coefficients of impurity doped quantum dots
NASA Astrophysics Data System (ADS)
Ganguly, Jayanta; Saha, Surajit; Pal, Suvajit; Ghosh, Manas
2016-01-01
We make an extensive investigation of linear, third-order nonlinear, and total optical absorption coefficients (ACs) of impurity doped quantum dots (QDs) in presence and absence of noise. The noise invoked in the present study is a Gaussian white noise. The quantum dot is doped with repulsive Gaussian impurity. Noise has been introduced to the system additively and multiplicatively. A perpendicular magnetic field acts as a source of confinement and a static external electric field has been applied. The AC profiles have been studied as a function of incident photon energy when several important parameters such as optical intensity, electric field strength, magnetic field strength, confinement energy, dopant location, relaxation time, Al concentration, dopant potential, and noise strength take on different values. In addition, the role of mode of application of noise (additive/multiplicative) on the AC profiles has also been analyzed meticulously. The AC profiles often consist of a number of interesting observations such as one photon resonance enhancement, shift of AC peak position, variation of AC peak intensity, and bleaching of AC peak. However, presence of noise alters the features of AC profiles and leads to some interesting manifestations. Multiplicative noise brings about more complexity in the AC profiles than its additive counterpart. The observations indeed illuminate several useful aspects in the study of linear and nonlinear optical properties of doped QD systems, specially in presence of noise. The findings are expected to be quite relevant from a technological perspective.
Noise-induced dispersion and breakup of clusters in cell cycle dynamics
Gong, Xue; Moses, Gregory; Neiman, Alexander B.; Young, Todd
2014-01-01
We study the effects of random perturbations on collective dynamics of a large ensemble of interacting cells in a model of the cell division cycle. We consider a parameter region for which the unperturbed model possesses asymptotically stable two-cluster periodic solutions. Two biologically motivated forms of random perturbations are considered: bounded variations in growth rate and asymmetric division. We compare the effects of these two dispersive mechanisms with additive Gaussian white noise perturbations. We observe three distinct phases of the response to noise in the model. First, for weak noise there is a linear relationship between the applied noise strength and the dispersion of the clusters. Second, for moderate noise strengths the clusters begin to mix, i.e. individual cells move between clusters, yet the population distribution clearly continues to maintain a two-cluster structure. Third, for strong noise the clusters are destroyed and the population is characterized by a uniform distribution. The second and third phases are separated by an order - disorder phase transition that has the characteristics of a Hopf bifurcation. Furthermore, we show that for the cell cycle model studied, the effects of bounded random perturbations are virtually indistinguishable from those induced by additive Gaussian noise, after appropriate scaling of the variance of noise strength. We then use the model to predict the strength of coupling among the cells from experimental data. In particular, we show that coupling must be rather strong to account for the observed clustering of cells given experimentally estimated noise variance. PMID:24694583
Quantum Fields Obtained from Convoluted Generalized White Noise Never Have Positive Metric
NASA Astrophysics Data System (ADS)
Albeverio, Sergio; Gottschalk, Hanno
2016-05-01
It is proven that the relativistic quantum fields obtained from analytic continuation of convoluted generalized (Lévy type) noise fields have positive metric, if and only if the noise is Gaussian. This follows as an easy observation from a criterion by Baumann, based on the Dell'Antonio-Robinson-Greenberg theorem, for a relativistic quantum field in positive metric to be a free field.
ERIC Educational Resources Information Center
Richards, Andrew
2015-01-01
Two quantitative measures of school performance are currently used, the average points score (APS) at Key Stage 2 and value-added (VA), which measures the rate of academic improvement between Key Stage 1 and 2. These figures are used by parents and the Office for Standards in Education to make judgements and comparisons. However, simple…
Propagation of Environmental Noise
ERIC Educational Resources Information Center
Lyon, R. H.
1973-01-01
Solutions for environmental noise pollution lie in systematic study of many basic processes such as reflection, scattering, and spreading. Noise propagation processes should be identified in different situations and assessed for their relative importance. (PS)
Distributed static linear Gaussian models using consensus.
Belanovic, Pavle; Valcarcel Macua, Sergio; Zazo, Santiago
2012-10-01
Algorithms for distributed agreement are a powerful means for formulating distributed versions of existing centralized algorithms. We present a toolkit for this task and show how it can be used systematically to design fully distributed algorithms for static linear Gaussian models, including principal component analysis, factor analysis, and probabilistic principal component analysis. These algorithms do not rely on a fusion center, require only low-volume local (1-hop neighborhood) communications, and are thus efficient, scalable, and robust. We show how they are also guaranteed to asymptotically converge to the same solution as the corresponding existing centralized algorithms. Finally, we illustrate the functioning of our algorithms on two examples, and examine the inherent cost-performance trade-off.
Bimetric structure formation: Non-Gaussian predictions
Magueijo, Joao; Noller, Johannes; Piazza, Federico
2010-08-15
The minimal bimetric theory employing a disformal transformation between matter and gravity metrics is known to produce exactly scale-invariant fluctuations. It has a purely equilateral non-Gaussian signal, with an amplitude smaller than that of Dirac Born Infeld inflation (with opposite sign) but larger than standard inflation. We consider nonminimal bimetric models, where the coupling B appearing in the disformal transformation g-circumflex{sub {mu}{nu}}=g{sub {mu}{nu}}-B{partial_derivative}{sub {mu}{phi}{partial_derivative}{nu}{phi}} can run with {phi}. For power-law B({phi}) these models predict tilted spectra. For each value of the spectral index, a distinctive distortion to the equilateral property can be found. The constraint between this distortion and the spectral index can be seen as a 'consistency relation' for nonminimal bimetric models.
Bimetric structure formation: Non-Gaussian predictions
NASA Astrophysics Data System (ADS)
Magueijo, João; Noller, Johannes; Piazza, Federico
2010-08-01
The minimal bimetric theory employing a disformal transformation between matter and gravity metrics is known to produce exactly scale-invariant fluctuations. It has a purely equilateral non-Gaussian signal, with an amplitude smaller than that of Dirac Born Infeld inflation (with opposite sign) but larger than standard inflation. We consider nonminimal bimetric models, where the coupling B appearing in the disformal transformation g^μν=gμν-B∂μϕ∂νϕ can run with ϕ. For power-law B(ϕ) these models predict tilted spectra. For each value of the spectral index, a distinctive distortion to the equilateral property can be found. The constraint between this distortion and the spectral index can be seen as a “consistency relation” for nonminimal bimetric models.
IBS for non-gaussian distributions
Fedotov, A.; Sidorin, A.O.; Smirnov, A.V.
2010-09-27
In many situations distribution can significantly deviate from Gaussian which requires accurate treatment of IBS. Our original interest in this problem was motivated by the need to have an accurate description of beam evolution due to IBS while distribution is strongly affected by the external electron cooling force. A variety of models with various degrees of approximation were developed and implemented in BETACOOL in the past to address this topic. A more complete treatment based on the friction coefficient and full 3-D diffusion tensor was introduced in BETACOOL at the end of 2007 under the name 'local IBS model'. Such a model allowed us calculation of IBS for an arbitrary beam distribution. The numerical benchmarking of this local IBS algorithm and its comparison with other models was reported before. In this paper, after briefly describing the model and its limitations, they present its comparison with available experimental data.
Non-Gaussian Berkson errors in bioassay.
Althubaiti, Alaa; Donev, Alexander
2016-02-01
The experimental design plays an important role in every experimental study. However, if errors in the settings of the studied factors cannot be avoided, i.e. Berkson errors occur, the estimates of the model parameters may be biased and the variability in the study increased. Correction methods for the effect of Berkson errors are compared. The emphasis is on the study of correlated Berkson errors which follow non-Gaussian distribution as this appears to have been a neglected, yet important, area. It is shown that the regression calibration approach bias correction methods are useful when the Berkson errors are independent. However, when these errors are dependent, the newly proposed method B-SIMEX clearly outperforms the other methods.
Absolute instability of the Gaussian wake profile
NASA Technical Reports Server (NTRS)
Hultgren, Lennart S.; Aggarwal, Arun K.
1987-01-01
Linear parallel-flow stability theory has been used to investigate the effect of viscosity on the local absolute instability of a family of wake profiles with a Gaussian velocity distribution. The type of local instability, i.e., convective or absolute, is determined by the location of a branch-point singularity with zero group velocity of the complex dispersion relation for the instability waves. The effects of viscosity were found to be weak for values of the wake Reynolds number, based on the center-line velocity defect and the wake half-width, larger than about 400. Absolute instability occurs only for sufficiently large values of the center-line wake defect. The critical value of this parameter increases with decreasing wake Reynolds number, thereby indicating a shrinking region of absolute instability with decreasing wake Reynolds number. If backflow is not allowed, absolute instability does not occur for wake Reynolds numbers smaller than about 38.
Exploring scalar field dynamics with Gaussian processes
Nair, Remya; Jhingan, Sanjay; Jain, Deepak E-mail: sanjay.jhingan@gmail.com
2014-01-01
The origin of the accelerated expansion of the Universe remains an unsolved mystery in Cosmology. In this work we consider a spatially flat Friedmann-Robertson-Walker (FRW) Universe with non-relativistic matter and a single scalar field contributing to the energy density of the Universe. Properties of this scalar field, like potential, kinetic energy, equation of state etc. are reconstructed from Supernovae and BAO data using Gaussian processes. We also reconstruct energy conditions and kinematic variables of expansion, such as the jerk and the slow roll parameter. We find that the reconstructed scalar field variables and the kinematic quantities are consistent with a flat ΛCDM Universe. Further, we find that the null energy condition is satisfied for the redshift range of the Supernovae data considered in the paper, but the strong energy condition is violated.
Scintillations of partially coherent Laguerre Gaussian beams
NASA Astrophysics Data System (ADS)
Yüceer, M.; Eyyuboğlu, H. T.; Lukin, I. P.
2010-12-01
Scintillations of Laguerre-Gaussian (LG) beams for weak atmospheric turbulence conditions are derived for on-axis receiver positions by using Huygens-Fresnel (HF) method in semi-analytic fashion. Numerical evaluations indicate that at the fully coherent limit, higher values of radial mode numbers will give rise to more scintillations, at medium and low partial coherence levels, particularly at longer propagation distances, scintillations will fall against rises in radial mode numbers. At small source sizes, the scintillations of LG beams having full coherence will initially rise, reaching saturation at large source sizes. For LG beams with low partial coherence levels, a steady fall toward the larger source sizes is observed. Partially coherent beams of medium levels generally exhibit a rising trend toward the large source sizes, also changing the respective positions of the related curves. Beams of low coherence levels will be less affected by the variations in the refractive index structure constant.
Gaussian polarizable-ion tight binding
NASA Astrophysics Data System (ADS)
Boleininger, Max; Guilbert, Anne AY; Horsfield, Andrew P.
2016-10-01
To interpret ultrafast dynamics experiments on large molecules, computer simulation is required due to the complex response to the laser field. We present a method capable of efficiently computing the static electronic response of large systems to external electric fields. This is achieved by extending the density-functional tight binding method to include larger basis sets and by multipole expansion of the charge density into electrostatically interacting Gaussian distributions. Polarizabilities for a range of hydrocarbon molecules are computed for a multipole expansion up to quadrupole order, giving excellent agreement with experimental values, with average errors similar to those from density functional theory, but at a small fraction of the cost. We apply the model in conjunction with the polarizable-point-dipoles model to estimate the internal fields in amorphous poly(3-hexylthiophene-2,5-diyl).