Journal articles on the topic 'Motion noise'
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KIM, J. S., and L. S. KIM. "Noise Robust Motion Refinement for Motion Compensated Noise Reduction." IEICE Transactions on Information and Systems E91-D, no. 5 (May 1, 2008): 1581–83. http://dx.doi.org/10.1093/ietisy/e91-d.5.1581.
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Kesrarat, Darun, and Vorapoj Patanavijit. "Noise resistance territorial intensity-based optical flow using inverse confidential technique on bilateral function." Bulletin of Electrical Engineering and Informatics 10, no. 6 (December 1, 2021): 3240–48. http://dx.doi.org/10.11591/eei.v10i6.3243.
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This paper presents the use of the inverse confidential technique on bilateral function with the territorial intensity-based optical flow to prove the effectiveness in noise resistance environment. In general, the image’s motion vector is coded by the technique called optical flow where the sequences of the image are used to determine the motion vector. But, the accuracy rate of the motion vector is reduced when the source of image sequences is interfered by noises. This work proved that the inverse confidential technique on bilateral function can increase the percentage of accuracy in the motion vector determination by the territorial intensity-based optical flow under the noisy environment. We performed the testing with several kinds of non-Gaussian noises at several patterns of standard image sequences by analyzing the result of the motion vector in a form of the error vector magnitude (EVM) and compared it with several noise resistance techniques in territorial intensity-based optical flow method.
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Kesrarat, Darun, and Vorapoj Patanavijit. "Noise resistance evaluation of spatial-field optical flow using modifying Lorentzian function." Bulletin of Electrical Engineering and Informatics 11, no. 5 (October 1, 2022): 2603–10. http://dx.doi.org/10.11591/eei.v11i5.3815.
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This paper presents the evaluation of the modifying Lorentzian function on the spatial-field optical flow to examine the validity in the noisy domain of motion estimation. In the routine of the motion estimation, the frame’s motion vector is estimated by the optical flow approach where the flow of the image’s frames is caught to estimate the motion vector. Nevertheless, in the noisy domain, the preciseness of the motion vector is weakened. We operated the measurement along with several non-Gaussian noises standards through several styles of the standard image frame. The determination on error vector magnitude (EVM) was taken into account to consider the preciseness of direction and length of the motion vector (MV) in comparison with various noise resistance techniques in spatial-field optical flow approach. In the achievement results, we found that this modifying Lorentzian norm function added up in the optical flow strengthen the degree of preciseness in the estimation of the spatial-field optical flow approach in the noisy domain.
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Fornasari, Simone Francesco, Deniz Ertuncay, and Giovanni Costa. "Seismic background noise levels in the Italian strong-motion network." Natural Hazards and Earth System Sciences 23, no. 10 (October 10, 2023): 3219–34. http://dx.doi.org/10.5194/nhess-23-3219-2023.
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Abstract. The Italian strong-motion network monitors the seismic activity in the region, with more than 585 stations with continuous data acquisition. In this study, we determine the background seismic noise characteristics of the network by using the data collected in 2022. We analyse the spatial and temporal characteristics of the background noise. It is found that most of the stations suffer from anthropogenic noises, since the strong-motion network is designed to capture the peak ground motions in populated areas. Hence, human activities enrich the low periods of noise. Therefore, land usage of the area where the stations are located affects the background noise levels. Stations can be noisier during the day, up to 12 dB, and during the weekday, up to 5 dB, in short periods. In long periods (≥ 5 s), accelerometric stations converge to similar noise levels and there are no significant daily or weekly changes. It is found that more than half of the stations exceed the background noise model designed for strong-motion stations in Switzerland by Cauzzi and Clinton (2013) in at least one of the calculated periods. We also develop an accelerometric seismic background noise model for periods between 0.0124 and 100 s for Italy by using the power spectral densities of the network. The model is in agreement with the background noise model developed by D’Alessandro et al. (2021) using broadband data for Italy in short periods, but in long periods there is no correlation among studies.
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Leung, Tim, and Theodore Zhao. "Multiscale Volatility Analysis for Noisy High-Frequency Prices." Risks 11, no. 7 (June 26, 2023): 117. http://dx.doi.org/10.3390/risks11070117.
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We present a multiscale analysis of the volatility of intraday prices from high-frequency data. Our multiscale framework includes a fractional Brownian motion and microstructure noise as the building blocks. The proposed noisy fractional Brownian motion model is shown to possess a variety of volatility behaviors suitable for intraday price processes. Algorithms for numerical estimation from time series observations are then introduced, with a new Hurst exponent estimator proposed for the noisy fractional Brownian motion model. Using real-world high-frequency price data for a collection of U.S. stocks and ETFs, we estimate the parameters in the noisy fractional Brownian motion and illustrate how the volatility varies over different timescales. The Hurst exponent and noise level also exhibit an intraday pattern whereby the the noise ratio tends to be higher near market close.
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Sutton, Matthew Daniel. "Motion and the Noise." JAAAS: Journal of the Austrian Association for American Studies 1, no. 2 (December 30, 2020): 317–29. http://dx.doi.org/10.47060/jaaas.v1i2.131.
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William Faulkner's dislike of unwanted sound is well documented. The acoustic environment of rural Mississippi amplified irreversibly after the introduction of the automobile, airplane, and automated farm machinery. In his Intruder in the Dust (1948), the jukebox and radio absorb pointed criticism for producing "canned" sounds outside of their "proper" environment. The narrowing gap between town square and dance hall signifies encroaching chaos, as noise drowns out the attenuated "harmony" that keeps elite whites in power and Intruder's African American protagonist Lucas Beauchamp out of the hands of the lynch mob. For Faulkner, the shift in the auditory environment presents both a disruption and an impediment to a system built on white bourgeois ideals. However, Faulkner's pessimism is counterpointed by sociological studies undertaken by Fisk University researchers. The Fisk study identifies the emergence of a blues culture in the Delta whose energy and boundary-crossing impulses illustrate the liberating possibilities of an expanding soundscape. By juxtaposing Faulkner's damning descriptions of "the motion and the noise" with the Fisk University researchers' illuminating fieldwork, this essay interprets a transformative period in the constantly shifting soundscape of the U.S. South. In line with Jacques Attali's dictum that "our music foretells our future," Intruder in the Dust anticipates the cultural upheaval that would energize the Civil Rights Movement. Both in fiction and in fact, the "noise" emanating from jukeboxes and radios in 1940s Mississippi accelerated social change at a volume much higher and a tempo much faster than Faulkner and other gradualists desired.
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Cauzzi, Carlo, and John Clinton. "A High- and Low-Noise Model for High-Quality Strong-Motion Accelerometer Stations." Earthquake Spectra 29, no. 1 (February 2013): 85–102. http://dx.doi.org/10.1193/1.4000107.
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We present reference noise models for high-quality strong-motion accelerometer installations. We use continuous accelerometer data to derive very broadband (50 Hz–100 s) high- and low-noise models. The proposed noise models are compared (1) to the broadband seismometer Peterson (1993) noise models; (2) the datalogger self-noise and background noise levels at existing Swiss and Southern California strong-motion stations; and (3) typical earthquake signals recorded in Switzerland and worldwide. The accelerometer low-noise model (ALNM) is dominated by instrument noise from the sensor and datalogger. The accelerometer high-noise model (AHNM) reflects (1) at high frequencies the acceptable site noise in urban areas, (2) at mid-periods the microseismal peaks and (3) at long periods the maximum noise observed from well-insulated sensor/datalogger systems placed in vault quality sites. This study also provides confirmation of the remarkable capability of modern strong-motion accelerometers to record low-amplitude ground motions with seismic observation quality over a broad frequency range.
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Skurowski, Przemysław, and Magdalena Pawlyta. "On the Noise Complexity in an Optical Motion Capture Facility." Sensors 19, no. 20 (October 13, 2019): 4435. http://dx.doi.org/10.3390/s19204435.
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Optical motion capture systems are state-of-the-art in motion acquisition; however, like any measurement system they are not error-free: noise is their intrinsic feature. The works so far mostly employ a simple noise model, expressing the uncertainty as a simple variance. In the work, we demonstrate that it might be not sufficient and we prove the existence of several types of noise and demonstrate how to quantify them using Allan variance. Such a knowledge is especially important for using optical motion capture to calibrate other techniques, and for applications requiring very fine quality of recording. For the automated readout of the noise coefficients, we solve the multidimensional regression problem using sophisticated metaheuristics in the exploration-exploitation scheme. We identified in the laboratory the notable contribution to the overall noise from white noise and random walk, and a minor contribution from blue noise and flicker, whereas the violet noise is absent. Besides classic types of noise we identified the presence of the correlated noises and periodic distortion. We analyzed also how the noise types scale with an increasing number of cameras. We had also the opportunity to observe the influence of camera failure on the overall performance.
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Nakadai, Kazuhiro, Taiki Tezuka, and Takami Yoshida. "Ego-Noise Suppression for Robots Based on Semi-Blind Infinite Non-Negative Matrix Factorization." Journal of Robotics and Mechatronics 29, no. 1 (February 20, 2017): 114–24. http://dx.doi.org/10.20965/jrm.2017.p0114.
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[abstFig src='/00290001/11.jpg' width='300' text='Ego-noise suppression achieves speech recognition even during motion' ] This paper addresses ego-motion noise suppression for a robot. Many ego-motion noise suppression methods use motion information such as position, velocity, and the acceleration of each joint to infer ego-motion noise. However, such inferences are not reliable, since motion information and ego-motion noise are not always correlated. We propose a new framework for ego-motion noise suppression based on single channel processing using only acoustic signals captured with a microphone. In the proposed framework, ego-motion noise features and their numbers are automatically estimated in advance from an ego-motion noise input using Infinite Non-negative Matrix Factorization (INMF), which is a non-parametric Bayesian model that does not use explicit motion information. After that, the proposed Semi-Blind INMF (SB-INMF) is applied to an input signal that consists of both the target and ego-motion noise signals. Ego-motion noise features, which are obtained with INMF, are used as inputs to the SB-INMF, and are treated as the fixed features for extracting the target signal. Finally, the target signal is extracted with SB-INMF using these newly-estimated features. The proposed framework was applied to ego-motion noise suppression on two types of humanoid robots. Experimental results showed that ego-motion noise was effectively and efficiently suppressed in terms of both signal-to-noise ratio and performance of automatic speech recognition compared to a conventional template-based ego-motion noise suppression method using motion information. Thus, the proposed method worked properly on a robot without a motion information interface.**This work is an extension of our publication “Taiki Tezuka, Takami Yoshida, Kazuhiro Nakadai: Ego-motion noise suppression for robots based on Semi-Blind Infinite Non-negative Matrix Factorization, ICRA 2014, pp.6293-6298, 2014.”
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Balachandran, G., and Praveen Kumar Gupta. "FPGA – Based Electrocardiography Signal Analysis System using (FIR) Filter." International Journal of Advance Research and Innovation 8, no. 1 (2020): 44–48. http://dx.doi.org/10.51976/ijari.812008.
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The cardiovascular attack is a more dangerous than other diseases and it is measured by ECG (Electro cardiograph) signals which is like a noisy signal in real time, especially in the field of telemedicine environment. The noisy ECG signals have more motion artifacts, electrical interference, etc. An adaptive filtering approach based on Discrete Wavelet Transform and an artificial neural network is proposed to reduce the noise in ECG signal. The quality of de-noised signal is improved by SVM algorithm. This suggested approach can successfully take out a broad scope of noise and our method achieve up to almost 82% improvement on the SNR of de-noised signals. The MATLAB simulation results shown clearly about the improvement of ECG signal with SNR value.
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MOGHADDAM, MOHSEN EBRAHIMI. "LINEAR MOTION BLUR IDENTIFICATION IN NOISY IMAGES USING BISPECTRUM AND FEED-FORWARD BACK PROPAGATION NEURAL NETWORKS." International Journal of Pattern Recognition and Artificial Intelligence 24, no. 02 (March 2010): 281–302. http://dx.doi.org/10.1142/s0218001410007907.
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Motion blur is one of the most common causes of image corruptions caused by blurring. Several methods have been presented up to now, which precisely identify linear motion blur parameters, but most of them possessed low precision in the presence of the noise. The present paper is aimed to introduce an algorithm for estimating linear motion blur parameters in noisy images. This study presents a method to estimate motion direction by using Radon transform, which is followed by the application of two other different methods to estimate motion length; the first of which is based on one-dimensional power spectrum to estimate parameters of noise free images and the second uses bispectrum modeling in noisy images. A Feed-Forward Back Propagation neural network has been designed on the basis of Weierstrass approximation theorem to model bispectrum and the Delta rule as the network learning rule. The methods were tested on several standard images like Camera man, Lena, Lake, etc. that were degraded by linear motion blur and additive noise. The experimental results have been satisfactory. The proposed method, compared to other related methods, suggests an improvement in the supported lowest SNR and precision of estimation.
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Ord, Terry J., Richard A. Peters, Barbara Clucas, and Judy A. Stamps. "Lizards speed up visual displays in noisy motion habitats." Proceedings of the Royal Society B: Biological Sciences 274, no. 1613 (January 30, 2007): 1057–62. http://dx.doi.org/10.1098/rspb.2006.0263.
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Extensive research over the last few decades has revealed that many acoustically communicating animals compensate for the masking effect of background noise by changing the structure of their signals. Familiar examples include birds using acoustic properties that enhance the transmission of vocalizations in noisy habitats. Here, we show that the effects of background noise on communication signals are not limited to the acoustic modality, and that visual noise from windblown vegetation has an equally important influence on the production of dynamic visual displays. We found that two species of Puerto Rican lizard, Anolis cristatellus and A. gundlachi , increase the speed of body movements used in territorial signalling to apparently improve communication in visually ‘noisy’ environments of rapidly moving vegetation. This is the first evidence that animals change how they produce dynamic visual signals when communicating in noisy motion habitats. Taken together with previous work on acoustic communication, our results show that animals with very different sensory ecologies can face similar environmental constraints and adopt remarkably similar strategies to overcome these constraints.
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Kwon, Ju Hyeok, So Eui Kim, Na Hye Kim, Eui Chul Lee, and Jee Hang Lee. "Preeminently Robust Neural PPG Denoiser." Sensors 22, no. 6 (March 8, 2022): 2082. http://dx.doi.org/10.3390/s22062082.
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Photoplethysmography (PPG) is a simple and cost-efficient technique that effectively measures cardiovascular response by detecting blood volume changes in a noninvasive manner. A practical challenge in the use of PPGs in real-world applications is noise reduction. PPG signals are likely to be compromised by various types of noise, such as scattering or motion artifacts, and removing such compounding noises using a monotonous method is not easy. To this end, this paper proposes a neural PPG denoiser that can robustly remove multiple types of noise from a PPG signal. By casting the noise reduction problem into a signal restoration approach, we aim to achieve a solid performance in the reduction of different noise types using a single neural denoiser built upon transformer-based deep generative models. Using this proposed method, we conducted the experiments on the noise reduction of a PPG signal synthetically contaminated with five types of noise. Following this, we performed a comparative study using six different noise reduction algorithms, each of which is known to be the best model for each noise. Evaluation results of the peak signal-to-noise ratio (PSNR) show that the neural PPG denoiser is superior in three out of five noise types to the performance of conventional noise reduction algorithms. The salt-and-pepper noise type showed the best performance, with the PSNR of the neural PPG denoiser being 36.6080, and the PSNRs of the other methods were 19.8160 and 32.8234. The Poisson noise type performed the worst, showing a PSNR of 33.0090; the PSNRs of other methods were 35.1822 and 33.4795, respectively. Thereafter, an experiment to recover a signal synthesized with two or more of the five noise types was conducted. When the number of mixed noises was two, three, four, and five, the PSNRs were 29.2759, 27.8759, 26.5608, and 25.9402, respectively. Finally, an experiment to recover motion artifacts was also conducted. The synthesized motion artifact signal was created by synthesizing only a certain ratio of the total signal length. As a result of the motion artifact signal restoration, the PSNRs were 25.2872, 22.8240, 21.2901, and 19.9577 at 30%, 50%, 70%, and 90% motion artifact ratios, respectively. In the three experiments conducted, the neural PPG denoiser showed that various types of noise were effectively removed. This proposal contributes to the universal denoising of continuous PPG signals and can be further expanded to denoise continuous signals in the general domain.
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Koivunen, T. "A noise-insensitive motion detector." IEEE Transactions on Consumer Electronics 38, no. 3 (1992): 168–74. http://dx.doi.org/10.1109/30.156679.
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Maddox, John. "Directed motion from random noise." Nature 369, no. 6477 (May 1994): 181. http://dx.doi.org/10.1038/369181a0.
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Ninomiya, Yuichi, and Yoshimichi Ohtsuka. "A motion-compensated noise reducer." Journal of the Institute of Television Engineers of Japan 39, no. 10 (1985): 956–62. http://dx.doi.org/10.3169/itej1978.39.956.
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Gu, Chenyang, Chunhua Ren, and Meilin Zhou. "A novel method to process surface electromyography signal for pedestrian lower limb motion pattern recognition." Transactions of the Institute of Measurement and Control 42, no. 13 (June 2, 2020): 2492–98. http://dx.doi.org/10.1177/0142331220918357.
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Surface electromyography (sEMG) signals mainly contain power line interference (PLI), white Gaussian noise (WGN), and baseline wandering (BW) noise. These noises lead to the problems of poor feature extraction performance and low recognition rate. In this paper, we propose a novel sEMG signal processing method named filtering and self-enhancement algorithm with classical wavelet (FSECW) algorithm, which denoises interference noises of raw signals and improve the pedestrian motion pattern recognition rate. The proposed FSECW algorithm contains two core parts: in the first part, the original signal is reconstructed after four-layer wavelet decomposition. This part decreases the BW noise and enhances the active segment; in the other part, band-pass filtering and lifting wavelet transformation are used to reduce noises inside and outside the sEMG signal band. Then two signals from the above parts are multiplied. Thus, the enhanced filtered signal of the active segment is obtained. After feature extraction, the algorithm uses classical machine learning algorithm for motion pattern recognition. Experimental results show that the proposed FSECW algorithm does not need to set different thresholds for different data sets with the same motion pattern. Moreover, it has better adaptability to conversions of different motion patterns.
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Ahlström, Ulf, and Erik Börjesson. "Segregation of Motion Structure from Random Visual Noise." Perception 25, no. 3 (March 1996): 279–91. http://dx.doi.org/10.1068/p250279.
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A potential source of information about spatial layout, surface slant, and self-motion is provided by transformations of the optic flow field. Theoretical analyses have demonstrated that such flow fields can be decomposed into translation, rotation, expansion, and deformation components. The objective in this study was to investigate the effectiveness of these motions as visual primitives for preattentive scene segregation. The stimuli consisted of two-frame patterns containing 998 dots. A variable number of these dots (5 to 50) were defined as target dots, representing a specific motion structure, with the residual dots defined as a noise field. The observers' task was to identify the location and type of motion structure. On the basis of the number of target dots needed to detect and correctly identify the target structure, it was determined that translation was the most easily detected structure, followed by rotation, expansion, and compression. Deformation was found to have a detection threshold as high as compression. The results are related to other studies of these motion components.
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Boracchi, Giacomo, and Alessandro Foi. "Uniform Motion Blur in Poissonian Noise: Blur/Noise Tradeoff." IEEE Transactions on Image Processing 20, no. 2 (February 2011): 592–98. http://dx.doi.org/10.1109/tip.2010.2062196.
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Zha, Ruosi, and Kai Wang. "Numerical simulation of flow-induced noise generation and propagation of a floating offshore wind turbine with prescribed pitch motion." Journal of the Acoustical Society of America 154, no. 4_supplement (October 1, 2023): A283. http://dx.doi.org/10.1121/10.0023533.
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It is important to evaluate flow-induced noise emitted due to operations of floating offshore wind turbines since noise pollution can damage the environments of seabirds and aquatic species. This paper studies the aerodynamic and aeroacoustic performance of a laboratory three-blade wind turbine model with an airfoil profile of NREL S826. The wind turbine blade rotation motion is coupled with the prescribed pitch motion of the floating offshore wind power platform. The influence of prescribed pitch motions on the noise generated by wind turbine blades and noise propagation was discussed. Adopting the computational fluid dynamics (CFD) method, the aerodynamic noise field on a blade is simulated by the improved delayed detached eddy simulation (IDDES) model and Ffowcs Williams–Hawkings (FW-H) acoustic analogy. The overset grid technique is adopted to simulate the six degrees of freedom (DoFs) motions of the floating offshore wind turbine model. The sound source distributions on blades for noise generation are obtained, and the flow-induced noise propagation characteristics are analyzed. It is found that the influence of the pitch amplitude and frequency on the flow-induced noise field as well as the wake vortex characteristics should not be neglected.
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Osborne, Leslie C., and Stephen G. Lisberger. "Spatial and Temporal Integration of Visual Motion Signals for Smooth Pursuit Eye Movements in Monkeys." Journal of Neurophysiology 102, no. 4 (October 2009): 2013–25. http://dx.doi.org/10.1152/jn.00611.2009.
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To probe how the brain integrates visual motion signals to guide behavior, we analyzed the smooth pursuit eye movements evoked by target motion with a stochastic component. When each dot of a texture executed an independent random walk such that speed or direction varied across the spatial extent of the target, pursuit variance increased as a function of the variance of visual pattern motion. Noise in either target direction or speed increased the variance of both eye speed and direction, implying a common neural noise source for estimating target speed and direction. Spatial averaging was inefficient for targets with >20 dots. Together these data suggest that pursuit performance is limited by the properties of spatial averaging across a noisy population of sensory neurons rather than across the physical stimulus. When targets executed a spatially uniform random walk in time around a central direction of motion, an optimized linear filter that describes the transformation of target motion into eye motion accounted for ∼50% of the variance in pursuit. Filters had widths of ∼25 ms, much longer than the impulse response of the eye, and filter shape depended on both the range and correlation time of motion signals, suggesting that filters were products of sensory processing. By quantifying the effects of different levels of stimulus noise on pursuit, we have provided rigorous constraints for understanding sensory population decoding. We have shown how temporal and spatial integration of sensory signals converts noisy population responses into precise motor responses.
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DAWSON, DONALD A., ZENGHU LI, and HAO WANG. "A DEGENERATE STOCHASTIC PARTIAL DIFFERENTIAL EQUATION FOR THE PURELY ATOMIC SUPERPROCESS WITH DEPENDENT SPATIAL MOTION." Infinite Dimensional Analysis, Quantum Probability and Related Topics 06, no. 04 (December 2003): 597–607. http://dx.doi.org/10.1142/s0219025703001377.
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A purely atomic superprocess with dependent spatial motion is characterized as the pathwise unique solution of a stochastic partial differential equation, which is driven by a time-space white noise defining the spatial motion and a sequence of independent Brownian motions defining the branching mechanism.
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Zhang, Qifei, Lingjian Fu, and Linyue Gu. "A Cascaded Convolutional Neural Network for Assessing Signal Quality of Dynamic ECG." Computational and Mathematical Methods in Medicine 2019 (October 20, 2019): 1–12. http://dx.doi.org/10.1155/2019/7095137.
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Motion artifacts and myoelectrical noise are common issues complicating the collection and processing of dynamic electrocardiogram (ECG) signals. Recent signal quality studies have utilized a binary classification metric in which ECG samples are determined to either be clean or noisy. However, the clinical use of dynamic ECGs requires specific noise level classification for varying applications. Conventional signal processing methods, including waveform discrimination, are limited in their ability to remove motion artifacts and myoelectrical noise from dynamic ECGs. As such, a novel cascaded convolutional neural network (CNN) is proposed and demonstrated for application to the five-classification problem (low interference, mild motion artifacts, mild myoelectrical noise, severe motion artifacts, and severe myoelectrical noise). Specifically, this study finally categorizes dynamic ECG signals into three levels (low, mild, and severe) using the proposed CNN to meet clinical requirements. The network includes two components, the first of which was used to distinguish signal interference types, while the second was used to distinguish signal interference levels. This model does not require feature engineering, includes powerful nonlinear mapping capabilities, and is robust to varying noise types. Experimental data are composed of private dataset and public dataset, which were acquired from 90,000 four-second dynamic ECG signals and MIT-BIH Arrhythmia database, respectively. Experimental results produced an overall recognition rate of 92.7% on private dataset and 91.8% on public dataset. These results suggest the proposed technique to be a valuable new tool for dynamic ECG auxiliary diagnosis.
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Lilley, G. M. "The generation of sound in turbulent motion." Aeronautical Journal 112, no. 1133 (July 2008): 381–94. http://dx.doi.org/10.1017/s0001924000002347.
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Abstract The present paper reviews and discusses the physical mechanisms of noise generation and reduction in turbulent flows with their applications towards aircraft noise reduction at takeoff and on the approach. This work began in 1948 when Lilley undertook an experimental investigation into the source of jet noise as a necessary precursor to finding methods for the reduction of high speed jet engine noise on civil jet airliners. Westley and Lilley completed this experimental programme in 1951, which included the design of a range of devices for high speed jet noise reduction. It was about this time that similar studies on jet noise were being started elsewhere and in particular by Lassiter and Hubbard in USA. The major contribution to the subject of turbulence as a source of noise came from Sir James Lighthill’s remarkable theory in 1952. In spite of the difficulties attached to theoretical and experimental studies on noise from turbulence, it is shown that with the accumulated knowledge on aerodynamic noise over the past 50 years, together with an optimisation of aircraft operations including flight trajectories, we are today on the threshold of approaching the design of commercial aircraft with turbofan propulsion engines that will not be heard above the background noise of the airport at takeoff and landing beyond 1-2km, from the airport boundary fence. It is evident that in the application of this work, which centres on the physical mechanisms relating to the generation of noise from turbulence and turbulent shear flows, to jet noise, there is not one unique mechanism of jet noise generation for all jet Mach numbers. This author in this publication has concentrated on what appears to be the dominant mechanism of noise generation from turbulence, where the mean convection speeds of the turbulence are subsonic. The noise generated at transonic and supersonic jet speeds invariably involves extra mechanisms, which are only briefly referred to here.
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Li, Ning, and Shuai Wan. "Research on Fast Compensation Algorithm for Interframe Motion of Multimedia Video Based on Manhattan Distance." Journal of Mathematics 2022 (January 5, 2022): 1–10. http://dx.doi.org/10.1155/2022/3468475.
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To improve the video quality, aiming at the problems of low peak signal-to-noise ratio, poor visual effect, and low bit rate of traditional methods, this paper proposes a fast compensation algorithm for the interframe motion of multimedia video based on Manhattan distance. The absolute median difference based on wavelet transform is used to estimate the multimedia video noise. According to the Gaussian noise variance estimation result, the active noise mixing forensics algorithm is used to preprocess the original video for noise mixing, and the fuzzy C-means clustering method is used to smoothly process the noisy multimedia video and obtain significant information from the multimedia video. The block-based motion idea is to divide each frame of the video sequence into nonoverlapping macroblocks, find the best position of the block corresponding to the current frame in the reference frame according to the specific search range and specific rules, and obtain the relative Manhattan distance between the current frame and the background of multimedia video using the Manhattan distance calculation formula. Then, the motion between the multimedia video frames is compensated. The experimental results show that the algorithm in this paper has a high peak signal-to-noise ratio and a high bit rate, which effectively improves the visual effect of the video.
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Yi, Yongwoo, and Sukyung Park. "LINEAR MOTION PERCEPTION ENHANCED BY NOISE(2C1 Musculo-Skeletal Biomechanics IV)." Proceedings of the Asian Pacific Conference on Biomechanics : emerging science and technology in biomechanics 2007.3 (2007): S150. http://dx.doi.org/10.1299/jsmeapbio.2007.3.s150.
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Allard, Remy. "Static noise can improve motion sensitivity." Journal of Vision 20, no. 11 (October 20, 2020): 1636. http://dx.doi.org/10.1167/jov.20.11.1636.
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Bocheva, N., and L. Bojilov. "How noise affects complex motion perception." Journal of Vision 11, no. 11 (September 23, 2011): 706. http://dx.doi.org/10.1167/11.11.706.
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Dakin, S. C., I. Mareschal, and P. J. Bex. "Equivalent noise analysis of motion integration." Journal of Vision 4, no. 8 (August 1, 2004): 106. http://dx.doi.org/10.1167/4.8.106.
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Gingl, Z., L. B. Kiss, and R. Vajtai. "noise generated by scaled Brownian motion." Solid State Communications 71, no. 9 (September 1989): 765–67. http://dx.doi.org/10.1016/0038-1098(89)90082-3.
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Bo Tao and M. T. Orchard. "Removal of motion uncertainty and quantization noise in motion compensation." IEEE Transactions on Circuits and Systems for Video Technology 11, no. 1 (2001): 80–90. http://dx.doi.org/10.1109/76.894287.
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Zanker, Johannes M. "Does Motion Perception Follow Weber's Law?" Perception 24, no. 4 (April 1995): 363–72. http://dx.doi.org/10.1068/p240363.
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The subjective strength of a percept often depends on the stimulus intensity in a nonlinear way. Such coding is often reflected by the observation that the just-noticeable difference between two stimulus intensities (JND) is proportional to the absolute stimulus intensity. This behaviour, which is usually referred to as Weber's Law, can be interpreted as a compressive nonlinearity extending the operating range of a sensory system. When the noise superimposed on a motion stimulus is increased along a logarithmic scale (in order to provide linear steps in subjective difference) in motion-coherency measurements, observers often report that the subjective differences between the various noise levels increase together with the absolute level. This observation could indicate a deviation from Weber's Law for variation of motion strength as obtained by changing the signal-to-noise ratio in random-dot kinematograms. Thus JNDs were measured for the superposition of uncorrelated random-dot patterns on static random-dot patterns and three types of motion stimuli realised as random-dot kinematograms, namely large-field and object ‘Fourier’ motion (all or a group of dots move coherently), ‘drift-balanced’ motion (a travelling region of static dots), and paradoxical ‘theta’ motion (the dots on the surface of an object move in opposite direction to the object itself). For all classes of stimuli, the JNDs when expressed as differences in signal-to-noise ratio turned out to increase with the signal-to-noise ratio, whereas the JNDs given as percentage of superimposed noise appear to be similar for all tested noise levels. Thus motion perception is in accordance with Weber's Law when the signal-to-noise ratio is regarded as stimulus intensity, which in turn appears to be coded in a nonlinear fashion. In general the Weber fractions are very large, indicating a poor differential sensitivity in signal-to-noise measurements.
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Nishiwaki, M. "Generalized Theory of Brake Noise." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 207, no. 3 (July 1993): 195–202. http://dx.doi.org/10.1243/pime_proc_1993_207_180_02.
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Eliminating brake noises generated during brake application is an important issue in the improvement of comfort in vehicles. Brake noises (frequency 1–15 kHz) are often called brake squeal. On the other hand, brake noises (frequency 200–500 Hz) are often called brake groan noise. The studies on drum brake squeal, disc brake squeal and disc brake groan noise have already been presented in references (2), (3) and (4), where theoretical analyses on these brake noises were described. This paper shows that the equations of motion are represented by the same type of equations. Based on these analyses. It is clear that drum brake squeal, disc squeal and disc brake groan noise are generated by the same cause—dynamic instability of the brake system with friction force variations.
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Xiahou, Shiji, Yuhang Liang, Min Ma, and Mingrui Du. "A strong anti-noise segmentation algorithm based on variational mode decomposition and multi-wavelet for wearable heart sound acquisition system." Review of Scientific Instruments 93, no. 5 (May 1, 2022): 054102. http://dx.doi.org/10.1063/5.0071316.
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Wearable devices have now been widely used in the acquisition and measurement of heart sound signals with good effect. However, the wearable heart sound acquisition system (WHSAS) will face more noise compared with the traditional system, such as Gaussian white noise, powerline interference, colored noise, motion artifact noise, and lung sound noise, because users often wear these devices for running, walking, jumping or various strong noise occasions. In a strong noisy environment, WHSAS needs a high-precision segmentation algorithm. This paper proposes a segmentation algorithm based on Variational Mode Decomposition (VMD) and multi-wavelet. In the algorithm, various noises are layered and filtered out using VMD. The cleaner signal is fed into multi-wavelet to construct a time–frequency matrix. Then, the principal component analysis method is applied to reduce the dimension of the matrix. After extracting the high order Shannon envelope and Teager energy envelope of the heart sound, we accurately segment the signals. In this paper, the algorithm is verified through our developing WHSAS. The results demonstrate that the proposed algorithm can achieve high-precision segmentation of the heart sound under a mixed noise condition.
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Fujimaki, Kengo, and Kimiyuki Mitsui. "Measurement and Analysis of Radial Error Motion of a Miniature Ultra-High-Speed Spindle." Key Engineering Materials 381-382 (June 2008): 73–76. http://dx.doi.org/10.4028/www.scientific.net/kem.381-382.73.
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The optical measuring device developed in this study is based on laser autocollimation and can measure the radial error motions of a miniature ultra-high-speed spindle having a maximum rotational speed of 200 krpm. The maximum response frequency of this optical measuring device is over 500 kHz, while the frequency of the radial error motion at 200 krpm is 3.33 kHz for 1 undulation per revolution (upr), and 333 kHz for 100 upr. In addition, the optical measuring device is capable of a highly detailed analysis of the radial error motion of a miniature ultra-high-speed spindle since it has a high signal-to-noise ratio due to little susceptibility to electrical noises.
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Ziti Fariha Mohd Apandi, Ryojun Ikeura, Soichiro Hayakawa, and Shigeyoshi Tsutsumi. "QRS Detection Based on Discrete Wavelet Transform for ECG Signal with Motion Artifacts." Journal of Advanced Research in Applied Sciences and Engineering Technology 40, no. 1 (February 19, 2024): 118–28. http://dx.doi.org/10.37934/araset.40.1.118128.
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Motion artifacts in ECG signals recorded during physical exercises activities can affect the diagnosis of arrhythmia. To minimize the faults in arrhythmia detection, it was important to choose accurate algorithm for detecting QRS in ECG signal with noises produced during physical movements of the patients. Therefore, choosing the QRS detection algorithm with good competency for the signal affected by noises and motion artifacts is needed for arrhythmia detection analysis. The QRS detection based on Discrete Wavelet Transform was implemented and presented in this paper. The performance of the algorithm was assessed using the MIT-BIH Arrhythmia Database and MIT-BIH Noise Stress Database. For the MIT-BIH Arrhythmia database, the average Sensitivity (Se) and positive Predictivity (+P) of the algorithm were 98.24% and 98.61%, respectively. The algorithms had a lower average false negative rate (FNR) than the pan Tompkins algorithm when applied to the MIT-BIH noise stress test database, which was 0.033% for record 118 and 0.032% for record 119, respectively. The results demonstrated that the algorithms perform well when dealing with arrhythmia data and motion artifact at various levels of signal to noise ratio.
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Gu, Jie, Won-jong Kim, and Shobhit Verma. "Nanoscale Motion Control With a Compact Minimum-Actuator Magnetic Levitator." Journal of Dynamic Systems, Measurement, and Control 127, no. 3 (August 24, 2004): 433–42. http://dx.doi.org/10.1115/1.1978906.
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This paper presents a novel magnetically levitated (maglev) stage developed to meet the ever-increasing precise positioning requirements in nanotechnology. This magnetic levitator has 6 independent linear actuators necessary and sufficient to generate all 6-degree-of-freedom (6-DOF) motions. This minimum-actuator design concept led to a compact, 200 g lightweight moving part and the power consumption less than of a Watt, thereby reducing the thermal-expansion error drastically. The analysis and sizing of the magnetic linear actuators and the working principle of the maglev stage are presented. We designed and implemented stabilizing controllers for 6-DOF motion control with the dynamic model based on the actuator analysis. Test results showed nanoscale step responses in all six axes with 2nmrms horizontal position noise. A noise propagation model and analysis identified the capacitance sensor noise and the floor vibration as the dominant noise sources in the vertical and horizontal dynamics, respectively. A comparison of noise performances with controllers closed at 25, 65, and 90 Hz crossover frequencies illustrated how the selection of the control bandwidth should be made for nanopositioning. Experimental results including a 250μm step response, sinusoidal and square-wave trajectories, and spherical motion generation demonstrated the three-dimensional (3D) nanoscale motion-control capability of this minimum-actuator magnetic levitator. Potential applications of this maglev stage include manufacture of nanoscale structures, atomic-level manipulation, assembly and packaging of microparts, vibration isolation for delicate instruments, and seismic motion detection.
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Suryawan, Herry Pribawanto. "SOME BASIC PROPERTIES OF THE NOISE REINFORCED BROWNIAN MOTION." BAREKENG: Jurnal Ilmu Matematika dan Terapan 16, no. 2 (June 1, 2022): 363–70. http://dx.doi.org/10.30598/barekengvol16iss2pp363-370.
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Noise reinforced Brownian motion appears as the universal limit of the step reinforced random walk. This article aims to study some basic properties of the noise reinforced Brownian motion. As main results, we prove integral representation, series expansion, Markov property, and martingale property of the noise reinforced Brownian motion.
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Stadtfeld, Hermann J., and Uwe Gaiser. "The Ultimate Motion Graph." Journal of Mechanical Design 122, no. 3 (August 1, 1999): 317–22. http://dx.doi.org/10.1115/1.1286124.
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The innovation was to develop a gear geometry that reduces or eliminates gear noise and increases the strength of gears. Gear noise is a common problem in all bevel and hypoid gear drives. A variety of expensive gear geometry optimizations are applied daily in all hypoid gear manufacturing plants, to reduce gear noise. In many cases those efforts have little success. Additional expensive finishing operations (lapping after the grinding) are applied to achieve the goal of quiet and stong gear sets. The ultimate motion graph is a concept for modulating the tooth surfaces that uses a physical effect to cancel out the dynamic disturbances that are naturally generated by all up-to-date known kind of gears. The ultimate motion graph also eliminates the sensitivity of gears against deflection under load or displacements because of manufacturing tolerances. Lower dynamic disturbances will also increase the dynamic strength. [S1050-0472(00)00203-8]
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Jha, Sumit Kumar, Rickard Ewetz, Alvaro Velasquez, Arvind Ramanathan, and Susmit Jha. "Shaping Noise for Robust Attributions in Neural Stochastic Differential Equations." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 9 (June 28, 2022): 9567–74. http://dx.doi.org/10.1609/aaai.v36i9.21190.
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Neural SDEs with Brownian motion as noise lead to smoother attributions than traditional ResNets. Various attribution methods such as saliency maps, integrated gradients, DeepSHAP and DeepLIFT have been shown to be more robust for neural SDEs than for ResNets using the recently proposed sensitivity metric. In this paper, we show that neural SDEs with adaptive attribution-driven noise lead to even more robust attributions and smaller sensitivity metrics than traditional neural SDEs with Brownian motion as noise. In particular, attribution-driven shaping of noise leads to 6.7%, 6.9% and 19.4% smaller sensitivity metric for integrated gradients computed on three discrete approximations of neural SDEs with standard Brownian motion noise: stochastic ResNet-50, WideResNet-101 and ResNeXt-101 models respectively. The neural SDE model with adaptive attribution-driven noise leads to 25.7% and 4.8% improvement in the SIC metric over traditional ResNets and Neural SDEs with Brownian motion as noise. To the best of our knowledge, we are the first to propose the use of attributions for shaping the noise injected in neural SDEs, and demonstrate that this process leads to more robust attributions than traditional neural SDEs with standard Brownian motion as noise.
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Webster, Kathryn E., J. Edwin Dickinson, Josephine Battista, Allison M. McKendrick, and David R. Badcock. "Increased internal noise cannot account for motion coherence processing deficits in migraine." Cephalalgia 31, no. 11 (July 18, 2011): 1199–210. http://dx.doi.org/10.1177/0333102411414440.
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Aim: This study aimed to revisit previous findings of superior processing of motion direction in migraineurs with a more stringent direction discrimination task and to investigate whether increased internal noise can account for motion processing deficits in migraineurs. Methods: Groups of 13 migraineurs (4 with aura, 9 without aura) and 15 headache-free controls completed three psychophysical tasks: one detecting coherence in a motion stimulus, one discriminating the spiral angle in a glass pattern and another discriminating the spiral angle in a global-motion task. Internal noise estimates were obtained for all tasks using an N-pass method. Results: Consistent with previous research, migraineurs had higher motion coherence thresholds than controls. However, there were no significant performance differences on the spiral global-motion and global-form tasks. There was no significant group difference in internal noise estimates associated with any of the tasks. Conclusions: The results from this study suggest that variation in internal noise levels is not the mechanism driving motion coherence threshold differences in migraine. Rather, we argue that motion processing deficits may result from cortical changes leading to less efficient extraction of global-motion signals from noise.
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Choi, Changyun, Jongmok Lee, Hyun-Joon Chung, Jaejung Park, Bumsoo Park, Seokman Sohn, and Seungchul Lee. "Directed Graph-based Refinement of Three-dimensional Human Motion Data Using Spatial-temporal Information." International Journal of Precision Engineering and Manufacturing-Smart Technology 2, no. 1 (January 1, 2024): 33–46. http://dx.doi.org/10.57062/ijpem-st.2023.0094.
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With recent advances in computer science, there is an increasing need to convert human motion to digital data for human body research. Skeleton motion data comprise human poses represented via joint angles or joint positions for each frame of captured motion. Three-dimensional (3D) skeleton motion data are widely used in various applications, such as virtual reality, robotics, and action recognition. However, they are often noisy and incomplete because of calibration errors, sensor noise, poor sensor resolution, and occlusion due to clothing. Data-driven models have been proposed to denoise and fill incomplete 3D skeleton motion data. However, they ignore the kinematic dependencies between joints and bones, which can act as noise in determining a marker position. Inspired by a directed graph neural network, we propose a novel model to fill and denoise the markers. This model can directly extract spatial information by creating bone data from joint data and temporal information from the long short-term memory layer. In addition, the proposed model can learn the connectivity between joints via an adaptive graph. On evaluation, the proposed model showed good refinement performance for unseen data with a different type of noise level and missing data in the learning process.
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Wei, Dong, Huaijiang Sun, Bin Li, Jianfeng Lu, Weiqing Li, Xiaoning Sun, and Shengxiang Hu. "Human Joint Kinematics Diffusion-Refinement for Stochastic Motion Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 5 (June 26, 2023): 6110–18. http://dx.doi.org/10.1609/aaai.v37i5.25754.
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Stochastic human motion prediction aims to forecast multiple plausible future motions given a single pose sequence from the past. Most previous works focus on designing elaborate losses to improve the accuracy, while the diversity is typically characterized by randomly sampling a set of latent variables from the latent prior, which is then decoded into possible motions. This joint training of sampling and decoding, however, suffers from posterior collapse as the learned latent variables tend to be ignored by a strong decoder, leading to limited diversity. Alternatively, inspired by the diffusion process in nonequilibrium thermodynamics, we propose MotionDiff, a diffusion probabilistic model to treat the kinematics of human joints as heated particles, which will diffuse from original states to a noise distribution. This process not only offers a natural way to obtain the "whitened'' latents without any trainable parameters, but also introduces a new noise in each diffusion step, both of which facilitate more diverse motions. Human motion prediction is then regarded as the reverse diffusion process that converts the noise distribution into realistic future motions conditioned on the observed sequence. Specifically, MotionDiff consists of two parts: a spatial-temporal transformer-based diffusion network to generate diverse yet plausible motions, and a flexible refinement network to further enable geometric losses and align with the ground truth. Experimental results on two datasets demonstrate that our model yields the competitive performance in terms of both diversity and accuracy.
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BAGLIETTO, GABRIEL, and EZEQUIEL V. ALBANO. "PHASE TRANSITIONS IN THE COLLECTIVE MOTION OF SELF-PROPELLED INDIVIDUALS." International Journal of Modern Physics C 17, no. 03 (March 2006): 395–402. http://dx.doi.org/10.1142/s0129183106008492.
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A model for the displacement of self-driven organisms is studied by means of extensive computer simulations. Local interactions influenced by noisy communications among organisms, leads to the onset of collective motion at low noise levels. When the noise is increased the system undergoes first-order transitions into disordered states of motion. We have also studied the relaxation process between these states. By fitting the time dependence of the order parameter when the system is annealed from a state below coexistence to another above it, we conclude that the relaxation can be well described by means of a stretched exponential. In this way the characteristic relaxation times are obtained.
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Li, Peizhuo, Kfir Aberman, Zihan Zhang, Rana Hanocka, and Olga Sorkine-Hornung. "GANimator." ACM Transactions on Graphics 41, no. 4 (July 2022): 1–12. http://dx.doi.org/10.1145/3528223.3530157.
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We present GANimator, a generative model that learns to synthesize novel motions from a single, short motion sequence. GANimator generates motions that resemble the core elements of the original motion, while simultaneously synthesizing novel and diverse movements. Existing data-driven techniques for motion synthesis require a large motion dataset which contains the desired and specific skeletal structure. By contrast, GANimator only requires training on a single motion sequence, enabling novel motion synthesis for a variety of skeletal structures e.g. , bipeds, quadropeds, hexapeds, and more. Our framework contains a series of generative and adversarial neural networks, each responsible for generating motions in a specific frame rate. The framework progressively learns to synthesize motion from random noise, enabling hierarchical control over the generated motion content across varying levels of detail. We show a number of applications, including crowd simulation, key-frame editing, style transfer, and interactive control, which all learn from a single input sequence. Code and data for this paper are at https://peizhuoli.github.io/ganimator.
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Trościanko, Tom. "Contribution of Colour to the Motion Aftereffect and Motion Perception." Perception 23, no. 10 (October 1994): 1221–31. http://dx.doi.org/10.1068/p231221.
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The aim in this work was to assess the contribution which colour information makes to the perception of motion. Two dependent variables were measured: the reaction time to a sudden cessation of motion (motion-end RT) and the duration of the motion aftereffect (MAE). In each case, a baseline measure of performance was made with the aid of a monochrome stimulus with a given contrast and added luminance noise. This was compared with performance when red/green colour modulation was added to the luminance display. Any difference between these measures would reveal the extent of chromatic input. For motion-end RT the addition of colour had little effect under conditions where the stimulus had a strong luminance component and little added luminance noise. Increasing departures from these conditions revealed the contribution of a colour-sensitive mechanism. In general, the chromatic contribution to MAE duration was much smaller than was the equivalent contribution to motion-end RT, thus possibly indicating a neurological dissociation between the mechanisms subserving these effects. The results of an experiment in which the effect of different temporal frequencies of the added luminance noise was assessed supported this dissociation between MAE and motion-end RT. The findings are therefore consistent with there being two motion (sub)systems, which differ in the extent of chromatic input. The subsystem revealed by MAE measures is less affected by colour information.
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Simiu, E., and M. Grigoriu. "Non-Gaussian Noise Effects on Reliability of Multistable Systems." Journal of Offshore Mechanics and Arctic Engineering 117, no. 3 (August 1, 1995): 166–70. http://dx.doi.org/10.1115/1.2827085.
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For certain types of compliant structures, the designer must consider limit states associated with the onset of fluidelastic instability. These limit states may include bifurcations from motion in a safe region of phase space to chaotic motion with exits (jumps) out of the safe region. In practice, such bifurcations occur in systems with noisy or stochastic excitations. For a wide class of dynamical systems, a fundamental connection between deterministic and stochastic chaos allows the application to stochastic systems of a necessary condition for the occurrence of chaos originally obtained by Melnikov for the deterministic case. We discuss the application of this condition to obtain probabilities that chaotic motions with jumps cannot occur in multistable systems excited by processes with tail-limited marginal distributions.
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Zhang, Peng, Mingfeng Jiang, Yang Li, Ling Xia, Zhefeng Wang, Yongquan Wu, Yaming Wang, and Huaxiong Zhang. "An efficient ECG denoising method by fusing ECA-Net and CycleGAN." Mathematical Biosciences and Engineering 20, no. 7 (2023): 13415–33. http://dx.doi.org/10.3934/mbe.2023598.
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<abstract> <p>For wearable electrocardiogram (ECG) acquisition, it was easy to infer motion artifices and other noises. In this paper, a novel end-to-end ECG denoising method was proposed, which was implemented by fusing the Efficient Channel Attention (ECA-Net) and the cycle consistent generative adversarial network (CycleGAN) method. The proposed denoising model was optimized by using the ECA-Net method to highlight the key features and introducing a new loss function to further extract the global and local ECG features. The original ECG signal came from the MIT-BIH Arrhythmia Database. Additionally, the noise signals used in this method consist of a combination of Gaussian white noise and noises sourced from the MIT-BIH Noise Stress Test Database, including EM (Electrode Motion Artifact), BW (Baseline Wander) and MA (Muscle Artifact), as well as mixed noises composed of EM+BW, EM+MA, BW+MA and EM+BW+MA. Moreover, corrupted ECG signals were generated by adding different levels of single and mixed noises to clean ECG signals. The experimental results show that the proposed method has better denoising performance and generalization ability with higher signal-to-noise ratio improvement (SNR<sub>imp</sub>), as well as lower root-mean-square error (RMSE) and percentage-root-mean-square difference (PRD).</p> </abstract>
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Liu, Fengkai, Darong Huang, Xinrong Guo, and Cunqian Feng. "Noise-Robust ISAR Translational Motion Compensation via HLPT-GSCFT." Remote Sensing 14, no. 24 (December 7, 2022): 6201. http://dx.doi.org/10.3390/rs14246201.
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Translational motion compensation is a prerequisite of inverse synthetic aperture radar (ISAR) imaging. Translational motion compensation for datasets with low signal-to-noise ratio (SNR) is important but challenging. In this work, we proposed a noise-robust translational motion compensation method based on high-order local polynomial transform–generalized scaled Fourier transform (HLPT-GSCFT). We first model the translational motion as a fourth-order polynomial according to order-of-magnitude analysis, and then design HLPT-GSCFT for translation parameter estimation and parametric translational motion compensation. Specifically, HLPT is designed to estimate the acceleration and third-order acceleration of the translational motion and GSCFT is introduced to estimate the second-order acceleration. Both HLPT and GSCFT have a strong ability for cross-term suppression. In addition, we use a minimum weighted entropy algorithm to estimate the velocity of the translational motion, which can improve the noise robustness of the parameter estimation. Experimental results based on a measured dataset prove that the proposed method is effective and noise-robust.
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Zhai, Qiang. "Application of Visual Correction on Physical Training." Advanced Materials Research 989-994 (July 2014): 5461–63. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.5461.
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A method is presented for three-dimensional motion posture correction employing Fuzzy kernel estimation and affine transformation. As selecting human moving node by a visual sensor and smoothing single frame image with irregular motion, noise interference is reduced. Based on principles of perspective and affine transformation, adjustment strategy of three-dimensional posture is deduced for irregular single-frame motion picture. In addition to determination of motion picture rotation, accurate correction of irregular single-frame motion picture is proposed. Experimental results show that, under different noise conditions, the algorithm corrects posture of a three-dimensional moving image accurately and presents strong anti-noise performance.