Noisy Motion Research Articles

Surface-energy ratchet motor with geometrical symmetry driven by biased random walk

A geometrically symmetric gear with asymmetric surface wettability exhibits one-way spin on a vibrating water bed. On the side face of the gear, a parafilm was coated to create asymmetry in the surface energy. The gear shows fluctuations in both directions within a shorter timescale; however, for a longer timescale, the gear exhibits a one-way spin. This unique motion is generated by a stochastic process with a biased driving force produced by the interaction between the vibrating water surface and the side face of the gear. This new model resembles an active Brownian ratchet. Until now, most ratchet motors, which obtain regular motion from nonthermal fluctuations, utilize a geometrical ratchet structure. However, in this study, the surface energy forms a ratchet that rectifies the noisy motion.

Noise and vestibular perception of passive self-motion.

Noise defined as random disturbances is ubiquitous in both the external environment and the nervous system. Depending on the context, noise can degrade or improve information processing and performance. In all cases, it contributes to neural systems dynamics. We review some effects of various sources of noise on the neural processing of self-motion signals at different stages of the vestibular pathways and the resulting perceptual responses. Hair cells in the inner ear reduce the impact of noise by means of mechanical and neural filtering. Hair cells synapse on regular and irregular afferents. Variability of discharge (noise) is low in regular afferents and high in irregular units. The high variability of irregular units provides information about the envelope of naturalistic head motion stimuli. A subset of neurons in the vestibular nuclei and thalamus are optimally tuned to noisy motion stimuli that reproduce the statistics of naturalistic head movements. In the thalamus, variability of neural discharge increases with increasing motion amplitude but saturates at high amplitudes, accounting for behavioral violation of Weber's law. In general, the precision of individual vestibular neurons in encoding head motion is worse than the perceptual precision measured behaviorally. However, the global precision predicted by neural population codes matches the high behavioral precision. The latter is estimated by means of psychometric functions for detection or discrimination of whole-body displacements. Vestibular motion thresholds (inverse of precision) reflect the contribution of intrinsic and extrinsic noise to perception. Vestibular motion thresholds tend to deteriorate progressively after the age of 40 years, possibly due to oxidative stress resulting from high discharge rates and metabolic loads of vestibular afferents. In the elderly, vestibular thresholds correlate with postural stability: the higher the threshold, the greater is the postural imbalance and risk of falling. Experimental application of optimal levels of either galvanic noise or whole-body oscillations can ameliorate vestibular function with a mechanism reminiscent of stochastic resonance. Assessment of vestibular thresholds is diagnostic in several types of vestibulopathies, and vestibular stimulation might be useful in vestibular rehabilitation.

Investigating the bactria motion in an asymmetric ratchet channel.

Extracting network from noisy motion is an old subject that goes back to the work by M. V. Smoluchovski. A so called ratchet and pawl mechanism shows that, it is impossible to extract work from equilibrium noisy motion. In order to achieve network from noisy motion, one needs to simultaneously break the translation symmetry and also derive the system to out of equilibrium conditions.

Adaptive measuring trajectory for scanning lidars: proof of concept

Application of nacelle-mounted long-range scanning lidars is facing the challenge that the nacelle motion causes deviations in the measuring trajectories. Such lidars are very sensitive to even the slightest trajectory deviation due to its long measuring range. Motivated by this need, we propose the concept of adaptive measuring trajectory and explore its use for eliminating the effect of the rotational motion of the lidar on the target measuring trajectory. This work first aims to experimentally test the performance of adaptive measuring trajectory under ideal conditions by installing a scanning lidar on a 6DOF motion platform to model the lidar motion on the turbine nacelle. The real trajectory with and without correction is measured by a camera. The comparison confirms that the adaptive measuring trajectory could stablize the target measuring trajectory given the lidar motion. Then, the possibility of using Kalman filter to estimate lidar motion given noisy motion measurements is investigated. The results show great potential of Kalman filter for lidar motion estimation, which could be very useful for its future application.

The role of priors in controlling smooth pursuit eye movements of clear and noisy target motions: predictive pursuit does more than compensate for time delays

The role of priors in controlling smooth pursuit eye movements of clear and noisy target motions: predictive pursuit does more than compensate for time delays

Origin of universality in the onset of superdiffusion in Lévy walks

Superdiffusion arises when complicated, correlated, and noisy motion at the microscopic scale conspires to yield peculiar dynamics at the macroscopic scale. It ubiquitously appears in a variety of scenarios, spanning a broad range of scientific disciplines. The approach of superdiffusive systems towards their long-time, asymptotic behavior was recently studied using the Lévy walk of order 1<β<2, revealing a universal transition at the critical βc=3/2. Here, we investigate the origin of this transition and identify two crucial ingredients: a finite velocity which couples the walker's position to time and a corresponding transition in the fluctuations of the number of walks n completed by the walker at time t.Received 22 June 2020Accepted 3 August 2020DOI:https://doi.org/10.1103/PhysRevResearch.2.032042Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasAnomalous diffusionLevy flightsStatistical Physics

A Study on Digital Image Restoration Filters

In this paper, at first a color image is taken Then the image is transformed into a grayscale image. After that, the motion blurring effect is applied to that image according to the image degradation model described in equation 3.the blurring effect can be controlled by a and b components of the model. Then random noise is added in the image via MATLAB programming. Many methods can restore the noisy and motion blurred image: particularly in this paper inverse filtering as well as wiener filtering are implemented for the restoration purpose consequently, both motion blurred and noisy motion blurred image are restored via inverse filtering as well as wiener filtering techniques and the comparison is made among them.

The combined effect of eye movements improve head centred local motion information during walking.

Eye movements play multiple roles in human behaviour-small stabilizing movements are important for keeping the image of the scene steady during locomotion, whilst large scanning movements search for relevant information. It has been proposed that eye movement induced retinal motion interferes with the estimation of self-motion based on optic flow. We investigated the effect of eye movements on retinal motion information during walking. Observers walked towards a target, wearing eye tracking glasses that simultaneously recorded the scene ahead and tracked the movements of both eyes. By realigning the frames of the recording from the scene ahead, relative to the centre of gaze, we could mimic the input received by the retina (retinocentric coordinates) and compare this to the input received by the scene camera (head centred coordinates). We asked which of these coordinate frames resulted in the least noisy motion information. Motion noise was calculated by finding the error in between the optic flow signal and a noise-free motion expansion pattern. We found that eye movements improved the optic flow information available, even when large diversions away from target were made.

Cerebellar Degeneration Increases Visual Influence on Dynamic Estimates of Verticality

Our perception of verticality relies on combining sensory information from multiple sources. Neuronal recordings in animals implicate the cerebellum in the process, yet disease of the human cerebellum was not found to affect this perception. Here we show that a perceptual disturbance of verticality is indeed present in people with a genetically determined and pure form of cerebellar degeneration (spinocerebellar ataxia type 6; SCA 6), but is only revealed under dynamic visual conditions. Participants were required to continuously orient a visually displayed bar to vertical while the bar angle was perturbed by a low-frequency random signal and a random dot pattern rotated in their visual periphery. The random dot pattern was rotated at one of two velocities (4°/s and 16°/s), traveling with either coherent or noisy motion. Perceived vertical was biased by visual rotation in healthy participants, particularly in a more elderly group, but SCA 6 participants were biased more than both groups. The bias was reduced by visual noise, but more so for SCA 6 participants than young controls. Distortion of verticality by visual rotation stems from the stimulus creating an illusion of self-rotation. We modeled this process using a maximum-likelihood sensory cue-combination model operating on noisy visual- and vestibular-rotation signals. The observed effects of visual rotation and visual noise could be compellingly explained by cerebellar degeneration, and to a lesser extent aging, causing an increase in central vestibular noise. This is consistent with the human cerebellum operating on dynamic vestibular signals to inform the process that estimates which way is up.

Altitude Estimation Using Multipath With a Two-Dimensional Radar Over Spherical Earth

This paper presents a target altitude estimation approach based on multipath detections with range and range rate information from a two-dimensional (2-D) airborne radar. Using multipath detections has to deal with the following challenges: first, low probability of detection (PD) of the indirect path signal reflected by the sea surface; second, lack of an analytical expression for the relationship between the target motion parameter/state and the indirect path measurements over spherical earth; and third, unknown range measurement error standard deviation (s.d.) for the indirect path signal. Two estimators are developed: an adaptive multiple model iterated least squares (AMM-ILS) estimator for motion parameter estimation (noiseless target motion) and an AMM unscented Kalman filter (AMM-UKF) for dynamic state estimation (noisy target motion). Altitude estimate accuracy is studied based on simulation data. It shows that the AMM-ILS yields statistically efficient estimates (i.e., optimal and with a quantifiable accuracy), but this is not always true for the AMM-UKF. For a long distant target (e.g., 300 km) with very low indirect path PD (0.1 and 0.2), the AMM-UKF is inconsistent and inefficient statistically, due to biased estimation. The altitude Cramer Rao lower bounds from a 3-D radar with range and elevation measurements are also computed for comparison. The results show that the 2-D radar can provide better altitude estimates than the 3-D radar in most of the cases, except when the 3-D radar has accurate elevation measurement (with error s.d. $\sigma _e=0.5^{\circ }$ ) and the 2-D radar has very low indirect path PD (PD = 0.1).

Unconscious decisional learning improves unconscious information processing

The idea that unconscious input can result in long-term learning or task improvement has been debated for decades, yet there is still little evidence to suggest that learning outside of awareness can produce meaningful changes to decision-making. Here we trained participants using noisy motion stimuli, which require the gradual accumulation of information until a decision can be reached. These stimuli were suppressed from conscious awareness by simultaneously presenting a dynamic dichoptic mask. We show that a short period of training on either a partially or fully suppressed motion stimulus resulted in improved accuracy when tested on a partially suppressed motion stimulus traveling in the orthogonal direction. We found this improvement occurred even when performance on the training task was at chance. Performance gains generalized across motion directions, suggesting that the improvement was the result of changes to the decisional mechanisms rather than perceptual. Interestingly, unconscious learning had a stronger effect on unconscious, compared to conscious decisional accumulation. We further show that a conscious coherent percept is necessary to reap the benefits of unconscious learning. Together, these data suggest that unconscious decisional processing can be improved via training.

Parametric study of virtual stick balancing based on a delayed PD model

Virtual stick balancing by human subjects is analyzed with special attention to the reaction time delay and the sensory dead zones. A pendulum-cart system displayed on a computer screen is controlled via an optical computer mouse. Realization of the balancing task involves additional computational delay in the feedback loop. The measurement results are compared to numerical simulations based on a model with delayed proportional-derivative (PD) feedback. Balancing skills are measured using the critical length, i.e., the length of the shortest stick that subjects can balance. The critical length obtained by the virtual balancing tests is larger by a factor of 2 ~ 3.5 than the critical length for mathematical model with delayed PD control. Time domain simulations for a model with dead zones both for the position and for the velocity show a limit cycle type of oscillation rather than a realistic noisy (or chaotic) motion.

Prior expectations modulate unconscious evidence accumulation.

Unconscious processes have been shown to affect both perception and behaviour. However, the flexibility of such processes remains unknown. Here we investigate whether unconscious decisional processes can adapt to the utility of sensory information. To this end, we had participants gradually accumulate information from noisy motion stimuli, until a decision was reached. We titrated conscious awareness of these stimuli by simultaneously presenting a dynamic dichoptic mask. Crucially, we manipulated the likelihood that the suppressed portion of each presentation would contain useful information. Our results show that the statistics of the environment can be used to modulate unconscious evidence accumulation, resulting in faster choices. Furthermore, computational modelling revealed that this modulation is due to a change in the quality of unconscious evidence accumulation, rather than a conscious change in strategy. Together, these results indicate that unconscious decisional mechanisms are capable of optimising performance by flexibly adapting to the statistical environment.

Information foraging for perceptual decisions.

We tested an information foraging framework to characterize the mechanisms that drive active (visual) sampling behavior in decision problems that involve multiple sources of information. Experiments 1 through 3 involved participants making an absolute judgment about the direction of motion of a single random dot motion pattern. In Experiment 4, participants made a relative comparison between 2 motion patterns that could only be sampled sequentially. Our results show that: (a) Information (about noisy motion information) grows to an asymptotic level that depends on the quality of the information source; (b) The limited growth is attributable to unequal weighting of the incoming sensory evidence, with early samples being weighted more heavily; (c) Little information is lost once a new source of information is being sampled; and (d) The point at which the observer switches from 1 source to another is governed by online monitoring of his or her degree of (un)certainty about the sampled source. These findings demonstrate that the sampling strategy in perceptual decision-making is under some direct control by ongoing cognitive processing. More specifically, participants are able to track a measure of (un)certainty and use this information to guide their sampling behavior.

Improved Shear Wave Group Velocity Estimation Method Based on Spatiotemporal Peak and Thresholding Motion Search.

Quantitative ultrasound elastography is increasingly being used in the assessment of chronic liver disease. Many studies have reported ranges of liver shear wave velocity values for healthy individuals and patients with different stages of liver fibrosis. Nonetheless, ongoing efforts exist to stabilize quantitative ultrasound elastography measurements by assessing factors that influence tissue shear wave velocity values, such as food intake, body mass index, ultrasound scanners, scanning protocols, and ultrasound image quality. Time-to-peak (TTP) methods have been routinely used to measure the shear wave velocity. However, there is still a need for methods that can provide robust shear wave velocity estimation in the presence of noisy motion data. The conventional TTP algorithm is limited to searching for the maximum motion in time profiles at different spatial locations. In this paper, two modified shear wave speed estimation algorithms are proposed. The first method searches for the maximum motion in both space and time [spatiotemporal peak (STP)]; the second method applies an amplitude filter [spatiotemporal thresholding (STTH)] to select points with motion amplitude higher than a threshold for shear wave group velocity estimation. The two proposed methods (STP and STTH) showed higher precision in shear wave velocity estimates compared with TTP in phantom. Moreover, in a cohort of 14 healthy subjects, STP and STTH methods improved both the shear wave velocity measurement precision and the success rate of the measurement compared with conventional TTP.

Preface

This paper is an introduction to the eleven works of the special issue on Quantum and Classical Frontiers of Noise. The weather, and its butterfly effect, is the typical example that explain why many natural phenomena are, in fact, not predictable with certainty. Noise in classical or quantum phenomena, understood as the difference between the empirical output of an experiment and its statistical prediction, is a measure of such uncertainty. One of the great contributions of noise appeared in 1905 when Einstein showed how the noisy (Brownian) motion of a dust particle seen in a microscope provided uncontroversial evidences on the existence of the (unseen) atoms. The aim of this special issue is to provoke discussions on how noise can help us to better understand (the reality behind) classical and quantum theories, and the frontier between them.

Perceptual learning modifies the functional specializations of visual cortical areas

Training can improve performance of perceptual tasks. This phenomenon, known as perceptual learning, is strongest for the trained task and stimulus, leading to a widely accepted assumption that the associated neuronal plasticity is restricted to brain circuits that mediate performance of the trained task. Nevertheless, learning does transfer to other tasks and stimuli, implying the presence of more widespread plasticity. Here, we trained human subjects to discriminate the direction of coherent motion stimuli. The behavioral learning effect substantially transferred to noisy motion stimuli. We used transcranial magnetic stimulation (TMS) and functional magnetic resonance imaging (fMRI) to investigate the neural mechanisms underlying the transfer of learning. The TMS experiment revealed dissociable, causal contributions of V3A (one of the visual areas in the extrastriate visual cortex) and MT+ (middle temporal/medial superior temporal cortex) to coherent and noisy motion processing. Surprisingly, the contribution of MT+ to noisy motion processing was replaced by V3A after perceptual training. The fMRI experiment complemented and corroborated the TMS finding. Multivariate pattern analysis showed that, before training, among visual cortical areas, coherent and noisy motion was decoded most accurately in V3A and MT+, respectively. After training, both kinds of motion were decoded most accurately in V3A. Our findings demonstrate that the effects of perceptual learning extend far beyond the retuning of specific neural populations for the trained stimuli. Learning could dramatically modify the inherent functional specializations of visual cortical areas and dynamically reweight their contributions to perceptual decisions based on their representational qualities. These neural changes might serve as the neural substrate for the transfer of perceptual learning.

Motion vector outlier removal using dissimilarity measure

Global motion estimation, being one of the most important tools in video processing field with many applications, is mainly carried out in pixel or compressed domain. Since those based on the pixels have drawbacks such as high computational complexity, most researches are oriented to the compressed domain in which motion vectors are utilized. On the other hand, there are many unwanted existing outliers in motion vector based global motion estimation because of noise or foreground effects. In this paper, proposed motion vector dissimilarity measure is used to remove the outliers to provide fast and accurate motion vector based global motion estimation. Performance of the dissimilarity measure is further improved by using different neighborhood orientations. Also phase correlation of motion vectors are effectively utilized. Therefore small noisy motion vectors are easily detected and different orientations contribute to both performance and low latency. Experiments using the proposed method achieve more accurate results with less computational complexity compared to the state of the art methods.

Noise-robust recognition of wide-field motion direction and the underlying neural mechanisms in Drosophila melanogaster.

Appropriate and robust behavioral control in a noisy environment is important for the survival of most organisms. Understanding such robust behavioral control has been an attractive subject in neuroscience research. Here, we investigated the processing of wide-field motion with random dot noise at both the behavioral and neuronal level in Drosophila melanogaster. We measured the head yaw optomotor response (OMR) and the activity of motion-sensitive neurons, horizontal system (HS) cells, with in vivo whole-cell patch clamp recordings at various levels of noise intensity. We found that flies had a robust sensation of motion direction under noisy conditions, while membrane potential changes of HS cells were not correlated with behavioral responses. By applying signal classification theory to the distributions of HS cell responses, however, we found that motion direction under noise can be clearly discriminated by HS cells, and that this discrimination performance was quantitatively similar to that of OMR. Furthermore, we successfully reproduced HS cell activity in response to noisy motion stimuli with a local motion detector model including a spatial filter and threshold function. This study provides evidence for the physiological basis of noise-robust behavior in a tiny insect brain.

Motion-compensated coding and frame rate up-conversion: models and analysis.

Block-based motion estimation (ME) and motion compensation (MC) techniques are widely used in modern video processing algorithms and compression systems. The great variety of video applications and devices results in diverse compression specifications, such as frame rates and bit rates. In this paper, we study the effect of frame rate and compression bit rate on block-based ME and MC as commonly utilized in inter-frame coding and frame rate up-conversion (FRUC). This joint examination yields a theoretical foundation for comparing MC procedures in coding and FRUC. First, the video signal is locally modeled as a noisy translational motion of an image. Then, we theoretically model the motion-compensated prediction of available and absent frames as in coding and FRUC applications, respectively. The theoretic MC-prediction error is studied further and its autocorrelation function is calculated, yielding useful separable-simplifications for the coding application. We argue that a linear relation exists between the variance of the MC-prediction error and temporal distance. While the relevant distance in MC coding is between the predicted and reference frames, MC-FRUC is affected by the distance between the frames available for interpolation. We compare our estimates with experimental results and show that the theory explains qualitatively the empirical behavior. Then, we use the models proposed to analyze a system for improving of video coding at low bit rates, using a spatio-temporal scaling. Although this concept is practically employed in various forms, so far it lacked a theoretical justification. We here harness the proposed MC models and present a comprehensive analysis of the system, to qualitatively predict the experimental results.