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Journal articles on the topic 'Frequency averaging'

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Author: Grafiati

Published: 10 March 2023

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1

Khitrin, A. K., Jiadi Xu, and Ayyalusamy Ramamoorthy. "Coherent averaging in the frequency domain." Journal of Chemical Physics 136, no. 21 (June 7, 2012): 214504. http://dx.doi.org/10.1063/1.4723682.

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2

Ide, Hideto, and Masafumi Uchida. "Analysis of Recognition Processes by Measurement of Brain Waves and Temperature Distributions." Journal of Robotics and Mechatronics 4, no. 1 (February 20, 1992): 63–69. http://dx.doi.org/10.20965/jrm.1992.p0063.

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In the past, in study of the evoked brain wave, the averaging was the most general method for analysis of signal components in brain wave. Specially, the averaging was applied for several evoked brain waves on the process of recognition to the stimulation, i.e. P300. However, any information for the averaging. The averaging is difficult to evaluate the evoked brain wave because of it's characteristics. In this study, it was the purpose to reveal the frequency range, the latency and the acyive parts of brain by following two method. 1) frequency and statistic analysis of evoked brain wave 2) measurement of temperature distribution on the surface of the head. The experiment for the measurement of the evoked brain wave was applied to fifteen men whose age were from 22 to 25. After frequency and statistic analysis, and waves were obtained at about 300ms latency. This result is as same as the averaging's result. From the temperature measurement, it was considered that the recognition to the stimulation was performed at the parietal lobe.

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3

Pinnau, René, and Alexander Schulze. "Radiation, Frequency Averaging and Proper Orthogonal Decomposition." PAMM 6, no. 1 (December 2006): 791–94. http://dx.doi.org/10.1002/pamm.200610376.

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4

IKAWA, NOBUKO. "AUTOMATED AVERAGING OF AUDITORY EVOKED RESPONSE WAVEFORMS USING WAVELET ANALYSIS." International Journal of Wavelets, Multiresolution and Information Processing 11, no. 04 (July 2013): 1360009. http://dx.doi.org/10.1142/s0219691313600096.

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The auditory brainstem response (ABR) is widely used as an index to assist hearing and brain function diagnoses. In particular, in clinical applications, the rapid detection of ABR peak characteristics is required. One approach to improving the speed of detection is to decrease the number of signal averaging procedures while denoising during the detection of ABR waveforms; another approach is to extract the characteristics of ABR waveform components. In our previous study, to represent ABR waveform components, we obtained not only the frequency characteristics of an ABR but also the frequency characteristics of each component of the ABR based on the time (latency). Using a one-dimensional discrete wavelet transform (DWT) in this latency-frequency analysis, we described an approximate method of reproducing ABR signals with a low SNR from observed values obtained with a smaller number of averaging procedures. At the same time, using this multiple-level frequency decomposition of ABR signals according to the known frequency content of the ABR, we extracted the peak latency of the fast component of the ABR using fewer averagings of the ABR data. From these decomposition and reconstruction results for ABR signals, we proposed the optimal decomposition level of the ABR and explained how we used the waveform of the ABR reconstructed by the inverse DWT (IDWT). In this paper, we propose a method of automated averaging of the ABR using the waveform reconstructed by discrete wavelet multiresolution analysis (MRA). Our proposed method will be useful for the fast detection of ABR latency characteristics in hearing screening test.

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5

Hansen, A. C., and T. E. Hausfeld. "Frequency-Response Matching to Optimize Wind-Turbine Test Data Correlation." Journal of Solar Energy Engineering 108, no. 3 (August 1, 1986): 246–51. http://dx.doi.org/10.1115/1.3268100.

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Pre-averaging is often applied to wind turbine test data to improve correlation between wind speed and power output data. In the past, trial and error or intuition have been used in the selection of pre-averaging time and researchers and institutions have differed widely in their pre-averaging practice. In this paper a standardized method is proposed for selection of the optimum pre-averaging time. The method selects an averaging time such that the test data are low-pass-filtered at the same frequency as the response frequency of the test wind turbine/anemometer system. A theoretial method is provided for estimation of the wind system transfer function as a function of the anemometer location, rotor moment of inertia, the stiffness of the connection between the rotor and the electrical grid, hub height, rotor speed and wind speed. The method is based in proven theory, repeatable, easy to use and applicable to a wide range of wind turbines and test conditions. Results of the transfer function predictions are compared with the measured response of two wind systems. Agreement between the predicted and measured response is completely adequate for the purposes of the method. Example results of calculated averaging times are presented for several wind turbines. In addition, a case study is used to demonstrate the dramatic effects of test design and data analysis methods on the results of a power coefficient measurement.

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6

Luque, Alejandro, and Jordi Villanueva. "Quasi-Periodic Frequency Analysis Using Averaging-Extrapolation Methods." SIAM Journal on Applied Dynamical Systems 13, no. 1 (January 2014): 1–46. http://dx.doi.org/10.1137/130920113.

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7

Weitenberg, Erik, Claudio De Persis, and Nima Monshizadeh. "Exponential convergence under distributed averaging integral frequency control." Automatica 98 (December 2018): 103–13. http://dx.doi.org/10.1016/j.automatica.2018.09.010.

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8

Sen, Surojit, Paul L. Evans, and C. Mark Johnson. "Multi‐frequency averaging (MFA) model of a generic electric vehicle powertrain suitable under variable frequency of averaging developed for remote operability." IET Electrical Systems in Transportation 10, no. 3 (September 2020): 268–74. http://dx.doi.org/10.1049/iet-est.2019.0043.

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9

Giannoulis, Johannes, Alexander Mielke, and Christof Sparber. "High-frequency averaging in semi-classical Hartree-type equations." Asymptotic Analysis 70, no. 1-2 (2010): 87–100. http://dx.doi.org/10.3233/asy-2010-1007.

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10

Dolganov, M. V. "High-frequency sampling error of averaging digital phase meters." Measurement Techniques 30, no. 4 (April 1987): 379–82. http://dx.doi.org/10.1007/bf00864771.

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11

Koliada, N. A., V. S. Pivtsov, S. A. Kuznetsov, A. A. Filonov, S. A. Farnosov, I. M. Kolyada, D. Yu Primakov, A. S. Dychkov, D. S. Kharenko, and I. S. Zhdanov. "Er:fiber-based femtosecond frequency comb stabilized to an Yb⁺ single-ion optical frequency standard." Laser Physics Letters 19, no. 1 (December 2, 2021): 015102. http://dx.doi.org/10.1088/1612-202x/19/1/015102.

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Abstract An erbium fiber-based femtosecond optical frequency comb stabilized to an Yb+ single-ion optical frequency standard was created. For the first time, a combination of an extra-cavity acousto-optic frequency modulator with fiber outputs and an intracavity electro-optic phase modulator based on a KTP crystal were used to stabilize offset frequency and one of the optical components of the Er:fiber femtosecond comb. As a result a locking bandwidth of 30 kHz for the optical comb offset frequency has been obtained. It is shown that the relative instability introduced by the stabilization and measurement systems into the output radio frequencies (in addition to the instability of the reference optical signal) is no worse than 5 × 10−14 for averaging times of 1 s and 2 × 10−16 for averaging times of 400 s.

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12

Lecomte, Christophe. "A frequency averaging framework for the solution of complex dynamic systems." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 470, no. 2166 (June 8, 2014): 20130743. http://dx.doi.org/10.1098/rspa.2013.0743.

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A frequency averaging framework is proposed for the solution of complex linear dynamic systems. It is remarkable that, while the mid-frequency region is usually very challenging, a smooth transition from low- through mid- and high-frequency ranges is possible and all ranges can now be considered in a single framework. An interpretation of the frequency averaging in the time domain is presented and it is explained that the average may be evaluated very efficiently in terms of system solutions.

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13

Narimani, A., M. E. Golnaraghi, and G. Nakhaie Jazar. "Frequency Response of a Piecewise Linear Vibration Isolator." Journal of Vibration and Control 10, no. 12 (December 2004): 1775–94. http://dx.doi.org/10.1177/1077546304044795.

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Piecewise linear vibration isolators are designed to optimally balance the competing goals of motion control and isolation. The piecewise linear system represents a hard nonlinearity, which cannot be assumed small, and hence standard perturbation methods are unable to provide a complete analytical solution. To date there is no frequency response equation reported for piecewise linear isolator systems to include both dual damping and stiffness behavior. In this investigation an averaging method was adopted to explore the frequency response of a symmetric piecewise linear isolator at resonance. The result obtained by an averaging method is in agreement with numerical simulation and experimental measurements. Preliminary sensitivity analysis is conducted to find the effect of system parameters. It appears that the damping ratio plays a more dominant role than stiffness in piecewise linear vibration isolators.

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14

Mouzaoui, Lounès. "High-frequency averaging in the semi-classical singular Hartree equation." Asymptotic Analysis 84, no. 3-4 (2013): 229–45. http://dx.doi.org/10.3233/asy-131175.

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15

Guillaume, P., R. Pintelon, and J. Schoukens. "Nonparametric frequency response function estimators based on nonlinear averaging techniques." IEEE Transactions on Instrumentation and Measurement 41, no. 6 (1992): 739–46. http://dx.doi.org/10.1109/19.199393.

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16

Guillaume, P. "Frequency response measurements of multivariable systems using nonlinear averaging techniques." IEEE Transactions on Instrumentation and Measurement 47, no. 3 (June 1998): 796–800. http://dx.doi.org/10.1109/19.744353.

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17

Daywitt, W. C. "Determining adapter efficiency by envelope averaging swept frequency reflection data." IEEE Transactions on Microwave Theory and Techniques 38, no. 11 (1990): 1748–52. http://dx.doi.org/10.1109/22.60027.

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18

Xu, Xiaoqiang, Jing Lin, and Chang Yan. "Adaptive determination of fundamental frequency for direct time-domain averaging." Measurement 124 (August 2018): 351–58. http://dx.doi.org/10.1016/j.measurement.2018.04.027.

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19

Gillen, Caitlin, and Matthew Heath. "Target frequency influences antisaccade endpoint bias: Evidence for perceptual averaging." Vision Research 105 (December 2014): 151–58. http://dx.doi.org/10.1016/j.visres.2014.10.010.

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20

Akulenko, L. D. "Averaging in a quasilinear system with a strongly varying frequency." Journal of Applied Mathematics and Mechanics 51, no. 2 (January 1987): 189–96. http://dx.doi.org/10.1016/0021-8928(87)90063-3.

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21

Roy, Julien, Jean-Daniel Deschênes, Simon Potvin, and Jérôme Genest. "Continuous real-time correction and averaging for frequency comb interferometry." Optics Express 20, no. 20 (September 11, 2012): 21932. http://dx.doi.org/10.1364/oe.20.021932.

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22

Pinnau, René, and Alexander Schulze. "Model reduction techniques for frequency averaging in radiative heat transfer." Journal of Computational Physics 226, no. 1 (September 2007): 712–31. http://dx.doi.org/10.1016/j.jcp.2007.04.024.

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23

Monakov, A. A., J. Vivekanandan, A. S. Stjernman, and A. K. Nystrom. "Spatial and frequency averaging techniques for a polarimetric scatterometer system." IEEE Transactions on Geoscience and Remote Sensing 32, no. 1 (1994): 187–96. http://dx.doi.org/10.1109/36.285201.

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24

Gehlbach, Steve M., and F. Graham Sommer. "Frequency Diversity Speckle Processing." Ultrasonic Imaging 9, no. 2 (April 1987): 92–105. http://dx.doi.org/10.1177/016173468700900202.

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Ultrasonic waveform data from a tissue-mimicking phantom containing low contrast targets was digitized, stored and processed prior to creating and displaying ultrasonic images. Speckle reduction was performed by digital filtering of the waveform data with appropriately spaced and weighted digital filters, prior to both coherent and incoherent image averaging. The resultant images showed increased signal-to-noise ratios, consistent with theory. Better definition of low contrast target boundaries was noted in the processed, compared to unprocessed, images. Incoherent and coherent processing were investigated, and appeared to be equivalent.

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25

Piersol, Allan G. "Optimum Resolution Bandwidth for Spectral Analysis of Stationary Random Vibration Data." Shock and Vibration 1, no. 1 (1993): 33–43. http://dx.doi.org/10.1155/1993/625127.

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This article presents a methodology for selecting the frequency resolution bandwidth for the spectral analysis of stationary random vibration signals in an optimum manner. Specifically, the resolution bandwidth that will produce power spectral density estimates with a minimum mean square error is determined for any given measurement duration (averaging time), and methods of approximating the optimum bandwidth using practical spectral analysis procedures are detailed. The determination of the optimum resolution bandwidth requires an estimate for the damping ratio of the vibrating structure that produced the measured vibration signal and the analysis averaging time. It is shown that the optimum resolution bandwidth varies approximately with the 0.8 power of the damping ratio and the bandwidth center frequency, and the −0.2 power of the averaging time. Also, any resolution bandwidth within ±50% of the optimum bandwidth will produce power spectral density (PSD) estimates with an error that is no more than 25% above the minimum achievable error. If a damping ratio of about 5% for structural resonances is assumed, a constant percentage resolution bandwidth of 1/12 octave, but no less than 2.5 Hz, will provide a near optimum PSD analysis for an averaging time of 2 seconds over the frequency range from 20 to 2000 Hz. A simple scaling formula allows the determination of appropriate bandwidths for other damping ratios and averaging times.

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26

Stovas, Alexey, and Børge Arntsen. "Vertical propagation of low-frequency waves in finely layered media." GEOPHYSICS 71, no. 3 (May 2006): T87—T94. http://dx.doi.org/10.1190/1.2197488.

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Multiple scattering in finely layered sediments is important for interpreting stratigraphic data, matching well-log data with seismic data, and seismic modeling. Two methods have been used to treat this problem in seismic applications: the O’Doherty-Anstey approximation and Backus averaging. The O’Doherty-Anstey approximation describes the stratigraphic-filtering effects, while Backus averaging defines the elastic properties for an effective medium from the stack of the layers. It is very important to know when the layered medium can be considered as an effective medium. In this paper, we only investigate vertical propagation. Therefore, no anisotropy effect is taken into consideration. Using the matrix-propagator method, we derive equations for transmission and reflection responses from the stack of horizontal layers. From the transmission response, we compute the phase velocity and compare the zero-frequency limit with the effective-medium velocity from Backus averaging. We also investigate how the transition from time-average medium to effective medium depends on contrast; i.e., strength of the reflection-coefficient series. Using numerical examples, we show that a transition zone exists between the effective medium (low-frequency limit) and the time-average medium (high-frequency limit), and that the width of this zone depends on the strength of the reflection-coefficient series.

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27

Porazik, Peter, and Zhihong Lin. "Gyrokinetic Simulation of Magnetic Compressional Modes in General Geometry." Communications in Computational Physics 10, no. 4 (October 2011): 899–911. http://dx.doi.org/10.4208/cicp.241110.280111a.

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AbstractA method for gyrokinetic simulation of low frequency (lower than the cyclotron frequency) magnetic compressional modes in general geometry is presented. The gyrokinetic-Maxwell system of equations is expressed fully in terms of the compressional component of the magnetic perturbation, δB∥, with finite Larmor radius effects. This introduces a “gyro-surface” averaging of δB∥ in the gyrocenter equations of motion, and similarly in the perpendicular Ampere’s law, which takes the form of the perpendicular force balance equation. The resulting system can be numerically implemented by representing the gyro-surface averaging by a discrete sum in the configuration space. For the typical wavelength of interest (on the order of the gyroradius), the gyro-surface averaging can be reduced to averaging along an effective gyro-orbit. The phase space integration in the force balance equation can be approximated by summing over carefully chosen samples in the magnetic moment coordinate, allowing for an efficient numerical implementation.

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28

Zhang, Ruohan, and Dana H. Ballard. "Parallel Neural Multiprocessing with Gamma Frequency Latencies." Neural Computation 32, no. 9 (September 2020): 1635–63. http://dx.doi.org/10.1162/neco_a_01301.

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The Poisson variability in cortical neural responses has been typically modeled using spike averaging techniques, such as trial averaging and rate coding, since such methods can produce reliable correlates of behavior. However, mechanisms that rely on counting spikes could be slow and inefficient and thus might not be useful in the brain for computations at timescales in the 10 millisecond range. This issue has motivated a search for alternative spike codes that take advantage of spike timing and has resulted in many studies that use synchronized neural networks for communication. Here we focus on recent studies that suggest that the gamma frequency may provide a reference that allows local spike phase representations that could result in much faster information transmission. We have developed a unified model (gamma spike multiplexing) that takes advantage of a single cycle of a cell's somatic gamma frequency to modulate the generation of its action potentials. An important consequence of this coding mechanism is that it allows multiple independent neural processes to run in parallel, thereby greatly increasing the processing capability of the cortex. System-level simulations and preliminary analysis of mouse cortical cell data are presented as support for the proposed theoretical model.

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29

Yang, Min, Xianhui Li, Zenong Cai, Junjuan Zhao, Peng Zhang, and Yunan Liu. "Theoretical and numerical study of nonlinear acoustic absorbers for low frequency noise control." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 263, no. 1 (August 1, 2021): 5600–5604. http://dx.doi.org/10.3397/in-2021-3171.

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In this paper, the sound absorption characteristics of cubic nonlinear sound-absorbing structures are analyzed by theoretical and numerical methods. The slow flow equations of the system are derived by using complexification averaging method, and the nonlinear equations which describe the steady- state response are obtained. The resulting equations are verified by comparing the results which respectively obtained from complexification-averaging method and Runge-Kutta method. It is helpful to optimize the structural parameters and further improve the sound absorption performance to study the variation of the sound absorption performance of cubic nonlinear structure with its structural parameters.

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30

Lee, Seong-Hun, and Byung-Lok Cho. "Measurement and Analysis of Local Average Power According to Averaging Length Changes of 3, 6, 10, and 17 GHz in an Indoor Corridor Environment." International Journal of Antennas and Propagation 2023 (January 20, 2023): 1–7. http://dx.doi.org/10.1155/2023/1485543.

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This study measures and analyzes the local average power for line-of-sight (LOS) and non-line-of-sight (NLOS) paths according to the averaging length in an indoor corridor environment. The indoor corridor comprises multiple offices, laboratory spaces, and lecture rooms. We selected 3, 6, 10, and 17 GHz measurement frequency bands. The measurement system consists of a signal generator, a low-noise amplifier, transmission and receiving antenna, and spectrum analyzer. To obtain an accurate prediction model of propagation due to the multipath effect, we determined the measurement method based on the measurement interval and number of measurements according to changes in the averaging length. 2, 4, 6, 8, and 10 lambdas (λ) were selected for the number of measurements by frequency, and 1.5 cm was set as the measurement interval. We used the close-in (CI) path loss model for the analysis according to changes in the averaging length. The coefficient of determination (R-squared) was applied using a linear regression equation to verify the measurement accuracy. Based on parameter n of the CI path loss model, no large differences were observed in the averaging length at each measurement frequency. However, at 2λ, owing to the multipath effect, R-squared was approximately 0.4–0.7 for the LOS path and 0.6–0.8 for the NLOS path. At 10λ, R-squared was approximately 0.7–0.8 for the LOS path and 0.8–0.9 for the NLOS path. This indicated that as the number of measurements increased by increasing the averaging length, the accuracy of the measurement results improved. The study findings will help determine an optimal averaging length, thus ensuring reliable indoor propagation measurement and contributing to the ITU-R standard.

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31

Wang, Ping, Can Liu, and Lin Guo. "Modeling and Simulation of Full-Bridge Series Resonant Converter Based on Generalized State Space Averaging." Applied Mechanics and Materials 347-350 (August 2013): 1828–32. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.1828.

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The small signal modeling technique based on the generalized state space averaging method is applied to single phase full-bridge resonant DC/DC converter .According to the simulation, the frequency characteristic curve is obtained from MATLAB, this paper analyzes the influence of the duty ratio, input voltage to frequency characteristics, then builds a closed loop simulation circuit with PSIM software, the simulation results show that, the small signal model has good controllability and anti disturbance resistance, generalized state space averaging method is more accurate to series resonant converter modeling.

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32

Ermak, S. V., E. A. Sagitov, R. V. Smolin, and V. V. Semenov. "Short-scale atomic clock based on the quantum magnetometers system." International Journal of Modern Physics: Conference Series 41 (January 2016): 1660140. http://dx.doi.org/10.1142/s201019451660140x.

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The experimental results of dependence of Allan variance as averaging time for system of two quantum magnetometers with laser pumping of the alkaline atoms are presented. Also the role of different components of luminous frequency shift of a radio optical resonance in case of an optimum operation mode of quantum magnetometers in such system is noted. It is shown that the effect of compensation of luminous shift components allows to reduce Allan variance in times of averaging more, than hundreds of seconds in comparison with the quantum standard of frequency on 0-0 transition.

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33

Hopp, F. A., J. L. Seagard, and J. P. Kampine. "Comparison of four methods of averaging nerve activity." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 251, no. 4 (October 1, 1986): R700—R711. http://dx.doi.org/10.1152/ajpregu.1986.251.4.r700.

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Four methods of averaging nerve activity, moving time average (Analog), integration (Integrated), counting spikes (Spikes), and counting pulses from a voltage-to-frequency converter (VFC), were used to analyze artificial pulse trains and renal, carotid sinus, and vagal nerve activities. Results of the methods were compared using least-squares linear regression and correlation to determine the linearity of each method with respect to changes in frequency, amplitude, and width of pulse trains and the degree of agreement between methods. The methods that respond to total voltage (Analog, Integrated, and VFC) were linear with respect to input pulse train modulations and agreed closely with each other when averaging pulse trains, summating pulses, and nerve activity. Spikes were linear with respect to frequency modulation but not with respect to amplitude changes, pulse width changes, or pulse summations. In general, Spikes did not agree as well with Analog, Integrated, and VFC as these methods agreed with each other when averaging nerve activity. The degree of agreement was a function of the voltage threshold for Spikes and the level of nerve activity. Two methods of minimizing noise and obtaining a zero reference level for nerve activity were compared: setting a voltage threshold, such that noise was below and activity above threshold, was found to shift the base-line activity toward zero and compress phasic changes in activity; and recording the average noise level from a crushed nerve and subtracting it from averaged activity shifted the base-line activity toward zero with no change in the phasic component.

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34

Wang, X. H., and C. S. Poon. "Spectral vs. compartmental averaging of VA/Q distributions: confidence limits." Journal of Applied Physiology 70, no. 3 (March 1, 1991): 1290–99. http://dx.doi.org/10.1152/jappl.1991.70.3.1290.

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We have investigated the method of statistical averaging as a nonparametric approach to obtain a representative ventilation-perfusion (VA/Q) distribution that exemplifies the family of compatible solutions for multiple inert gas elimination data. The variability of the compatible solutions was examined by determining the standard deviation of the statistical average. For six inert gases, it can be predicted that a distribution with up to seven contiguous nonzero VA/Q compartments can be uniquely recovered, whereas the compatible family becomes more diverse, the broader the distribution. For a given compatible family consisting of multimodal distributions with various phase relationships, the average distribution was found to display an uncharacteristically unimodal shape as a result of modal smoothing. To avoid this possible artifact, an alternative approach was adopted in which statistical averaging was performed in the frequency domain. For both deterministic and empirical data, the energy spectra of all feasible VA/Q distributions displayed a well-defined low-frequency band that was invariant within the compatible family and with a bandwidth that approximated the predicted sampling cutoff frequency. The nonuniqueness of the result was ascribable to a variable high-frequency band that was due to an aliasing effect. For a wide range of clinical data, the representative distributions resulting from compartmental and spectral averaging were indistinguishable from each other and had little variability both in the VA/Q and frequency domains. For these cases, therefore, the resolving power of the recovery algorithm was not critical. Finally, an efficient method of finding the average distribution was proposed.

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35

Flekkøy, Eirik G., Endre Håland, and Knut Jørgen Måløy. "Comparison of the low-frequency variations of the vertical and horizontal components of the electric background field at the sea bottom." GEOPHYSICS 77, no. 6 (November 1, 2012): E391—E396. http://dx.doi.org/10.1190/geo2011-0145.1.

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Natural electric field variations are measured at the sea bottom over long periods of time by means of stationary, vertical, and horizontal galvanic antennas. We compare the power spectra of the vertical and horizontal field components and the extent to which they may be reduced by standard averaging techniques. Although the raw spectra of the vertical and horizontal components do not differ greatly, the difference in the spectra after averaging is significantly greater. Most significantly, in the frequency range between 0.0005 and 0.03 Hz, this averaging scheme suppresses the vertical electric field component more strongly than the horizontal component.

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36

KOHAMA, Yoshikazu, Mamoru EGAWA, and Yuuichi SHIRAKI. "Theoritical study of processing gain by averaging in the frequency analysis." Journal of the Marine Acoustics Society of Japan 21, no. 1 (1994): 35–43. http://dx.doi.org/10.3135/jmasj.21.35.

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37

So, Hing Cheung, and Hongqing Liu. "Improved Single-Tone Frequency Estimation by Averaging and Weighted Linear Prediction." ETRI Journal 33, no. 1 (February 7, 2011): 27–31. http://dx.doi.org/10.4218/etrij.11.0110.0138.

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38

MIYAZAKI, Shinichiro, Shoichiro YAMASAKI, and Ryuji KOHNO. "Single-Carrier Transmission Using Overlap Frequency Domain Equalizing and Coherent Averaging." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E94-A, no. 11 (2011): 2169–77. http://dx.doi.org/10.1587/transfun.e94.a.2169.

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39

Addison, P. S. "Running wavelet archetype: time–frequency ensemble averaging requiring no fiducial points." Electronics Letters 51, no. 15 (July 2015): 1153–55. http://dx.doi.org/10.1049/el.2015.0821.

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40

Lomax, Anthony, and Roel Snieder. "Estimation of finite-frequency waveforms through wavelength-dependent averaging of velocity." Geophysical Journal International 126, no. 2 (August 1996): 369–81. http://dx.doi.org/10.1111/j.1365-246x.1996.tb05297.x.

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41

Simpson-Porco, John W., Qobad Shafiee, Florian Dorfler, Juan C. Vasquez, Josep M. Guerrero, and Francesco Bullo. "Secondary Frequency and Voltage Control of Islanded Microgrids via Distributed Averaging." IEEE Transactions on Industrial Electronics 62, no. 11 (November 2015): 7025–38. http://dx.doi.org/10.1109/tie.2015.2436879.

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42

Tahmasian, Sevak, David W. Allen, and Craig A. Woolsey. "On averaging and input optimization of high-frequency mechanical control systems." Journal of Vibration and Control 24, no. 5 (July 6, 2016): 937–55. http://dx.doi.org/10.1177/1077546316655706.

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This paper presents the optimization of input amplitudes for mechanical control-affine systems with high-frequency, high-amplitude inputs. The problem consists of determining the input waveform shapes and the relative phases between inputs to minimize the input amplitudes while accomplishing some control objective. The effects of the input waveforms and relative phases on the dynamics are investigated using averaging. It is shown that of all zero-mean, periodic functions, square waves require the smallest amplitudes to accomplish a control objective. Using the averaging theorem the problem of input optimization is transformed into a constrained optimization problem. The constraints are algebraic nonlinear equalities in terms of the amplitudes of the inputs and their relative phases. The constrained optimization problem may be solved using analytical or numerical methods. A second approach uses finite Fourier series to solve the input optimization problem. This second approach confirms the earlier results concerning minimum amplitude inputs and is then applied to the problem of minimizing control energy.

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Schmid, Kurt, and Gerd Böhmer. "Instantaneous frequency meter with averaging capability based on interval duration measurement." Electroencephalography and Clinical Neurophysiology 60, no. 1 (January 1985): 90–92. http://dx.doi.org/10.1016/0013-4694(85)90955-1.

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Piatkowska, Jolanta M., Brian J. Prendergast, and Michael R. Gorman. "Temporal integration of melatonin infusion duration: signal averaging versus frequency dependence." Journal of Pineal Research 35, no. 2 (September 2003): 91–97. http://dx.doi.org/10.1034/j.1600-079x.2003.00060.x.

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Min, Dong‐Joo, Changsoo Shin, Byung‐Doo Kwon, and Seunghwan Chung. "Improved frequency‐domain elastic wave modeling using weighted‐averaging difference operators." GEOPHYSICS 65, no. 3 (May 2000): 884–95. http://dx.doi.org/10.1190/1.1444785.

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We develop a new finite‐difference scheme that reduces the number of grid points per wavelength required in frequency‐domain elastic modeling. Our approach computes weighted averages of the spatial second‐order derivative and the mass acceleration terms using a 25-point computational stencil. By determining the weighting coefficients to minimize numerical dispersion and numerical anisotropy, we reduce the number of grid points to 3.3 per shear wavelength, with a resulting error in velocities smaller than 1%. Our choice of grid points reduces the computer memory needed to store the complex impedance matrix to 4% of that for a conventional second‐order scheme and to 54% of that for a combined second‐order scheme. The 25-point weighted averaging scheme of this paper makes it possible to accurately simulate realistic models. Numerical examples show that this technique can achieve the same accurate solutions with fewer grid points than those from previous frequency‐domain second‐order schemes. Our technique can be extended directly to 3-D elastic modeling; the computational efficiency will be even greater than that realized for 2-D models.

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Degroat, Ronald D., Eric M. Dowling, and Darel A. Linebarger. "Bias correction and forward-backward averaging in frequency/DOA tracking problems." Journal of VLSI signal processing systems for signal, image and video technology 14, no. 1 (October 1996): 93–105. http://dx.doi.org/10.1007/bf00925271.

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Zakharchenko, Vladimir, Sergey Zakharchenko, and Andrey Yakimets. "Numeric Modeling of Low-Altitude Altimeter with Double Frequency Modulation." Mathematical Physics and Computer Simulation, no. 3 (December 2020): 5–11. http://dx.doi.org/10.15688/mpcm.jvolsu.2020.3.1.

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This study is concerned with the problem of increasing the accuracy of a low-altitude altimeter employing the frequency modulation principle. A way to suppress the "discrete error" of the altimeter by employing additional «slow» frequency modulation of the carrier wave and averaging the resulting counts is considered. The benefit of such approach is simplicity of technical implementation manifesting in minimal changes in the microwave path and the recording device, which needs to run in averaging count mode. It is shown that, given a limited frequency band, the linear modulation form is not optimal. Results of error calculations presented are obtained via mathematical modelling of the altimeter’s operation for different shapes of the additional prequency modulation. It is shown that using complex shapes of the «slow» modulation with positive third derivative and optimizing for a given altitude range allows to reduce the average measurement error 2–3 times additionally relatively to the linear modulation form without expanding the occupied frequency band.

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Barnes, Arthur E. "Weighted average seismic attributes." GEOPHYSICS 65, no. 1 (January 2000): 275–85. http://dx.doi.org/10.1190/1.1444718.

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Local weighted averaging of instantaneous seismic attributes improves their interpretability by removing spikes and reducing rapid and confusing variations. Averaging in a window weighted by the instantaneous power produces a local measure that equals a Fourier spectral average, facilitating quantitative analysis. Local 1-D frequency and bandwidth are scalars, but local 2-D and 3-D seismic attributes derive from average vector wavenumbers, which may require velocity information. The direction of the average vector wavenumber provides average dip and azimuth, and its magnitude provides a measure of average wavelength or frequency. A related measure of bandwidth is a scalar in all dimensions; it includes contributions from both instantaneous bandwidth and the variance of instantaneous frequency or wavenumber. It quantifies data similarity.

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Zhu, LiMin, Han Ding, and Xiang Yang Zhu. "Synchronous Averaging of Time-Frequency Distribution With Application to Machine Condition Monitoring." Journal of Vibration and Acoustics 129, no. 4 (March 23, 2007): 441–47. http://dx.doi.org/10.1115/1.2748466.

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A novel signal analysis technique capable of detecting periodic bursts of exponentially decaying oscillation from noisy discrete-time observations is presented. The time-frequency distribution of the signal is viewed as a series of time signals corresponding to different frequency variables, and each time signal is processed with the technique of synchronous average scanning over a frequency region of interest. The energies of the outputs corresponding to different frequency bands and different average reference frequencies are recorded in a 2D array and depicted as a gray level image. Detection of the signal of interest leads itself to the identification of the spot in the image. The period of the impulse train and the frequency of the oscillation can be further estimated from the location of the spot. Examples that diagnose machine faults under complicated conditions are given to confirm the validity of the approach.

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Stovas, A., Y. Roganov, K. Duffaut, and A. J. Carter. "Low-frequency layer-induced anisotropy." GEOPHYSICS 78, no. 5 (September 1, 2013): WC3—WC14. http://dx.doi.org/10.1190/geo2012-0301.1.

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It is often useful to compare velocities estimated from seismic experiments with those measured by well logs at much smaller scales and higher frequencies. In the presence of fine layering, seismic velocities and anisotropy are scale- and frequency-dependent and upscaling of well logs is necessary to allow comparison. When upscaling well-log data measured at sonic frequencies, we assumed a layered medium with layer thickness given by logging step. Standard upscaling gives the exact solution for effective properties of a layered medium assuming that all constituents of the medium are linearly elastic and there are no anelastic energy losses. This method is static, meaning that the solution is obtained in the zero frequency limit. We expand upscaling to low frequencies and propose to use a seismic wavelet estimate for weighted averaging of effective properties. By expanding the logarithm of the propagator matrix computed from the stack of horizontal transversely isotropic layers with a vertical symmetry axis (VTI) in series with frequency and keeping the first three terms in this series, we obtain the low-frequency extension of Backus upscaling. The effective medium properties computed for individual nonzero frequencies correspond to a nonphysical medium with a vertical symmetry axis. To preserve the VTI symmetry, the effective slowness surface obtained for each individual frequency is approximated by a VTI medium by fitting the coefficients of the Taylor series derived from corresponding eigenvalues of the effective system matrix and similar series obtained for the vertical slownesses of different wave modes. The frequency-dependent anisotropy parameters are obtained from Taylor series coefficients for individual wave modes. Afterwards, these coefficients are averaged with frequencies weighted by the spectrum of a seismic wavelet extracted at the corresponding depth interval. The proposed averaging technique is data-driven and takes into account the low-frequency behavior of seismic waves with near-vertical propagation.

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