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Journal articles on the topic 'Impulse response functions'

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Published: 4 June 2021
Last updated: 11 March 2023

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1

Potter, Simon M. "Nonlinear impulse response functions." Journal of Economic Dynamics and Control 24, no. 10 (September 2000): 1425–46. http://dx.doi.org/10.1016/s0165-1889(99)00013-5.

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2

Montes‐Rojas, Gabriel. "Multivariate Quantile Impulse Response Functions." Journal of Time Series Analysis 40, no. 5 (April 21, 2019): 739–52. http://dx.doi.org/10.1111/jtsa.12452.

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3

Tubiello, Francesco N., and Michael Oppenheimer. "Impulse-response functions and anthropogenic CO2." Geophysical Research Letters 22, no. 4 (February 15, 1995): 413–16. http://dx.doi.org/10.1029/94gl03276.

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4

Breitung, Jörg, and Philip Hans Franses. "Impulse response functions for periodic integration." Economics Letters 55, no. 1 (August 1997): 35–40. http://dx.doi.org/10.1016/s0165-1765(97)00047-5.

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5

Wickens, Michael R., and Roberto Motto. "Estimating shocks and impulse response functions." Journal of Applied Econometrics 16, no. 3 (2001): 371–87. http://dx.doi.org/10.1002/jae.617.

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6

Herlufsen, Henrik, and Svend Gade. "Errors involved in computing impulse response functions via frequency response functions." Mechanical Systems and Signal Processing 6, no. 3 (May 1992): 193–206. http://dx.doi.org/10.1016/0888-3270(92)90023-c.

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7

Naka, Atsuyuki, and David Tufte. "Examining impulse response functions in cointegrated systems." Applied Economics 29, no. 12 (December 1997): 1593–603. http://dx.doi.org/10.1080/00036849700000035.

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8

Plagborg-M{ø}ller, Mikkel. "Bayesian inference on structural impulse response functions." Quantitative Economics 10, no. 1 (2019): 145–84. http://dx.doi.org/10.3982/qe926.

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9

Kozachenko, Yu V., and I. V. Rozora. "Cross-correlogram estimators of impulse response functions." Theory of Probability and Mathematical Statistics 93 (February 7, 2017): 79–91. http://dx.doi.org/10.1090/tpms/995.

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10

Jun Li, Hong Hao, and XINGYU FAN. "Structural Damage Identification with Extracted Impulse Response Functions and Optimal Sensor Locations." Electronic Journal of Structural Engineering 14, no. 1 (January 1, 2015): 123–32. http://dx.doi.org/10.56748/ejse.141961.

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This paper presents a structural damage identification approach based on the time domain impulse response functions, which are extracted from the measured dynamic responses with the input available. The theoretical sensitivity of the impulse response function with respect to the system stiffness parameters considering the damping model is derived. The first-order sensitivity based model updating technique is performed for the iterative model updating. The initial structural finite element model and acceleration measurements from the damaged structure are required. Local damage is identified as a reduction in the elemental stiffness factors. The impulse response function sensitivity based optimal sensor placement strategy is employed to investigate the best sensor locations for identification. Numerical studies on a beam model are conducted to validate the proposed approach for the extraction of time domain impulse response functions and subsequent damage identification. The simulated damage can be identified effectively and accurately.
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11

Sulima, Mariusz. "Hilbert Transformation Impulse Response." Image Processing & Communications 19, no. 4 (December 1, 2014): 27–35. http://dx.doi.org/10.1515/ipc-2015-0022.

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Abstract This work presents a new DHT impulse response function based on the proposed nonlinear equation system obtained as a result of combining the DHT and IDHT equation systems. In the case of input time series with selected characteristics, the DHT results obtained using this impulse response function are characterised by a higher accuracy compared to the DHT results obtained based on the convolution using other known DHT impulse response functions. The results are also characterised by a higher accuracy than the DHT results obtained using the popular indirect DHT method based on discrete Fourier transform (DFT). Analysis of these example time series with selected characteristics was performed based on the signal-to-noise ratio.
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12

Chang, Pao‐Li, and Shinichi Sakata. "Estimation of impulse response functions using long autoregression." Econometrics Journal 10, no. 2 (June 20, 2007): 453–69. http://dx.doi.org/10.1111/j.1368-423x.2007.00216.x.

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13

O’Keefe, John. "Counting reflections in measured acoustic impulse response functions." Journal of the Acoustical Society of America 128, no. 4 (October 2010): 2465. http://dx.doi.org/10.1121/1.3508829.

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14

Kilian, Lutz. "Small-sample Confidence Intervals for Impulse Response Functions." Review of Economics and Statistics 80, no. 2 (May 1998): 218–30. http://dx.doi.org/10.1162/003465398557465.

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15

Burr, David C., and M. Concetta Morrone. "Impulse-response functions for chromatic and achromatic stimuli." Journal of the Optical Society of America A 10, no. 8 (August 1, 1993): 1706. http://dx.doi.org/10.1364/josaa.10.001706.

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16

Frey, Douglas, Victor Coelho, and Rangaraj M. Rangayyan. "Acoustical Impulse Response Functions of Music Performance Halls." Synthesis Lectures on Speech and Audio Processing 9, no. 2 (April 15, 2013): 1–110. http://dx.doi.org/10.2200/s00488ed1v01y201303sap012.

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17

Jayasuriya, S., and M. A. Franchek. "A Class of Transfer Functions With Non-Negative Impulse Response." Journal of Dynamic Systems, Measurement, and Control 113, no. 2 (June 1, 1991): 313–15. http://dx.doi.org/10.1115/1.2896381.

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Presented in this note is a class of stable, minimum phase transfer functions whose impulse response is non-negative. A simple sufficiency criterion based on the relative locations of the poles and zeros characterizes the class. When the transfer function is in a factored form the sign of its impulse response may either be obtained by inspection or is inconclusive. A need for identifying such transfer functions was recently established by Jayasuriya (1989) who showed that a controller designed on the basis of maximizing a step input disturbance will reject a persistent disturbance bounded by the size of the maximized step if and only if the closed-loop system’s impulse response is of one sign.
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18

Qu, Chun-Xu, Ting-Hua Yi, and Hong-Nan Li. "Modal identification for superstructure using virtual impulse response." Advances in Structural Engineering 22, no. 16 (July 12, 2019): 3503–11. http://dx.doi.org/10.1177/1369433219862951.

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In civil engineering, structural modes are identified with the assumption of stationary white noise, which cannot be satisfied in practical engineering. This article proposes a new method, which contains the virtual impulse response and eigensystem realization algorithm. The formulation of virtual impulse response is derived from the inverse Fourier transform of the ratio of the cross-power to auto-power spectral density functions of the measurement responses, which is based on the concept of frequency response function. During the formulation derivation, a single point excitation is only considered. Frequency response function would not change with different excitations and responses, which means that the excitation cannot influence frequency response function. The impulse response is pointed out to only represent the behavior of superstructure. After obtaining impulse responses, eigensystem realization algorithm is then performed to identify the modes of superstructure. The proposed method is validated by a numerical example. The results show that virtual impulse response can have much better free decayed behavior than natural excitation technique and identify very precise modal parameters for superstructure.
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19

Yoshii, K., L. E. Moore, and B. N. Christensen. "Effect of subthreshold voltage-dependent conductances on the transfer function of branched excitable cells and the conduction of synaptic potentials." Journal of Neurophysiology 59, no. 3 (March 1, 1988): 706–16. http://dx.doi.org/10.1152/jn.1988.59.3.706.

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1. Impulse response functions were determined from complex point impedance and transfer functions from cultured NG-108 cells to simulate the propagation of a synaptic potential in response to the release of transmitter. In general, the flow of synaptic current has a much shorter duration than the normal membrane time constant, thereby making the use of impulse response functions useful approximations to synaptic events. 2. The resonance observed during the activation of the potassium conductance was reflected in the impulse response function as a pronounced damped oscillation. A comparison of the impulse response functions calculated from point impedance and transfer functions showed similar results for current injections in the growth cone. 3. In addition to the resonance effects of the voltage-dependent conductances on transfer and impulse response functions due principally to the activation of conductances for outward currents, transfer functions were measured during the activation of a steady-state negative conductance. Under these conditions the phase function approaches 180 degrees, indicating that the voltage response is out of phase with the current. 4. In the steady state, the effect of a negative conductance is to algebraically add to the positive conductances and generally decrease the absolute conductance unless there is a net negative current. The decreased conductance enhances the impulse response and the DC space constant, thus leading to a better propagation of slow potentials. This effect can be seen as a decrease in the electrotonic length, L, with intermediate depolarizations. At large depolarizations the steady-state activation of the K conductance generally dominates and leads to a greatly increased electrotonic length. 5. Both the net conductances and the associated kinetics play a role in shaping the potential changes during a synaptic current. This is especially critical if there is a net negative steady-state conductance. Under these conditions there is a surprising reduction in the impulse response function. 6. Thus, during a subthreshold activation of the voltage-dependent negative conductances, the observable synaptic potentials would be either large potential responses due to an apparent increase in the impedance (algebraic summation of positive and negative conductances with a net positive conductance) or a minimal response because of the phasic cancellation due to a net negative conductance. The latter condition could exist near the synaptic reversal potential due to a large synaptic drive and would appear experimentally as a form of inhibition.(ABSTRACT TRUNCATED AT 400 WORDS)
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20

Chen, Kui Fu, and Yan Feng Li. "On the Integration Schemes of Retrieving Impulse Response Functions from Transfer Functions." Mathematical Problems in Engineering 2010 (2010): 1–9. http://dx.doi.org/10.1155/2010/143582.

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The numerical inverse Laplace transformation (NILM) makes use of numerical integration. Generally, a high-order scheme of numerical integration renders high accuracy. However, surprisingly, this is not true for the NILM to the transfer function. Numerical examples show that the performance of higher-order schemes is no better than that of the trapezoidal scheme. In particular, the solutions from high-order scheme deviate from the exact one markedly over the rear portion of the period of interest. The underlying essence is examined. The deviation can be reduced by decreasing the frequency-sampling interval.
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21

Yoo, Chul-Sang, Ha-Young Kim, and Joo-Young Park. "Analysis of Runoff Characteristics Using Multiple Impulse Response Functions." Journal of Korea Water Resources Association 43, no. 6 (June 30, 2010): 571–81. http://dx.doi.org/10.3741/jkwra.2010.43.6.571.

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22

Zele, A. J., D. Cao, and J. Pokorny. "Dark-adapted rods alter cone temporal impulse response functions." Journal of Vision 6, no. 13 (March 28, 2010): 68. http://dx.doi.org/10.1167/6.13.68.

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23

LEE, A. C. "Some restrictions of the non-causal impulse response functions." International Journal of Systems Science 20, no. 8 (August 1989): 1403–10. http://dx.doi.org/10.1080/00207728908910225.

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24

Blazhievska, Irina, and Vladimir Zaiats. "Estimation of impulse response functions in two-output systems." Communications in Statistics - Theory and Methods 49, no. 2 (November 16, 2018): 257–80. http://dx.doi.org/10.1080/03610926.2018.1536210.

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25

Inoue, Atsushi, and Lutz Kilian. "Inference on impulse response functions in structural VAR models." Journal of Econometrics 177, no. 1 (November 2013): 1–13. http://dx.doi.org/10.1016/j.jeconom.2013.02.009.

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26

Vega-Bermudez, F., and K. O. Johnson. "SA1 and RA Receptive Fields, Response Variability, and Population Responses Mapped with a Probe Array." Journal of Neurophysiology 81, no. 6 (June 1, 1999): 2701–10. http://dx.doi.org/10.1152/jn.1999.81.6.2701.

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SA1 and RA receptive fields, response variability, and population responses mapped with a probe array. Twenty-four slowly adapting type 1 (SA1) and 26 rapidly adapting (RA) cutaneous mechanoreceptive afferents in the rhesus monkey were studied with an array of independently controlled, punctate probes that covered an entire fingerpad. Each afferent had a receptive field (RF) on a single fingerpad and was studied at 73 skin sites (50 mm2). The entire array was lowered to 1.6 mm below the point of initial skin contact (the background indentation) before delivering single-probe indentations. SA1 and RA responses differed in several ways. 1) SA1 RF boundaries were affected much less by indentation depth than were RA boundaries, and the SA1 RF areas were much more uniform in size. The mean SA1 RF area grew from 5.1 to 8.8 mm2 as the indentation depth increased from 50 to 500 μm; the mean RA RF area grew from 5.5 to 22.4 mm2 over the same intensity range. 2) SA1 RFs were more elongated than RA RFs. Elongated RFs were oriented in all directions relative to the skin ridges and the finger axis. 3) SA1 impulse rates were linear functions of indentation depth at all probe locations in the RF; RA responses tended toward saturation beginning at 100 μm indentation depth when the probe was over the HS. Similarities between SA1 and RA responses were that 1) both were extremely repeatable with SDs < 1 impulse per trial and 2) both had population responses (number of impulses) that were nearly linear functions of indentation depth. However, SA1s represented increasing indentation depth by increasing impulse rates in a small, relatively constant group of afferents, whereas the RAs represented increasing indentation depth predominantly by the recruitment of new afferents at a distance.
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27

van der Valk, Paul L. C., and Daniel J. Rixen. "An Impulse Based Substructuring method for coupling impulse response functions and finite element models." Computer Methods in Applied Mechanics and Engineering 275 (June 2014): 113–37. http://dx.doi.org/10.1016/j.cma.2014.03.003.

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28

Kirchner, James W. "Impulse Response Functions for Nonlinear, Nonstationary, and Heterogeneous Systems, Estimated by Deconvolution and Demixing of Noisy Time Series." Sensors 22, no. 9 (April 25, 2022): 3291. http://dx.doi.org/10.3390/s22093291.

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Impulse response functions (IRFs) are useful for characterizing systems’ dynamic behavior and gaining insight into their underlying processes, based on sensor data streams of their inputs and outputs. However, current IRF estimation methods typically require restrictive assumptions that are rarely met in practice, including that the underlying system is homogeneous, linear, and stationary, and that any noise is well behaved. Here, I present data-driven, model-independent, nonparametric IRF estimation methods that relax these assumptions, and thus expand the applicability of IRFs in real-world systems. These methods can accurately and efficiently deconvolve IRFs from signals that are substantially contaminated by autoregressive moving average (ARMA) noise or nonstationary ARIMA noise. They can also simultaneously deconvolve and demix the impulse responses of individual components of heterogeneous systems, based on their combined output (without needing to know the outputs of the individual components). This deconvolution–demixing approach can be extended to characterize nonstationary coupling between inputs and outputs, even if the system’s impulse response changes so rapidly that different impulse responses overlap one another. These techniques can also be extended to estimate IRFs for nonlinear systems in which different input intensities yield impulse responses with different shapes and amplitudes, which are then overprinted on one another in the output. I further show how one can efficiently quantify multiscale impulse responses using piecewise linear IRFs defined at unevenly spaced lags. All of these methods are implemented in an R script that can efficiently estimate IRFs over hundreds of lags, from noisy time series of thousands or even millions of time steps.
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29

Kirchner, James W. "Impulse Response Functions for Nonlinear, Nonstationary, and Heterogeneous Systems, Estimated by Deconvolution and Demixing of Noisy Time Series." Sensors 22, no. 9 (April 25, 2022): 3291. http://dx.doi.org/10.3390/s22093291.

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Impulse response functions (IRFs) are useful for characterizing systems’ dynamic behavior and gaining insight into their underlying processes, based on sensor data streams of their inputs and outputs. However, current IRF estimation methods typically require restrictive assumptions that are rarely met in practice, including that the underlying system is homogeneous, linear, and stationary, and that any noise is well behaved. Here, I present data-driven, model-independent, nonparametric IRF estimation methods that relax these assumptions, and thus expand the applicability of IRFs in real-world systems. These methods can accurately and efficiently deconvolve IRFs from signals that are substantially contaminated by autoregressive moving average (ARMA) noise or nonstationary ARIMA noise. They can also simultaneously deconvolve and demix the impulse responses of individual components of heterogeneous systems, based on their combined output (without needing to know the outputs of the individual components). This deconvolution–demixing approach can be extended to characterize nonstationary coupling between inputs and outputs, even if the system’s impulse response changes so rapidly that different impulse responses overlap one another. These techniques can also be extended to estimate IRFs for nonlinear systems in which different input intensities yield impulse responses with different shapes and amplitudes, which are then overprinted on one another in the output. I further show how one can efficiently quantify multiscale impulse responses using piecewise linear IRFs defined at unevenly spaced lags. All of these methods are implemented in an R script that can efficiently estimate IRFs over hundreds of lags, from noisy time series of thousands or even millions of time steps.
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30

Mehta, Avinash, Munish Verma, Vijay K. Lamba, Susheel Kumar, and Sandeep Kumar. "ANALYSIS OF MODIFIED COSH WINDOW FUNCTION AND PERFORMANCE EVALUATION OF THE FIR FILTER DESIGNED USING WINDOWING TECHNIQUES." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 3, no. 2 (October 30, 2012): 324–28. http://dx.doi.org/10.24297/ijct.v3i2c.2893.

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Filters are used in electronic circuits to remove the unwanted frequency components from desired signals. A digital filter basically provide high attenuation to the unwanted ones and offer very low or ideally zero attenuation to desired signal components when it’s impulse response is adjusted as per requirement. For ideal filters, the length of such an impulse response is infinite and also the filter will be non-causal and unrealizable. So, we need to truncate this infinite impulse response to make it finite. For this truncation, we use window functions. Using window functions, we obtain a finite impulse response or simply FIR filter. The shape of a window in time domain decides the characteristics of resultant filter in frequency domain. Several window functions are available in literature. For the present work we have choosen the three parameter Cosh window for truncation of infinite impulse response. It is also called as modified Cosh window because it has been obtained by inserting a third parameter in the basic 2-parameter Cosh window function. The main goal of this work is to study this modified Cosh window and design a digital low pass FIR filter using the same in MATLAB. First of all the properties of window function are described and frequeny domain responses of window function is obtained. Then FIR filter is analyzed using window design method and it’s characteristics have also been studied in frequency domain.
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31

Murray, Drew A., and Robert J. McGough. "Numerical spatial impulse response calculations for a circular piston radiating in a lossy medium." Journal of the Acoustical Society of America 151, no. 5 (May 2022): 3104–15. http://dx.doi.org/10.1121/10.0009351.

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Exact analytical expressions for the spatial impulse response are available for certain transducer geometries. These exact expressions for the spatial impulse response, which are only available for lossless media, analytically evaluate the Rayleigh integral to describe the effect of diffraction in the time domain. To extend the concept of the spatial impulse response by including the effect of power law attenuation in a lossy medium, time-domain Green's functions for the Power Law Wave Equation, which are expressed in terms of stable probability density functions, are computed numerically and superposed. Numerical validations demonstrate that the lossy spatial impulse for a circular piston converges to the analytical lossless spatial impulse response as the value of the attenuation constant grows small. The lossy spatial impulse response is then evaluated in different spatial locations for four specific values of the power law exponent using several different values for the attenuation constant. As the attenuation constant or the distance from the source increases, the amplitude decreases while an increase in temporal broadening is observed. The sharp edges that appear in the time-limited lossless impulse response are replaced by increasingly smooth curves in the lossy impulse response, which decays slowly as a function of time.
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32

Cromwell, Jeff B., and Michael J. Hannan. "The Utility of Impulse Response Functions in Regional Analysis: Some Critical Issues." International Regional Science Review 15, no. 2 (August 1993): 199–222. http://dx.doi.org/10.1177/016001769301500204.

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Regional scientists have long been interested in measuring the effects of various external and internal stimuli on a regional economy. Measuring the actual size and timing of exogenous and endogenous impacts has been of special interest, as numerical or estimation techniques allow regional actors (governments, business, and others) to make policy-type probability statements and actions in response to changes to these stimuli. Recently, the use of vector autoregressive (VAR) models and, consequently, impulse response functions has become increasingly popular. This paper will closely examine the VAR methodology and its assumptions and will address the types of empirical issues that arise from actual regional implementation. The issues of stationarity, model specification and selection, order determination, and impulse responses are discussed.
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33

Fursov, V. A. "Constructing a quadratic-exponential FIR-filter with an extended frequency response midrange." Computer Optics 42, no. 2 (July 24, 2018): 297–305. http://dx.doi.org/10.18287/2412-6179-2018-42-2-297-305.

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This article is concerned with synthesizing filter with finite impulse response (FIR-filters) employed to correct radially symmetric distortions such as defocusing. We propose a new parametric class of finite impulse response filters (FIR-filters) based on a model of the one-dimensional radially symmetric frequency response. In the proposed method, the one-dimensional frequency response is composed of quadratic and exponential functions. The two-dimensional impulse response of the filter is constructed by sampling one-dimensional impulse responses for all directions. The development consists in introducing an extended mid-frequency region of the frequency response, thus increasing the contribution of the frequencies to image correction. Examples are given in order to illustrate the possibility of the high-quality distortion correction. In particular, it is shown that the proposed method provides the restoration quality higher than that obtained when using an optimal Wiener filter (taken from OpenCV).
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34

Bruder, Stefan, and Michael Wolf. "Balanced Bootstrap Joint Confidence Bands for Structural Impulse Response Functions." Journal of Time Series Analysis 39, no. 5 (May 9, 2018): 641–64. http://dx.doi.org/10.1111/jtsa.12289.

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35

Panopoulou, Ekaterini, and Theologos Pantelidis. "Integration at a cost: evidence from volatility impulse response functions." Applied Financial Economics 19, no. 11 (June 2009): 917–33. http://dx.doi.org/10.1080/09603100802112300.

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36

Mourjopoulos, J. "On the variation and invertibility of room impulse response functions." Journal of Sound and Vibration 102, no. 2 (September 1985): 217–28. http://dx.doi.org/10.1016/s0022-460x(85)80054-7.

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37

Hafner, C. M., and H. Herwartz. "Structural analysis of portfolio risk using beta impulse response functions." Statistica Neerlandica 52, no. 3 (November 1998): 336–55. http://dx.doi.org/10.1111/1467-9574.00088.

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38

van Nugteren-Osinga, I. C., M. Bos, and W. E. van der Linden. "Impulse/response functions of individual components of flow-injection manifolds." Analytica Chimica Acta 214 (1988): 77–86. http://dx.doi.org/10.1016/s0003-2670(00)80431-6.

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39

Draper, J. W., S. W. Lee, and E. C. Marineau. "Numerical construction of impulse response functions and input signal reconstruction." Journal of Sound and Vibration 432 (October 2018): 259–71. http://dx.doi.org/10.1016/j.jsv.2018.06.041.

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40

BURR, DAVID C., and CONCETTA MORRONE. "Temporal Impulse Response Functions for Luminance and Colour During Saccades." Vision Research 36, no. 14 (July 1996): 2069–78. http://dx.doi.org/10.1016/0042-6989(95)00282-0.

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41

Choi, Chi-Young, and Alexander Chudik. "Estimating impulse response functions when the shock series is observed." Economics Letters 180 (July 2019): 71–75. http://dx.doi.org/10.1016/j.econlet.2019.04.017.

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42

Lütkepohl, Helmut, and D. S. Poskitt. "Estimating Orthogonal Impulse Responses via Vector Autoregressive Models." Econometric Theory 7, no. 4 (December 1991): 487–96. http://dx.doi.org/10.1017/s0266466600004722.

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Impulse response functions from time series models are standard tools for analyzing the relationship between economic variables. The asymptotic distribution of orthogonalized impulse responses is derived under the assumption that finite order vector autoregressive (VAR) models are fitted to time series generated by possibly infinite order processes. The resulting asymptotic distributions of forecast error variance decompositions are also given.
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43

Rozora, I. V., and A. O. Melnyk. "Construction of goodness-of-fit criteria for the type of impulse response function." Science, technologies, innovation, no. 2(22) (2022): 52–60. http://dx.doi.org/10.35668/2520-6524-2022-2-07.

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The article is devoted to the study of the impulse response function, its estimation and properties, square-Gaussian random variables and processes, the rate of convergence of the unknown impulse response function, testing the hypothesis about the type of impulse response function, building a simulation model. The study showed that the pulse response function is the output signal of the system during signal processing, when the input signal is a short pulse. In a more general form, the impulse response function describes the response or output of the system as a function of time. Also, the impulse response function is considered a property of linear displacement systems. During the study of the estimation of the impulse response function on orthonormal and trigonometric bases, two conditions A, B and remarks to them were formed, which are used in the future to find different coefficients. The study of square-Gaussian random variables and processes has shown the benefits of using them in relation to the impulse response function. A theorem was also presented, which estimated the probability of a large deviation of the square-Gaussian process in the norm of a continuous function. To study the rate of convergence of the unknown impulse response function in the space of continuous functions and in the space L2, a lemma was formed, as well as a theorem that directly showed the rate of convergence of the impulse response function in the space of continuous functions. Zero and alternative hypotheses were formed. The null hypothesis claimed that the impulse response function existed, and the alternative hypothesis suggested the opposite. To test the hypothesis about the form of the impulse response function, a theorem was used by which a criterion was formed. Visual Studio Community 2022 integrated development environment (C ++ programming language) and Wolfram Mathematica computer algebra system for analytical transformations and numerical calculations were used to build the simulation model, which allowed to make mathematical calculations quite accurately.
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44

Rahman, Syed Mustafizur, M. Rezaul Islam, and Mumnunul Keramat. "Seismic Imaging by Impulse Response for Studying Crustal Structure of the Central Tibet." Journal of Scientific Research 1, no. 1 (December 17, 2008): 61–71. http://dx.doi.org/10.3329/jsr.v1i1.1082.

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Time-shifted source functions along with unwanted events produce the composite seismic trace. The time-shift varies according to the travel path of the source, which is reflected back from an interface. The arrival times of the reflected waves have been estimated as time-shifted sources in a technique with the application of Fourier transformation of composite seismic trace and source functions. Accurate time-shifts and amplitude coefficients are determined using the technique in the composite seismic traces. A very small time-shift has also been shown resolved. The technique is applied to INDEPTH seismic data and impulse responses are successfully obtained. Analyzed impulse response images have given the stratigraphic idea beneath the region of the central Tibet. High amplitude impressions in the implemented images have indicated the presence of a strong support that upholding the northern geology and high probability of further crustal change of the region. The technique impulse response is considered robust for the seismic reflection sequence analysis and can be used effectively for studying the subsurface geology.   Keywords: Seismic trace; Power spectrum; Impulse response; Reflection sequence; Subsurface geology.  © 2009 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved. DOI: 10.3329/jsr.v1i1.1082
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45

Khalaf, Lynda, and Beatriz Peraza López. "Simultaneous Indirect Inference, Impulse Responses and ARMA Models." Econometrics 8, no. 2 (April 2, 2020): 12. http://dx.doi.org/10.3390/econometrics8020012.

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A two-stage simulation-based framework is proposed to derive Identification Robust confidence sets by applying Indirect Inference, in the context of Autoregressive Moving Average (ARMA) processes for finite samples. Resulting objective functions are treated as test statistics, which are inverted rather than optimized, via the Monte Carlo test method. Simulation studies illustrate accurate size and good power. Projected impulse-response confidence bands are simultaneous by construction and exhibit robustness to parameter identification problems. The persistence of shocks on oil prices and returns is analyzed via impulse-response confidence bands. Our findings support the usefulness of impulse-responses as an empirically relevant transformation of the confidence set.
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46

Gunn, Roger N., Steve R. Gunn, and Vincent J. Cunningham. "Positron Emission Tomography Compartmental Models." Journal of Cerebral Blood Flow & Metabolism 21, no. 6 (June 2001): 635–52. http://dx.doi.org/10.1097/00004647-200106000-00002.

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The current article presents theory for compartmental models used in positron emission tomography (PET). Both plasma input models and reference tissue input models are considered. General theory is derived and the systems are characterized in terms of their impulse response functions. The theory shows that the macro parameters of the system may be determined simply from the coefficients of the impulse response functions. These results are discussed in the context of radioligand binding studies. It is shown that binding potential is simply related to the integral of the impulse response functions for all plasma and reference tissue input models currently used in PET. This article also introduces a general compartmental description for the behavior of the tracer in blood, which then allows for the blood volume-induced bias in reference tissue input models to be assessed.
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47

Nair, Veena J., Athri S. S, and Jishnu R. "SLEEP - THE MOST NEGLECTED IMPULSE OF PRESENT AGE." International Ayurvedic Medical Journal p4, no. 05 (July 30, 2020): 2433–36. http://dx.doi.org/10.46607/iamj11p4052020.

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A good night sleep is required for wellbeing of every organism. Humans though considered as an intelligent among living beings, seldom understand the importance of sleep impulse and ignore it. This ignorance has paved way for many health issues. Impulses are generated for maintenance of body and must be managed at proper time. Sleep impulse is essential for various brain functions, production of various hormone like growth hormone, cognitive power, good immune response etc. Today in highly competitive world human have forgotten his body needs. Earlier handful of persons suffered from sleep disturbance unlike today, where sleep disturbance is very common factor almost in every household. This article is an attempt to pull in the importance of sleep impulse and the ill effects if not attended on time.
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48

Yoshimura, K. "A novel type of mechanoreception by the flagella of Chlamydomonas." Journal of Experimental Biology 199, no. 2 (February 1, 1996): 295–302. http://dx.doi.org/10.1242/jeb.199.2.295.

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A novel type of mechanosensory mechanism is found in Chlamydomonas reinhardtii. When a cell is captured with a suction pipette and a negative pressure is applied, the cell produces repetitive Ca2+ impulses at a frequency of 0.5-1.0 Hz. The impulse frequency increases with the applied pressure. The impulses are produced when the flagella are sucked into the pipette but not when the cell body is sucked in leaving the flagella outside the pipette. Cells with short flagella produce impulses of small amplitude. Thus, the site where the cell senses mechanical stimuli and generates the impulse current must be localized at the flagella. The amplitude, shape and ion selectivity of the pressure-induced impulses are distinct from the all-or-none flagellar current that is evoked by photostimulation. The impulses are possibly produced by a combination of currents passing through mechanosensitive channels and Ca2+ channels. This response probably functions to modulate flagellar beating and thereby to regulate the behaviour of the cell.
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49

Hatemi-J, Abdulnasser, and Youssef El-Khatib. "The nexus of trade-weighted dollar rates and the oil prices: an asymmetric approach." Journal of Economic Studies 47, no. 7 (April 29, 2020): 1579–89. http://dx.doi.org/10.1108/jes-06-2019-0266.

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PurposeThis paper investigates the dynamic relationship between the trade-weighted dollar exchange rates and the oil prices in the world market. Monthly data during 1980–2017 are used for this purpose.Design/methodology/approachThe symmetric and asymmetric generalized impulse response functions are estimated for these important economic indicators.FindingsThe empirical findings show that if the dollar rate increases (i.e. the dollar depreciates), the oil price will increase. The reverse relationship is also supported empirically meaning that an increase in the oil price will results in a significant depreciation of the dollar rate. Based on the asymmetric impulses responses, it can also be claimed that the negative interaction is only significant for the positive changes and not for the negative ones. Thus, the underlying variables are negatively interrelated only for the positive shocks since a negative shock from any variable does not seem to have any significant impact on the other variable. These results have implications for cross hedging of price risk.Originality/valueTo the best knowledge, this is the first attempt to investigate the relationship between the dollar weighted exchange rate and the oil pieces via the asymmetric impulse response functions. Both of these variables and their interactions are very important for investors as well as policy makers worldwide.
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Sheefeni, Johannes, and Matthew Ocran. "Exchange rate pass-through to domestic prices in Namibia: SVAR evidence." Journal of Economic and Financial Sciences 7, no. 1 (April 30, 2014): 89–102. http://dx.doi.org/10.4102/jef.v7i1.132.

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This article investigates exchange rate pass-through to domestic prices in Namibia. The study covers the period of 1993:Q1 – 2011:Q4, and employed the impulse response functions and variance decompositions obtained from a structural vector autoregressive model. The results from the impulse response functions show that there is a high and long-lasting effect from changes in exchange rates to inflation in Namibia, or high exchange rate pass-through into domestic inflation. The results from the forecast error variance decompositions also reflect that changes in the price level evolve endogenously with changes in the exchange rate. The results are in agreement with the findings of the impulse response functions regarding the significant effect of the exchange rate variable on domestic prices (inflation). The results confirm an incomplete pass-through, indicating that the purchasing power parity theory does not hold, with regard to the price level, in the context of Namibia.
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