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Journal articles on the topic 'Information velocity'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 February 2022

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1

Montes, Juan Domínguez. "Information Inaccessible at Superluminary Velocity." Physics Essays 6, no. 3 (September 1993): 389–94. http://dx.doi.org/10.4006/1.3029073.

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2

Evrard, G. "Minimal information in velocity space." Physics Letters A 201, no. 2-3 (May 1995): 95–102. http://dx.doi.org/10.1016/0375-9601(95)00249-3.

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3

Wang Hao, 王号, 张春光 Zhang Chunguang, and 李晖 Li Hui. "Information Speed in Controllable Light Velocity." Laser & Optoelectronics Progress 48, no. 3 (2011): 031902. http://dx.doi.org/10.3788/lop48.031902.

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4

LAI, Zhantao, Takayuki TANAKA, and Yuki SAMPEI. "3P2-E06 Velocity calibration by analog information presentation using vibration alert interface(Haptic Interface)." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2014 (2014): _3P2—E06_1—_3P2—E06_3. http://dx.doi.org/10.1299/jsmermd.2014._3p2-e06_1.

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5

Iyer, Srikanth, and Rahul Vaze. "Achieving nonzero information velocity in wireless networks." Annals of Applied Probability 27, no. 1 (February 2017): 48–64. http://dx.doi.org/10.1214/16-aap1196.

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6

Scheib, John P., and Sheng-Tz Lin. "Processing velocity information in an ultrasonic system." Journal of the Acoustical Society of America 102, no. 5 (1997): 2485. http://dx.doi.org/10.1121/1.420301.

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7

Brown, Robert A. "New Information from Radial Velocity Data Sets." Astrophysical Journal 610, no. 2 (August 2004): 1079–92. http://dx.doi.org/10.1086/421896.

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8

Iwasaki, Nobuhiro, and Keiichiro Yasuda. "Adaptive Particle Swarm Optimization via Velocity Information." Proceedings of OPTIS 2004.6 (2004): 73–78. http://dx.doi.org/10.1299/jsmeoptis.2004.6.73.

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9

Ravani, Reza, and Ali Meghdari. "Velocity Distribution Profile for Robot Arm Motion Using Rational Frenet–Serret Curves." Informatica 17, no. 1 (January 1, 2006): 69–84. http://dx.doi.org/10.15388/informatica.2006.124.

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10

Brodin, Lars-Åke, Jan van der Linden, and Björn Olstad. "Echocardiographic functional images based on tissue velocity information." Herz 23, no. 8 (December 1998): 491–98. http://dx.doi.org/10.1007/bf03043756.

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11

Madsen, B., M. Salewski, J. Huang, A. S. Jacobsen, O. Jones, and K. G. McClements. "Velocity-space tomography using prior information at MAST." Review of Scientific Instruments 89, no. 10 (October 2018): 10D125. http://dx.doi.org/10.1063/1.5035498.

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12

Shen, DongBin, ZhenDong Sun, and WeiJie Sun. "Leader-follower formation control without leader’s velocity information." Science China Information Sciences 57, no. 9 (July 18, 2014): 1–12. http://dx.doi.org/10.1007/s11432-013-4965-8.

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13

Vishram-Nielsen, Julie K. K., Stephane Laurent, Peter M. Nilsson, Allan Linneberg, Thomas S. G. Sehested, Sara V. Greve, Manan Pareek, et al. "Does Estimated Pulse Wave Velocity Add Prognostic Information?" Hypertension 75, no. 6 (June 2020): 1420–28. http://dx.doi.org/10.1161/hypertensionaha.119.14088.

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14

Saha, Tushar K., Joseph N. E. Lucero, Jannik Ehrich, David A. Sivak, and John Bechhoefer. "Maximizing power and velocity of an information engine." Proceedings of the National Academy of Sciences 118, no. 20 (May 10, 2021): e2023356118. http://dx.doi.org/10.1073/pnas.2023356118.

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Information-driven engines that rectify thermal fluctuations are a modern realization of the Maxwell-demon thought experiment. We introduce a simple design based on a heavy colloidal particle, held by an optical trap and immersed in water. Using a carefully designed feedback loop, our experimental realization of an “information ratchet” takes advantage of favorable “up” fluctuations to lift a weight against gravity, storing potential energy without doing external work. By optimizing the ratchet design for performance via a simple theory, we find that the rate of work storage and velocity of directed motion are limited only by the physical parameters of the engine: the size of the particle, stiffness of the ratchet spring, friction produced by the motion, and temperature of the surrounding medium. Notably, because performance saturates with increasing frequency of observations, the measurement process is not a limiting factor. The extracted power and velocity are at least an order of magnitude higher than in previously reported engines.
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15

Clapp, Robert G., Biondo L. Biondi, and Jon F. Claerbout. "Incorporating geologic information into reflection tomography." GEOPHYSICS 69, no. 2 (March 2004): 533–46. http://dx.doi.org/10.1190/1.1707073.

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In areas of complex geology, prestack depth migration is often necessary if we are to produce an accurate image of the subsurface. Prestack depth migration requires an accurate interval velocity model. With few exceptions, the subsurface velocities are not known beforehand and should be estimated. When the velocity structure is complex, with significant lateral variations, reflection‐tomography methods are often an effective tool for improving the velocity estimate. Unfortunately, reflection tomography often converges slowly, to a model that is geologically unreasonable, or it does not converge at all. The large null space of reflection‐tomography problems often forces us to add a sparse parameterization of the model and/or regularization criteria to the estimation. Standard tomography schemes tend to create isotropic features in velocity models that are inconsistent with geology. These isotropic features result, in large part, from using symmetric regularization operators or from choosing a poor model parameterization. If we replace the symmetric operators with nonstationary operators that tend to spread information along structural dips, the tomography will produce velocity models that are geologically more reasonable. In addition, by forming the operators in helical 1D space and performing polynomial division, we apply the inverse of these space‐varying anisotropic operators. The inverse operators can be used as a preconditioner to a standard tomography problem, thereby significantly improving the speed of convergence compared with the typical regularized inversion problem. Results from 2D synthetic and 2D field data are shown. In each case, the velocity obtained improves the focusing of the migrated image.
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16

Safran, Moshe N., Virginia L. Flanagin, Alexander Borst, and Haim Sompolinsky. "Adaptation and Information Transmission in Fly Motion Detection." Journal of Neurophysiology 98, no. 6 (December 2007): 3309–20. http://dx.doi.org/10.1152/jn.00440.2007.

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In this work, we studied the adaptation of H1, a motion-sensitive neuron in the fly visual system, to the variance of randomly fluctuating velocity stimuli. We ask two questions. 1) Which components of the motion detection system undergo genuine adaptational changes in response to the variance of the fluctuating velocity signal? 2) What are the consequences of this adaptation for the information processing capabilities of the neuron? To address these questions, we characterized the adaptation of H1 by estimating the changes in the parameters of an associated Reichardt motion detection model under various stimulus conditions. The strongest stimulus dependence was exhibited by the temporal kernel of the motion detector and was parametrized by changes in the model's high-pass time constant (τH). This time constant shortened considerably with increasing velocity fluctuations. We showed that this adaptive process contributes significantly to the shortening of the velocity response time-course but not to velocity gain control. To assess the contribution of time-constant adaptation to information transmission, we compared the information rates generated by our adaptive model motion detector with model simulations in which τH was held fixed at its unadapted value for all stimulus conditions. We found that for intermediate stimulus conditions, fixing τH at its unadapted value led to higher information rates, suggesting that time-constant adaptation does not optimize total information rates about velocity trajectories. We also found that, over the wide range of stimulus conditions tested here, H1 information rates are dependent on the amplitude of velocity fluctuations.
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17

Kornfeld, Richard P., R. John Hansman, John J. Deyst, Keith Amonlirdviman, and Elizabeth M. Walker. "Applications of Global Positioning System Velocity-Based Attitude Information." Journal of Guidance, Control, and Dynamics 24, no. 5 (September 2001): 998–1008. http://dx.doi.org/10.2514/2.4808.

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18

MUTO, Shin-yo, and Ken-ichiro SHIMOKURA. "Contact Point Detection Using Force and Velocity Information Complementarily." Journal of the Robotics Society of Japan 11, no. 3 (1993): 429–35. http://dx.doi.org/10.7210/jrsj.11.429.

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19

Yu, Zhengshi, and John L. Crassidis. "Accelerometer Bias Calibration Using Attitude and Angular Velocity Information." Journal of Guidance, Control, and Dynamics 39, no. 4 (April 2016): 741–53. http://dx.doi.org/10.2514/1.g001437.

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20

Fleet, David J., and Allan D. Jepson. "Computation of component image velocity from local phase information." International Journal of Computer Vision 5, no. 1 (August 1990): 77–104. http://dx.doi.org/10.1007/bf00056772.

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21

ZHU, X. Q., and S. S. LAW. "IDENTIFICATION OF MOVING INTERACTION FORCES WITH INCOMPLETE VELOCITY INFORMATION." Mechanical Systems and Signal Processing 17, no. 6 (November 2003): 1349–66. http://dx.doi.org/10.1006/mssp.2002.1577.

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22

Benson, Robert F., Adolfo F. Viñas, Vladimir A. Osherovich, Joseph Fainberg, Carola M. Purser, Mark L. Adrian, Ivan A. Galkin, and Bodo W. Reinisch. "Magnetospheric electron-velocity-distribution function information from wave observations." Journal of Geophysical Research: Space Physics 118, no. 8 (August 2013): 5039–49. http://dx.doi.org/10.1002/jgra.50459.

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23

Choi, K., S. J. Yoo, J. B. Park, and Y. H. Choi. "Adaptive formation control in absence of leader's velocity information." IET Control Theory & Applications 4, no. 4 (April 1, 2010): 521–28. http://dx.doi.org/10.1049/iet-cta.2009.0074.

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24

Zhang, Wenxue, Erick J. Rodríguez-Seda, Shankar A. Deka, Massinissa Amrouche, Di Zhou, Dušan M. Stipanović, and George Leitmann. "Avoidance Control with Relative Velocity Information for Lagrangian Dynamics." Journal of Intelligent & Robotic Systems 99, no. 2 (December 13, 2019): 229–44. http://dx.doi.org/10.1007/s10846-019-01122-x.

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25

de Lussanet, Marc H. E., Jeroen B. J. Smeets, and Eli Brenner. "The quantitative use of velocity information in fast interception." Experimental Brain Research 157, no. 2 (February 28, 2004): 181–96. http://dx.doi.org/10.1007/s00221-004-1832-2.

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26

Bellone, Jason, Segolene de Basquiat, and Juan Rodriguez. "Reaching escape velocity." Information Management & Computer Security 16, no. 1 (March 21, 2008): 49–57. http://dx.doi.org/10.1108/09685220810862742.

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27

Frakes, David, Mark Smith, Diane de Ze´licourt, Kerem Pekkan, and Ajit Yoganathan. "Three-Dimensional Velocity Field Reconstruction." Journal of Biomechanical Engineering 126, no. 6 (December 1, 2004): 727–35. http://dx.doi.org/10.1115/1.1824117.

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The problem of inter-slice magnetic resonance (MR) image reconstruction is encountered often in medical imaging applications. In such scenarios, there is a need to approximate information not captured in contiguously acquired MR images due to hardware sampling limitations. In the context of velocity field reconstruction, these data are required for visualization and computational analyses of flow fields to be effective. To provide more complete velocity information, a method has been developed for the reconstruction of flow fields based on adaptive control grid interpolation (ACGI). In this study, data for reconstruction were acquired via MRI from in vitro models of surgically corrected pediatric cardiac vasculatures. Reconstructed velocity fields showed strong qualitative agreement with those obtained via other acquisition techniques. Quantitatively, reconstruction was shown to produce data of comparable quality to accepted velocity data acquisition methods. Results indicate that ACGI-based velocity field reconstruction is capable of producing information suitable for a variety of applications demanding three-dimensional in vivo velocity data.
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28

Steiner, Brian, Erik H. Saenger, and Stefan M. Schmalholz. "Time-reverse imaging with limited S-wave velocity model information." GEOPHYSICS 76, no. 5 (September 2011): MA33—MA40. http://dx.doi.org/10.1190/geo2010-0303.1.

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Time-reverse imaging is a wave propagation algorithm for locating sources. Signals recorded by synchronized receivers are reversed in time and propagated back to the source location by elastic wavefield extrapolation. Elastic wavefield extrapolation requires a P-wave as well as an S-wave velocity model. The velocity models available from standard reflection seismic methods are usually restricted to only P-waves. In this study, we use synthetically produced time signals to investigate the accuracy of seismic source localization by means of time-reverse imaging with the correct P-wave and a perturbed S-wave velocity model. The studies reveal that perturbed S-wave velocity models strongly influence the intensity and position of the focus. Imaging the results with the individual maximum energy density for both body wave types instead of mixed modes allows individual analysis of the two body waves. P-wave energy density images render stable focuses in case of a correct P-wave and incorrect S-wave velocity model. Thus, P-wave energy density seems to be a more suitable imaging condition in case of a high degree of uncertainty in the S-wave velocity model.
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29

Ito, M. "Processing of vibrissa sensory information within the rat neocortex." Journal of Neurophysiology 54, no. 3 (September 1, 1985): 479–90. http://dx.doi.org/10.1152/jn.1985.54.3.479.

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Neuronal response properties were compared among different layers of the urethan-anesthetized rat vibrissa cortex. Measurements were made of the receptive-field (RF) size, the degree of directional selectivity, the latency of driving, the velocity threshold, and the tuning-curve slope. The RF size was defined by the number of whiskers that, when deflected individually, activated a neurons. For the center whisker of the RF (usually whisker C3), the response to deflection in the most preferred direction was compared with that in the opposite direction to classify the neuron as either strongly directional, weakly directional, or nondirectional. For the most preferred direction of the center whisker, the minimum velocity of deflection required to drive the unit was defined as the velocity threshold, the latency of driven response to a standard supramaximal velocity was measured, and finally, using exponential ramp-and-hold deflection, the threshold amplitude was determined at different values of time constant to construct a tuning-curve slope. Cortical layer IV neurons, as a whole, have the lowest threshold velocity. Layer Vb neurons stand on the opposite extreme in having the highest mean velocity threshold value. Although this difference is consistent with the generally held view that the "barrels" in layer IV represent the input stage of cortical information processing, the lack of laminar differences in latency and RF size support the idea that neurons of other cortical layers also receive direct thalamocortical inputs. The population of cortical neurons thus appears quite homogeneous across different layers as far as the results of examination with short-pulsed stimulation are concerned. Correlation of pairs of parameters (RF, directionality, velocity threshold, and latency) was tested in the two layers (layer IV and layer Vb). The latency and velocity threshold are highly correlated within both layers. Also, most of correlation coefficients of the corresponding pairs of the two layers are similar. However, the use of exponential ramp-and-hold deflection of whiskers revealed a difference in tuning-curve slope between layer IV and layer Vb (also layers II-III); layer IV neurons show flatter tuning-curve slopes (more oriented for detection of the amplitude component of whisker deflection) than neurons of layer Vb and layers II-III, which are more oriented for velocity detection. During the hold phase of whisker deflection, layer IV neurons tend to show sustained discharges, whereas layer Vb (also layers II-III) neurons mainly exhibit transient responses.(ABSTRACT TRUNCATED AT 400 WORDS)
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30

Du, Yu Hong, Xiu Ming Jiang, Gong Yuan Yang, and Zhen Hong Zhao. "Design of Flow Stability System Based on Information Fusion." Key Engineering Materials 426-427 (January 2010): 170–75. http://dx.doi.org/10.4028/www.scientific.net/kem.426-427.170.

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Stability of cotton flow velocity determines the accuracy of foreign fiber detection system. There are many factors affecting fiber flow flux. In the process of operational control, recent research has been directed towards synthetically and effectively adjusting the relative parameters, and thus achieving a stable and economic foreign fiber detection system in textile sector. In the foreign fiber detection system, the parameters of flow velocity are affected by the temperature, pressure, density, which are also interrelated and redundant information. Based on Clustering Fusion, the design of the flow velocity used in the detection device is provided in this paper. Using captured parameter characteristic information, clustering fusion control, conducts the second fusion for ART-2 and BP neural network, and sends to fusion center, then fusion center process the data using neural network method. Linking with synthesis repository and global data base, different control strategies can be utilized to adjust velocity of flow parameter. The cluster control strategy that keeps output velocity of flow stable are proposed to improve the fiber measure precision in foreign fiber detection system. This system can be used in indigenous foreign fiber detection system, and significantly improve the performance of the entire system.
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31

TATSUMI, Kimio, Nobuyoshi KOUGUCHI, Takashi KUBOTA, and Yasuo ARAI. "3-D Precise Velocity Information by GPS and Evaluation Test." Journal of Japan Institute of Navigation 119 (2008): 239–47. http://dx.doi.org/10.9749/jin.119.239.

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32

Liu, Zhen-Dong, Qing-Tian Lu, Shi-Xue Dong, and Ming-Chun Chen. "Research on velocity and acceleration geophones and their acquired information." Applied Geophysics 9, no. 2 (June 2012): 149–58. http://dx.doi.org/10.1007/s11770-012-0324-6.

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33

Zhang, Yan, Yang Liu, Xinsong Yang, and Jianlong Qiu. "Velocity Constraint on Double-Integrator Dynamics Subject to Antagonistic Information." IEEE Transactions on Circuits and Systems II: Express Briefs 68, no. 1 (January 2021): 411–15. http://dx.doi.org/10.1109/tcsii.2020.2999375.

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34

Molotkov, S. N. "On the supraluminal group velocity and the transmission of information." JETP Letters 91, no. 12 (June 2010): 693–99. http://dx.doi.org/10.1134/s0021364010120155.

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35

Rodriguez-Sanchez, A. J., J. K. Tsotsos, and J. C. Martinez-Trujillo. "Velocity gradient information influences optical flow processing in human observers." Journal of Vision 4, no. 8 (August 1, 2004): 606. http://dx.doi.org/10.1167/4.8.606.

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36

Tiwari, Ravi, and Siddharth Deshmukh. "Prior Information-Based Bayesian MMSE Estimation of Velocity in HetNets." IEEE Wireless Communications Letters 8, no. 1 (February 2019): 81–84. http://dx.doi.org/10.1109/lwc.2018.2857805.

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37

Fine, Justin M., Kimberly L. Ward, and Eric L. Amazeen. "Manual coordination with intermittent targets: Velocity information for prospective control." Acta Psychologica 149 (June 2014): 24–31. http://dx.doi.org/10.1016/j.actpsy.2014.02.012.

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38

Zhang, Jianxing, Qin Yang, Jigang Li, Xianhai Meng, and Xing Liang. "Representation of a velocity model with implicitly embedded interface information." Computers & Geosciences 82 (September 2015): 183–90. http://dx.doi.org/10.1016/j.cageo.2015.06.012.

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39

Yu, Kaikai, and Jinglei Xu. "Adaptive PIV algorithm based on seeding density and velocity information." Flow Measurement and Instrumentation 51 (October 2016): 21–29. http://dx.doi.org/10.1016/j.flowmeasinst.2016.08.004.

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40

Heimdahl, O. E. R., and J. C. Schulz. "A Note on the Obtainment of Instantaneous Penetration Information From Final Penetration Data." Journal of Applied Mechanics 53, no. 1 (March 1, 1986): 226. http://dx.doi.org/10.1115/1.3171719.

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A simple, practical technique is described for generating instantaneous penetration depth versus velocity information for a given projectile-target combination from experimentally obtained final penetration depth versus impact velocity data. The technique applies to penetrations where the resistive pressure is a function of velocity only.
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41

Nakamura, Shinji. "Effects of Background Motion on Eye-Movement Information." Perceptual and Motor Skills 84, no. 1 (February 1997): 107–13. http://dx.doi.org/10.2466/pms.1997.84.1.107.

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The effect of background stimulus on eye-movement information was investigated by analyzing the underestimation of the target velocity during pursuit eye movement (Aubert-Fleishl paradox). In the experiment, a striped pattern with various brightness contrasts and spatial frequencies was used as a background stimulus, which was moved at various velocities. Analysis showed that the perceived velocity of the pursuit target, which indicated the magnitudes of eye-movement information, decreased when the background stripes moved in the same direction as eye movement at higher velocities and increased when the background moved in the opposite direction. The results suggest that the eye-movement information varied as a linear function of the velocity of the motion of the background retinal image (optic flow). In addition, the effectiveness of optic flow on eye-movement information was determined by the attributes of the background stimulus such as the brightness contrast or the spatial frequency of the striped pattern.
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42

Zhang, Yu, and Daoliu Wang. "Traveltime information-based wave-equation inversion." GEOPHYSICS 74, no. 6 (November 2009): WCC27—WCC36. http://dx.doi.org/10.1190/1.3243073.

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We propose a new wave-equation inversion method that mainly depends on the traveltime information of the recorded seismic data. Unlike the conventional method, we first apply a [Formula: see text] transform to the seismic data to form the delayed-shot seismic record, back propagate the transformed data, and then invert the velocity model by maximizing the wavefield energy around the shooting time at the source locations. Data fitting is not enforced during the inversion, so the optimized velocity model is obtained by best focusing the source energy after a back propagation. Therefore, inversion accuracy depends only on the traveltime information embedded in the seismic data. This method may overcome some practical issues of waveform inversion; in particular, it relaxes the dependency of the seismic data amplitudes and the source wavelet.
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43

Collaris, R. J., and A. P. G. Hoeks. "Postprocessing of Velocity Distributions in Real-Time Ultrasonic Color Velocity Imaging." Ultrasonic Imaging 16, no. 4 (October 1994): 249–64. http://dx.doi.org/10.1177/016173469401600403.

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A robust processing scheme is proposed that improves the presentation of 2-dimensional velocity distributions in real-time ultrasonic color velocity images. Essentially, the algorithm is a modification of a first order recursive filter, processing each velocity signal in the spatial distribution separately from the others. It restores the sudden holes and notches in the velocity profiles that occur wherever the observed blood velocity is incidentally close to zero. At the same time, unlike conventional persistence filters, it does not influence any of the true velocity information that is measured. The result is a consistent sequence of color velocity images with smooth transitions between the borders of the consecutive velocity profiles and with an improved definition of the regions containing blood.
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44

Bakulin, Andrey, Marta Woodward, Dave Nichols, Konstantin Osypov, and Olga Zdraveva. "Localized anisotropic tomography with well information in VTI media." GEOPHYSICS 75, no. 5 (September 2010): D37—D45. http://dx.doi.org/10.1190/1.3481702.

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We develop a concept of localized seismic grid tomography constrained by well information and apply it to building vertically transversely isotropic (VTI) velocity models in depth. The goal is to use a highly automated migration velocity analysis to build anisotropic models that combine optimal image focusing with accurate depth positioning in one step. We localize tomography to a limited volume around the well and jointly invert the surface seismic and well data. Well information is propagated into the local volume by using the method of preconditioning, whereby model updates are shaped to follow geologic layers with spatial smoothing constraints. We analyze our concept with a synthetic data example of anisotropic tomography applied to a 1D VTI model. We demonstrate four cases of introducing additionalinformation. In the first case, vertical velocity is assumed to be known, and the tomography inverts only for Thomsen’s [Formula: see text] and [Formula: see text] profiles using surface seismic data alone. In the second case, tomography simultaneously inverts for all three VTI parameters, including vertical velocity, using a joint data set that consists of surface seismic data and vertical check-shot traveltimes. In the third and fourth cases, sparse depth markers and walkaway vertical seismic profiling (VSP) are used, respectively, to supplement the seismic data. For all four examples, tomography reliably recovers the anisotropic velocity field up to a vertical resolution comparable to that of the well data. Even though walkaway VSP has the additional dimension of angle or offset, it offers no further increase in this resolution limit. Anisotropic tomography with well constraints has multiple advantages over other approaches and deserves a place in the portfolio of model-building tools.
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45

Jeka, John, Tim Kiemel, Robert Creath, Fay Horak, and Robert Peterka. "Controlling Human Upright Posture: Velocity Information Is More Accurate Than Position or Acceleration." Journal of Neurophysiology 92, no. 4 (October 2004): 2368–79. http://dx.doi.org/10.1152/jn.00983.2003.

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The problem of how the nervous system fuses sensory information from multiple modalities for upright stance control remains largely unsolved. It is well established that the visual, vestibular, and somatosensory modalities provide position and rate (e.g., velocity, acceleration) information for estimation of body dynamics. However, it is unknown whether any particular property dominates when multisensory information is fused. Our recent stochastic analysis of postural sway during quiet stance suggested that sensory input provides more accurate information about the body's velocity than its position or acceleration. Here we tested this prediction by degrading major sources of velocity information through removal/attenuation of sensory information from vision and proprioception. Experimental measures of postural sway were compared with model predictions to determine whether sway behavior was indicative of a deficit in velocity information rather than position or acceleration information. Subjects stood with eyes closed on a support surface that was 1) fixed, 2) foam, or 3) sway-referenced. Six measures characterizing the stochastic structure of postural sway behaved in a manner consistent with model predictions of degraded velocity information. Results were inconsistent with the effect of degrading only position or acceleration information. These findings support the hypothesis that velocity information is the most accurate form of sensory information used to stabilize posture during quiet stance. Our results are consistent with the assumption that changes in sway behavior resulting from commonly used experimental manipulations (e.g., foam, sway-referencing, eyes closed) are primarily attributed to loss of accurate velocity information.
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46

Biondi, Biondo. "Velocity estimation by image-focusing analysis." GEOPHYSICS 75, no. 6 (November 2010): U49—U60. http://dx.doi.org/10.1190/1.3506505.

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Migration velocity can be estimated from seismic data by analyzing, focusing, and defocusing of residual-migrated images. The accuracy of these velocity estimates is limited by the inherent ambiguity between velocity and reflector curvature. However, velocity resolution improves when reflectors with different curvatures are present. Image focusing is measured by evaluating coherency across structural dips, in addition to coherency across aperture/azimuth angles. The inherent ambiguity between velocity and reflector curvature is directly tackled by introducing a curvature correction into the computation of the semblance functional that estimates image coherency. The resulting velocity estimator provides velocity estimates that are (1) unbiased by reflector curvature and (2) consistent with the velocity information that is routinely obtained by measuring coherency over aperture/azimuth angles. Applications to a 2D synthetic prestack data set and a 2D field prestack data set confirm that the proposed method provides consistent and unbiased velocity information. They also suggest that velocity estimates based on the new image-focusing semblance may be more robust and have higher resolution than estimates based on conventional semblance functionals. Applying the proposed method to zero-offset field data recorded in New York Harbor yields a velocity function that is consistent with available geologic information and clearly improves the focusing of the reflectors.
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47

Lu, Ying, Xiaojie Ji, and Yu Shu. "Method to Correct the Velocity Variation Information of an Automatic Crash Notification System in Vehicle-to-Rigid Barrier Frontal Collisions." Shock and Vibration 2021 (August 18, 2021): 1–18. http://dx.doi.org/10.1155/2021/5597886.

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Automatic crash notification systems (ACNSs) play a key role in post-accident safety. To improve the accuracy and efficiency of ACNSs, a method to correct the velocity variation information of ACNSs was established. First, after the acceleration data of sled crash tests were analysed, the factors affecting the accuracy of the velocity variation information were determined, and the influence of the discrimination threshold and acceleration curve shape on the velocity variation information was examined. Second, according to the acceleration data generated by the simulation model of a sled crash, the correlation between the accuracy of the velocity variation information and influencing factors was modelled. Third, an automatic crash notification algorithm involving a velocity variation correction function (VVCF) was proposed based on the correlation model. Finally, to verify its reliability, the improved algorithm was applied to an automatic crash notification system (ACNS) terminal. The validation results show that the ACNS terminal can accurately identify collisions and transmit accident information. Moreover, more accurate velocity variation information can be retrieved.
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48

Mirauda, Domenica, and Maria Grazia Russo. "Information Entropy Theory Applied to the Dip-Phenomenon Analysis in Open Channel Flows." Entropy 21, no. 6 (June 1, 2019): 554. http://dx.doi.org/10.3390/e21060554.

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The knowledge of the fluid discharge in free surface flows requires a great number of velocity measurements along the whole cross-section, taking up a large amount of time, using expensive equipment, and employing specialized labor. To overcome these obstacles, various models have been developed thus far that show how to estimate the mean velocity through the maximum velocity. In three-dimensional open channels, the maximum velocity can be located below the free surface because of the presence of secondary flows mainly originating by the sidewalls, an occurrence known as dip-phenomenon. In this condition, predicting the maximum velocity position is quite difficult and has always represented a challenge to most hydraulic engineers and researchers. In the present study, a mathematical model derived from the information entropy theory is proposed to evaluate the velocity-dip-position over the entire cross-section of both wide and narrow open channels, thus overcoming the limitations of the existing methods. Large literature measurement sets, collected in uniform and non-uniform flows, were used to test the validity of the model, showing good agreement with the experimental data and providing an accurate estimation of the dip-position.
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49

Lagarde, S., L. J. Sánchez, and R. G. Petrov. "Sub-resolution limit spatio-spectral information using Differential Speckle Interferometry." International Astronomical Union Colloquium 149 (1995): 360–64. http://dx.doi.org/10.1017/s0252921100023319.

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AbstractDifferential Speckle Interferometry (DSI) combines high angular resolution techniques with medium to high resolution spectroscopy. For non resolved sources, it yields the displacement of the object photocenter with wavelength. Combined with the spectrum s (λ), can give information with spatial and/or spectral resolution well beyond the telescope or spectrograph limits. This complementarity is illustrated here with experimental results. For the binary system Capella, we measure the angular separation, separate the spectra of the components and derive the radial velocity difference and the rotation velocity of each component. For the slowly rotating star Aldebaran we obtain the position angle of the stellar rotation axis and a relation between the angular diameter and the rotation velocity which in this case gives the latest.
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50

Niederer, Peter F. "Ultrasound imaging and Doppler flow velocity measurement." Technology and Health Care 18, no. 3 (July 8, 2010): 245–65. http://dx.doi.org/10.3233/thc-2010-0587.

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