Additive Noise Component Research Articles

Semiparametric distribution forecasting

Given m time series regression models, linear or not, with additive noise components, it is shown how to estimate semiparametrically the predictive probability distribution of one of the time series conditional on past random covariate data. This is done by assuming that the distributions of the residual components associated with the regression models are tilted versions of a reference distribution.

Reprint of: On the importance of specifying appropriate nozzle-exit conditions in jet noise prediction

In this paper, the importance of initial conditions on subsonic jet noise is emphasized by showing numerical results obtained by large-eddy simulations for initially laminar round jets at Mach number 0.9 and Reynolds number 105. The near and the far sound pressure fields of the jets are found to significantly vary with the flow parameters, namely the boundary-layer thickness and the turbulence levels, at the nozzle exit. With respect to initially turbulent jets, strong additional noise components generated by pairings of coherent vortical structures in the transitional shear layers are in addition observed, in agreement with experiments.

On the importance of specifying appropriate nozzle-exit conditions in jet noise prediction

In this paper, the importance of initial conditions on subsonic jet noise is emphasized by showing numerical results obtained by large-eddy simulations for initially laminar round jets at Mach number 0.9 and Reynolds number 105. The near and the far sound pressure fields of the jets are found to significantly vary with the flow parameters, namely the boundary-layer thickness and the turbulence levels, at the nozzle exit. With respect to initially turbulent jets, strong additional noise components generated by pairings of coherent vortical structures in the transitional shear layers are in addition observed, in agreement with experiments.

Arbitrary-order corrections for finite-time drift and diffusion coefficients

We address a standard class of diffusion processes with linear drift and quadratic diffusion coefficients. These contributions to dynamic equations can be directly drawn from data time series. However, real data are constrained to finite sampling rates and therefore it is crucial to establish a suitable mathematical description of the required finite-time corrections. Based on Itô-Taylor expansions, we present the exact corrections to the finite-time drift and diffusion coefficients. These results allow to reconstruct the real hidden coefficients from the empirical estimates. We also derive higher-order finite-time expressions for the third and fourth conditional moments that furnish extra theoretical checks for this class of diffusion models. The analytical predictions are compared with the numerical outcomes of representative artificial time series.

Acoustic Measurements of High-Speed Jets from Rectangular Nozzle with Thrust Vectoring

Laboratory experiments were conducted to characterize the noise produced by jets from a rectangular nozzle with thrust-vectoring capability. Such exhaust jets are present on state-of-the-art fighter aircraft designed for supermaneuverability. This scaled nozzle research was facilitated by the design of a model with rectangular exit geometry and thrust-vectoring attachments, thus simulating one configuration of such aircraft. A supersonic, helium―air mixture jet issued from this nozzle, and acoustic measurements were made with four microphones arranged in a circular arc in the far-field region. Schlieren photographs were recorded to establish the major features of the jet flow. Significant among the results was the fact that the addition of thrust-vectoring attachments set to 0 deg deflection, on an otherwise clean rectangular nozzle, decreases the intensity of sound radiated on each axis plane to below that of a round nozzle jet. Furthermore, deflection of the flow in the minor axis plane by virtue of these nozzle attachments induces an approximately corresponding deflection of the acoustic field without significant additional noise components in the peak emission direction. The frequency spectra as well as overall sound pressure levels of microphone measurements with the rectangular nozzle jet at several observation positions and thrust-vectoring configurations are presented and summarized in the results of this paper.

Population stochastic modelling (PSM)—An R package for mixed-effects models based on stochastic differential equations

The extension from ordinary to stochastic differential equations (SDEs) in pharmacokinetic and pharmacodynamic (PK/PD) modelling is an emerging field and has been motivated in a number of articles [N.R. Kristensen, H. Madsen, S.H. Ingwersen, Using stochastic differential equations for PK/PD model development, J. Pharmacokinet. Pharmacodyn. 32 (February(1)) (2005) 109–141; C.W. Tornøe, R.V. Overgaard, H. Agersø, H.A. Nielsen, H. Madsen, E.N. Jonsson, Stochastic differential equations in NONMEM: implementation, application, and comparison with ordinary differential equations, Pharm. Res. 22 (August(8)) (2005) 1247–1258; R.V. Overgaard, N. Jonsson, C.W. Tornøe, H. Madsen, Non-linear mixed-effects models with stochastic differential equations: implementation of an estimation algorithm, J. Pharmacokinet. Pharmacodyn. 32 (February(1)) (2005) 85–107; U. Picchini, S. Ditlevsen, A. De Gaetano, Maximum likelihood estimation of a time-inhomogeneous stochastic differential model of glucose dynamics, Math. Med. Biol. 25 (June(2)) (2008) 141–155]. PK/PD models are traditionally based ordinary differential equations (ODEs) with an observation link that incorporates noise. This state-space formulation only allows for observation noise and not for system noise. Extending to SDEs allows for a Wiener noise component in the system equations. This additional noise component enables handling of autocorrelated residuals originating from natural variation or systematic model error. Autocorrelated residuals are often partly ignored in PK/PD modelling although violating the hypothesis for many standard statistical tests. This article presents a package for the statistical program R that is able to handle SDEs in a mixed-effects setting. The estimation method implemented is the FOCE 1 1 FOCE—First-Order Conditional Estimation. approximation to the population likelihood which is generated from the individual likelihoods that are approximated using the Extended Kalman Filter’s one-step predictions.

Covariance Statistics of Polarimetric Brightness Temperature Measurements

All microwave radiometer measurements of brightness temperature (T B) include an additive noise component. With conventional linearly polarized radiometers, the variance of the noise is a well-understood function of the system temperature, the predetection bandwidth, and the integration time according to the so-called ldquoradiometer uncertainty equation.rdquo The noise has generally been considered to be uncorrelated between orthogonally polarized channels. The variance and the correlation statistics of the additive noise component of fully polarimetric radiometer measurements are derived from theoretical considerations, and the resulting relationships are experimentally verified. It is found that the noise can be correlated between polarimetric channels, and the correlation statistics will vary as a function of the polarization state of the scene under observation. For example, a strong correlation is typical between the noise in either vertically or horizontally polarized TB and the 45 deg slant linear polarizations that are often used to derive the third Stokes T B. A weak, but nonzero, correlation is also possible between the additive noise in the vertically and horizontally polarized T B's themselves. This is a correction to the common assumption that they are uncorrelated.

Convergence analysis of a continuous-time nonlinear LMS-type algorithm using stochastic calculus

In several applications least mean square (LMS) has served as a good tool for estimating the parameters of linear models but the success of continuous-time in nonlinear models has not reached its height. In this paper, we have developed a nonlinear continuous-time LMS type algorithm that estimates parameters of nonlinear systems considering the noisy input–output relationship. The nonlinear system has been assumed to be memoryless and an additive Gaussian noise component to the system has been assumed. The mean squared error between the true system output and the estimated output, when the estimated output is modeled using the same form of the nonlinear function as the original system but with the parameters unknown, is minimized using the gradient scheme with the expectation removed. The result is a least mean square algorithm for nonlinear systems. In particular, we have performed a convergence analysis of the continuous-time nonlinear LMS algorithm applied to nonlinear systems when the time step goes to zero. The resulting algorithm then behaves as a stochastic differential equation, and the standard methods of Itô calculus and Fokker–Planck theory are applied to obtain statistical properties of the mean and covariance evolution of the parameter estimates. Computer simulations corroborate the theoretical results.

Design of the optimal sector device and determination of its parameters in gas electron diffraction

This work presents a way to select the optimal form of the sector lobe and practical algorithms to determine the sector function of the device in use. Knowledge of the sector function enhances the quality of drawing a background line and ensures the division of multiplicative and additive noise components, thus increasing the reliability of structural parameters determined by gas electron diffraction.

Extraneous scattering and its discrimination in gas electron diffraction

The division algorithm for additive and multiplicative noise components of the electron scattering intensity is proposed in the work. The procedure presented provides a substantial improvement in experimental data processing in comparison with the traditional method used by Russian research groups. A comparison of the additive noise with the simulated residual gas scattering shows that it is this scattering that is largely responsible for the strange contribution to the measured diffraction pattern.

Hot-Carrier-Induced Damage and Its Spatial Location on RF Noise in Deep-Submicrometer NMOSFETs

In this paper, the impact of hot-carrier (HC)-induced interface damage and its spatial location on RF noise in 0.18-mum NMOSFETs have been characterized and analyzed. The experimental results revealed a significant increase in NF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">min</sub> and R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> , which could be attributed to the additional channel noise component associated with the HC-induced interface damage. It is found that the presence of interface states at the source side has greater impact on the increase in channel noise, which is consistent with the recent theoretical simulation using the hydrodynamic and full Langevin-Boltzmann equation noise models based on impedance field representation. Our results provide direct experimental verification that the local noise at the source side plays a more important role in determining the overall channel noise.

The aspect Bernoulli model: multiple causes of presences and absences

We present a probabilistic multiple cause model for the analysis of binary (0–1) data. A distinctive feature of the aspect Bernoulli (AB) model is its ability to automatically detect and distinguish between “true absences” and “false absences” (both of which are coded as 0 in the data), and similarly, between “true presences” and “false presences” (both of which are coded as 1). This is accomplished by specific additive noise components which explicitly account for such non-content bearing causes. The AB model is thus suitable for noise removal and data explanatory purposes, including omission/addition detection. An important application of AB that we demonstrate is data-driven reasoning about palaeontological recordings. Additionally, results on recovering corrupted handwritten digit images and expanding short text documents are also given, and comparisons to other methods are demonstrated and discussed.

On the Extension of Multidimensional Speckle Noise Model From Single-Look to Multilook SAR Imagery

Speckle noise represents one of the major problems when synthetic aperture radar (SAR) data are considered. Despite the fact that speckle is caused by the scattering process itself, it must be considered as a noise source due to the complexity of the scattering process. The presence of speckle makes data interpretation difficult, but it also affects the quantitative retrieval of physical parameters. In the case of one-dimensional SAR systems, speckle is completely determined by a multiplicative noise component. Nevertheless, for multidimensional SAR systems, speckle results from the combination of multiplicative and additive noise components. This model has been first developed for single-look data. The objective of this paper is to extend the single-look data model to define a multilook multidimensional speckle noise model. The asymptotic analysis of this extension, for a large number of averaged samples, is also considered to assess the model properties. Details and validation of the multilook multidimensional speckle noise model are provided both theoretically and by means of experimental SAR data acquired by the experimental synthetic aperture radar system, operated by the German Aerospace Center

Coherence estimation in synthetic aperture radar data based on speckle noise modeling

In the past we proposed a multidimensional speckle noise model to which we now include systematic phase variation effects. This extension makes it possible to define what is believed to be a novel coherence model able to identify the different sources of bias when coherence is estimated on multidimensional synthetic radar aperture (SAR) data. On the one hand, low coherence biases are basically due to the complex additive speckle noise component of the Hermitian product of two SAR images. On the other hand, the availability of the coherence model permits us to quantify the bias due to topography when multilook filtering is considered, permitting us to establish the conditions upon which information may be estimated independently of topography. Based on the coherence model, two coherence estimation approaches, aiming to reduce the different biases, are proposed. Results with simulated and experimental polarimetric and interferometric SAR data illustrate and validate both, the coherence model and the coherence estimation algorithms.

Methods of imitational computer modeling of decameter communication channels

Imitational models of one-dimensional discrete communication channels with and without edge distortions of message fragments and of one-dimensional discrete-continuous, two-dimensional linear and nonlinear, and three-dimensional continuous linear (space and time) communication channels are suggested that allow comparative tests of decameter communication systems to be performed on paths under conditions of multibeam propagation in the presence of every possible additive noise component.

Self-aligned spatial filtering using laser optical tweezers

We present an optical spatial filtering device that has been integrated into a microfluidic system and whose motion and alignment is controlled using a laser optical tweezer. The lithographically patterned micro-optical spatial filter device filters out higher frequency additive noise components by automatically aligning itself in three dimensions to the focus of the laser beam. This self-alignment capability is achieved through the attachment of a refractive optical element directly over the circular aperture or pinhole of the spatial filter. A discussion of two different spatial filter designs is presented along with experimental results that demonstrate the effectiveness of the self-aligned micro-optic spatial filter.

Correction of the vocal signal distorted by additive noise

Proposed was a method of vocal signal correction by processing its short-period amplitude logarithmic spectrum, which makes it possible to reduce the additive noise component. The method suits for speech preprocessing, analysis, and recognition. The results of applying it to concrete vocal signals were presented.

Reduced-Rank Regularized Multivariate Model for High-Dimensional Data

This article proposes a modeling framework for high-dimensional experimental data, such as brain images or microarrays, that discovers statistically significant structures most relevant to the experimental covariates. To deal with the curse of dimensionality, three regularization schemes are used: a reduced-rank model, penalization of the covariance matrix, and regularization of the basis-expanded predictor set. The latter allows us to flexibly model associations while controlling for overfitting. The modeling framework is derived from a reduced-rank multiresponse linear model, which offers a familiar interface for researchers. The novel regularizations of both sides of the model make it applicable in high-dimensional settings, without a need for prior dimension reduction, and can model nonlinear relationships. An efficient, dual-space algorithm is proposed to estimate its components in low-dimensional space. It permits the use of the bootstrap, to provide pointwise standard error bands on association graphs, and other resampling techniques to optimize hyperparameters. We evaluate the model on a small neuroimaging dataset, and in a simulation study using simple images corrupted by additive Gaussian iid and random field noise components with signal-to-noise ratios below 0.1. Our model compares well with a general linear model (GLM) even when the nonlinear associations are specified explicitly in GLM.

Blind Equalization of Dispersive Fast Fading Ricean Channels Via the EMV Algorithm

A blind maximum likelihood equalization method is proposed for frequency selective fast fading Ricean channels. This method employs the expectation-maximization Viterbi algorithm (EMVA) developed in for blind channel estimation and signal detection. Since the Viterbi algorithm (VA) is used to execute the E-phase of an expectation-maximization (EM) iteration, it requires that the observed sequence can be modelled as a finite-state hidden Markov process. We develop a hidden Markov model for frequency selective fast fading Ricean channels, so that the observed process can be viewed as the noisy output of a finite state machine (FSM), to which the VA is applicable. The EMVA is then employed to obtain a blind maximum likelihood estimate of the specular part of the channel and, for one special case, of a noise parameter measuring the total power of the additive and multiplicative channel noise components. Simulation results are presented which show that the EMVA achieves an accurate estimate of the channel specular part and has an error rate performance close to that of the maximum likelihood detector based on true parameters for the given FSM model.

Reset noise in active pixel image sensors

In most image sensor pixel architectures, signal detection involves the reset of charge in a capacitive node. This operation gives rise to reset noise, and in particular, partition noise. In modern day transistors, with the shrinkage of device critical dimensions and increase of clock frequency, reset noise impacts many aspects of active pixel sensor operation. An additional spurious noise component has been observed from the reset noise model that is closely related to the switching operation, and is known as partition noise. Partition noise has been found to relate closely to the fall times of the applied reset pulse. However, existing noise models assume a constant charge density at the onset of transistor pinch-off, thus resulting in an underestimation of partition noise. This work investigates the influence of reset noise and other related noise generation on active pixel sensor performance.