Effects Of Random Noises Research Articles

Density estimation of a sum random variable from contaminated data samples

Let X, Y be independent random variables with unknown distributions and f X + Y be the unknown density of X + Y. Under the effects of independent random noises ζ and η, which are assumed to have known distributions, we observe the random variables X ′ and Y ′ , where X ′ = X + ζ and Y ′ = Y + η . Our aim is to estimate nonparametrically f X + Y on the basis of random samples from the distributions of X ′ , Y ′ . Using the observed data, we suggest an estimator of f X + Y and show that it is consistent with respect to the mean integrated squared error. Under some conditions restricted to the smoothness of the noises as well as of the variable X + Y, we derive some upper and lower bounds on the convergence rate of the error. We also conduct some simulations to illustrate the efficient of our method.

PD-Type Iterative Learning Control with Adaptive Learning Gains for High-Performance Load Torque Tracking of Electric Dynamic Load Simulator

To realize the high-performance load torque tracking of an electric dynamic load simulator system with random measurement noises and strong position disturbances, a PD-type iterative learning control (ILC) algorithm with adaptive learning gains is proposed in this paper. With the principle of system analyzing, a nonlinear discrete state-space model is established. The adaptive learning gains is used to suppress the effects of periodic disturbances and random measurement noises on the load torque tracking performance. A traditional PD feedback controller in parallel with the proposed ILC is designed to stabilize the system and render the ILC converge quickly. The convergence analysis of the proposed control method ensures the stability of the system. Compared with the fixed learning gains, the experiment results show that the proposed control method has better load torque tracking performance and can effectively suppress the adverse effects of periodic and aperiodic disturbances on tracking accuracy.

Instantaneous frequency extraction in time-varying structures using a maximum gradient method

A method is proposed for the identification of instantaneous frequencies (IFs) in time-varying structures. The proposed method combines a maximum gradient algorithm and a smoothing operation. The maximum gradient algorithm is designed to extract the wavelet ridges of response signals. The smoothing operation, based on a polynomial curve fitting algorithm and a threshold method, is employed to reduce the effects of random noises. To verify the effectiveness and accuracy of the proposed method, a numerical example of a signal with two frequency modulated components is investigated and an experimental test on a steel cable with time-varying tensions is also conducted. The results demonstrate that the proposed method can extract IFs from the noisy multi-component signals and practical response signals successfully. In addition, the proposed method can provide a better IF identification results than the standard synchrosqueezing wavelet transform.

Improvement of the sensitivity in velocity sensing using dynamic speckles

Dynamic speckle patterns can be used for imaging of relative velocity of moving objects in fields of biomedical and mechanical measurements. In spite of the widespread use of this method, the effect of speckle size on velocity sensing has not fully been estimated so far. In addition, effects of speckle contrast and random noises on the sensitivity of velocity sensing have not been investigated yet. In the present study, we estimated condition of image processing of speckle patterns for reducing effects of random noises with relation to linearity and sensitivity in velocity sensing. We further introduced binarization of the speckle pattern to improve the sensitivity in velocity sensing. Experiments were conducted for sample models using a diffusive plate and fluid flows to confirm the feasibility of the proposed method.

Analysis of blood flow covering a wide region of velocity in laser speckle image sensing

Imaging technique based on bio-speckles is useful for visualizing blood flow distribution. We have proposed so far temporal contrast factor (TCF) and inversed temporal contrast factor (ITCF) for imaging of slow and fast blood flows, respectively. However, selection of TCF and ITCF depends on user’s experience. Additionally, these parameters are significantly influenced by the speckle contrast and random noises. In the present study, we propose a method for automatic selection of a proper parameter for imaging blood flow in an unknown velocity region with reduced effects of speckle contrast and random noises. Experiments are conducted for a sample model using ground-glass plates, human wrist, and human finger to confirm the feasibility of the proposed method.

Optimal harvesting of a stochastic mutualism model with Lévy jumps

In this letter, a stochastic mutualism model with Lévy jumps and harvesting is considered. Under some simple assumptions, sufficient and necessary criteria for the existence of optimal harvesting policy are established. The optimal harvesting effort and the maximum of sustainable yield are also obtained. The effects of random noises on the optimal harvesting of the model are discussed and some numerical simulations are introduced to illustrate the main results.

Periodicity of non-expanding piecewise linear maps and effects of random noises

We consider non-expanding piecewise linear maps described as Sα, β(x) = αx + β(mod 1), where 0 < α, β < 1. This transformation is known as the Nagumo– Sato (NS) model. The NS model corresponds to a special case of Caianiello's model, and it describes simplified dynamics of a single neuron. In this thesis, we can describe regions explicitly in which Sα, β has a periodic point with period n for an arbitrary integer n, and clarify that these regions are associated with the Farey series, which are already observed experimentally in [5]. We shall explain a mathematical rigorous reason for a complexity of a periodicity of Sα, β that is observed by Aihara-Oku. Furthermore, we introduce an example that noises induce the asymptotic periodicity in the sense of Lasota-Machey, even if an original transformation has no periodicity.

Equifinality in parameterization of process‐based biogeochemistry models: A significant uncertainty source to the estimation of regional carbon dynamics

Numerical biogeochemistry models suffer from equifinality problem in their parameterizations using eddy flux tower data, which can contribute to diverged estimates of regional carbon dynamics. To date, the uncertainty in regional estimates propagated from the site‐level parameterization equifinality has not been well characterized. Here, we use a process‐based biogeochemistry model, the Terrestrial Ecosystem Model (TEM), and a Bayesian inference framework to quantify the influence of parameterization equifinality on the estimates of carbon dynamics in boreal forest ecosystems during the 20th century. By conducting three groups of ensemble regional simulations, we find that, given a certain climate data set being used, (1) in comparison to the effects of random noises in climate forcing, the regional uncertainty due to parameterization equifinality is remarkably greater, (2) the parameterization equifinality results in drastically different decadal variations in the estimation of carbon storage during the 20th century, and (3) the uncertainties associated with parameterization equifinality and random noises in climate forcing vary both spatially and seasonally. We conclude that the equifinality from site‐level parameterizations in biogeochemistry models is an important uncertainty source in estimating regional carbon dynamics. Simply extrapolating the site‐level parameterization to large spatial and temporal scales could bias the regional estimates irrespective of regional climate data sets used in our analysis. Ensemble process‐based biogeochemistry model simulations conditioned on observed ecosystem fluxes with Bayesian inference techniques could provide more serious estimates of regional carbon dynamics and their associated uncertainties.

Optical tomographic mapping of cerebral haemodynamics by means of time-domain detection: methodology and phantom validation

One of the primary applications of diffuse optical imaging is to localize and quantify the changes in the cerebral oxygenation during functional brain activation. Up to now, data from an optical imager are simply presented as a two-dimensional (2D) topographic map using the modified Beer–Lambert law that assumes homogeneous optical properties beneath each optode. Due to the highly heterogeneous nature of the optical properties in the brain, the assumption is evidently invalid, leading to both low spatial resolution and inaccurate quantification in the assessment of haemodynamic changes. To cope with these difficulties, we propose a nonlinear tomographic image reconstruction algorithm for a two-layered slab geometry that uses time-resolved reflected light. The algorithm is based on the previously developed generalized pulse spectrum technique, and implemented within a semi-three-dimensional (3D) framework to conform to the topographic visualization and to reduce computational load. We demonstrate the advantages of the algorithm in quantifying simulated changes in haemoglobin concentrations and investigate its robustness to the uncertainties in the cortical structure and optical properties, as well as the effects of random noises on image quality. The methodology is also validated by experiments using a solid layered phantom.

Spectral finite elements for vibrating rods and beams with random field properties

The classical stochastic Helmholtz equation grasps, through the random field of the refraction index, the spatial variability in the mass density but not the variability in elastic moduli or geometric parameters. In contradistinction to this restriction, the present analysis accounts for the spatial randomness of mass density as well as those of elastic properties and cross-sectional geometric properties of rods undergoing longitudinal vibrations and of Timoshenko beams in flexural vibrations. All the material variabilities are described here by random Fourier series with a typical (average) characteristic size of inhomogeneity d, which is either smaller, comparable to, or larger than the wavelength. The third length scale entering the problem, but kept constant, is the rod or beam length. We investigate the relative effects of random noises in all the material parameters on the spectral stiffness matrices associated with rods and beams for a very wide range of frequencies.

Mine avoidance techniques for underwater vehicles

It is shown that by implementing certain mine avoidance techniques, an underwater vehicle equipped with an obstacle avoidance sonar (OAS) and a navigation system can safely navigate an unknown minefield. The mine avoidance techniques take into account the physical limitations of the sonar and the navigation system, the maneuverability constraints on the underwater vehicle, and the required safe standoff distance from all mines. Extensive computer simulations have verified the mine avoidance capability in more than 50 different minefields. In all 50 simulations the vehicle reached a predetermined end point and maintained at least the specified, minimum safe standoff distance from each mine. The simulation accurately models the major difficulties associated with the sonar, the navigation system, and the vehicle dynamics. The sonar model includes surface, bottom, and volume reverberation; thermal, ambient, and flow noises; actual receiver and projector beam patterns; and false alarms and missed detections. The navigation system model contains the effects of biases, random noises, and scale factor errors. The vehicle dynamic model simulates angular velocities and accelerations associated with underwater vehicles. >

Automatic ultrasonic defect locating and sizing by a probabilistic approach with high accuracy

Based on dynamic Time-Of-Flight Measurement with scanning transducers, a probabilistic approach has been proposed for automatic defect locating and sizing. This approach significantly reduces the inconsistent effects of human factors on results. The probabilistic approach suppresses the effects of random noises as well as measurement errors and makes possible the identification and elimination of spurious indications from mode-converted waves. No prior knowledge of an unknown defect is required to obtain a sizing image for the distribution of the unknown defects under a scan line. The probabilistic approach has been applied to the inspection of three testblocks, which include different types of defects, with the location and sizing accuracy as expected.