scholarly journals TOWARDS WAVE DISTURBANCE IN PORTS COMPUTED BY A DETERMINISTIC CONVOLUTION-TYPE MODEL

2012 ◽  
Vol 1 (33) ◽  
pp. 7
Author(s):  
Hemming Andreas Schäffer
Keyword(s):  
Impulse Response ◽  
Response Function ◽  
Impulse Response Function ◽  
Boundary Integral ◽  
Wave Disturbance ◽  
Convolution Type ◽  
Wave Transformation ◽  
Type Model ◽  
Impulse Response Functions ◽  
Wide Range

Among the wide range of potential applications of the convolution-type approach to deterministic wave modeling, this paper looks into the challenge of complex shaped domains. The canonical case of diffraction around a semiinfinite vertical barrier, the ‘Sommerfeld diffraction’ case, is first studied. Focusing on locally constant water depth, the convolution method is related to a boundary integral representation by which the impulse response function representing the convolution kernel is related to a Green’s function for the Laplace equation. This provides a framework for determining the impulse response function by solving a local, three-dimensional Laplace problem prior to the time-stepping of the wave transformation problem. For the Sommerfeld case, numerical results for the impulse response function near the barrier are computed numerically and compared with an analytical solution. For complex-shaped domains, numerical determination of the impulse response functions is the only solution. A very preliminary example of application to wave disturbance in a real port is given.

IMPROVING EXTRACTION OF IMPULSE RESPONSE FUNCTIONS USING STATIONARY WAVELET SHRINKAGE IN TERAHERTZ REFLECTION IMAGING

2010 ◽  
Vol 09 (04) ◽  
pp. 387-394 ◽  
Author(s):  
YANG CHEN ◽  
YIWEN SUN ◽  
EMMA PICKWELL-MACPHERSON
Keyword(s):  
Experimental Data ◽  
Impulse Response ◽  
Response Function ◽  
Impulse Response Function ◽  
Terahertz Imaging ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Inverse Filtering ◽  
Wavelet Shrinkage ◽  
Gaussian Filtering

In terahertz imaging, deconvolution is often performed to extract the impulse response function of the sample of interest. The inverse filtering process amplifies the noise and in this paper we investigate how we can suppress the noise without over-smoothing and losing useful information. We propose a robust deconvolution process utilizing stationary wavelet shrinkage theory which shows significant improvement over other popular methods such as double Gaussian filtering. We demonstrate the success of our approach on experimental data of water and isopropanol.

Hilbert Transformation Impulse Response

2014 ◽  
Vol 19 (4) ◽  
pp. 27-35
Author(s):  
Mariusz Sulima
Keyword(s):  
Time Series ◽  
Impulse Response ◽  
Response Function ◽  
Signal To Noise Ratio ◽  
Impulse Response Function ◽  
Equation System ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Signal To Noise ◽  
Input Time

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.

ANALYSIS OF ALTERNATIVE METHODS FOR IMPULSE RESPONSE FUNCTIONS BASED ON SIGNAL-TO-NOISE RATIO ENHANCEMENT AND COMPLETENESS OF SOURCE SIGNAL RECONSTRUCTION USING PASSIVE TIME REVERSAL

2013 ◽  
Vol 21 (03) ◽  
pp. 1350008 ◽  
Author(s):  
YU-HAO HSIEH ◽  
GEE-PINN TOO
Keyword(s):  
Impulse Response ◽  
Response Function ◽  
Time Reversal ◽  
Signal To Noise Ratio ◽  
Impulse Response Function ◽  
Computation Time ◽  
Alternative Methods ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Source Signal

Noise reduction and signal separation are important functions of acoustic signal processing. This study presents a detailed analysis for designing an acoustic signal processing procedure based on the time-reversal method. For some applications, setting transducers to retransmit at source locations is impracticable. Modeling a wave propagation path between two points using impulse response function is one way to overcome this limitation. This paper introduces alternative methods to calculate impulse response function, including an adaptive digital filter, deconvolution with singular value decomposition and Tikhonov regularization, and correlation. A discussion is also provided on the applicable frequency range and anti-noise ability of the impulse response functions obtained by all three techniques through simulation, and subsequently applies them to the designed time reversal process to enhance the signal-to-noise ratio (SNR) and restore source signals through experimentation. The conclusions of this study are given based on the level of accuracy using the SNR and correlation coefficient as indicators, and the computation time required by alternative methods is also an important factor to be discussed for real-time system design. Results prove that the proposed passive time reversal process is capable of enhancing the SNR and restoring the source signal. The alternative methods of calculating the impulse response function offer various advantages, and should be selected according to the application. If the time-cost is the first consideration and there is no dominant noise source, then correlation is the best choice for calculating impulse response function. If completeness of the reconstructed signal is the key point, the optimal deconvolution process is appropriate. If noise reduction is the highest priority in extracting a useful signal from noisy environments while ensuring acceptable restoration capability and computation time, an adaptive digital filter is suitable.

Effect of subthreshold voltage-dependent conductances on the transfer function of branched excitable cells and the conduction of synaptic potentials

1988 ◽  
Vol 59 (3) ◽  
pp. 706-716 ◽  
Author(s):  
K. Yoshii ◽  
L. E. Moore ◽  
B. N. Christensen
Keyword(s):  
Steady State ◽  
Impulse Response ◽  
Response Function ◽  
Transfer Functions ◽  
Impulse Response Function ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Synaptic Current ◽  
Voltage Dependent ◽  
Negative Conductance

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)

Fractional Vector-Order h-Realisation of the Impulse Response Function

2020 ◽  
Vol 14 (2) ◽  
pp. 108-113
Author(s):  
Ewa Pawłuszewicz
Keyword(s):  
Control Systems ◽  
Impulse Response ◽  
Response Function ◽  
Impulse Response Function ◽  
Linear Control ◽  
Linear Control Systems

AbstractThe problem of realisation of linear control systems with the h–difference of Caputo-, Riemann–Liouville- and Grünwald–Letnikov-type fractional vector-order operators is studied. The problem of existing minimal realisation is discussed.

scholarly journals Convolution-based time-domain simulation for fluidelastic instability in tube arrays

2021 ◽  
Author(s):  
Mingjie Zhang ◽  
Ole Øiseth
Keyword(s):  
Impulse Response ◽  
Response Function ◽  
Time Domain ◽  
Impulse Response Function ◽  
Time Domain Simulation ◽  
Unit Step ◽  
Tube Arrays ◽  
Unit Impulse ◽  
The Time Domain ◽  
Unit Impulse Response

AbstractA convolution-based numerical algorithm is presented for the time-domain analysis of fluidelastic instability in tube arrays, emphasizing in detail some key numerical issues involved in the time-domain simulation. The unit-step and unit-impulse response functions, as two elementary building blocks for the time-domain analysis, are interpreted systematically. An amplitude-dependent unit-step or unit-impulse response function is introduced to capture the main features of the nonlinear fluidelastic (FE) forces. Connections of these elementary functions with conventional frequency-domain unsteady FE force coefficients are discussed to facilitate the identification of model parameters. Due to the lack of a reliable method to directly identify the unit-step or unit-impulse response function, the response function is indirectly identified based on the unsteady FE force coefficients. However, the transient feature captured by the indirectly identified response function may not be consistent with the physical fluid-memory effects. A recursive function is derived for FE force simulation to reduce the computational cost of the convolution operation. Numerical examples of two tube arrays, containing both a single flexible tube and multiple flexible tubes, are provided to validate the fidelity of the time-domain simulation. It is proven that the present time-domain simulation can achieve the same level of accuracy as the frequency-domain simulation based on the unsteady FE force coefficients. The convolution-based time-domain simulation can be used to more accurately evaluate the integrity of tube arrays by considering various nonlinear effects and non-uniform flow conditions. However, the indirectly identified unit-step or unit-impulse response function may fail to capture the underlying discontinuity in the stability curve due to the prespecified expression for fluid-memory effects.

Interest Rate–Economic Growth Nexus Under Currency Board Operations

2021 ◽  
Vol 8 (1) ◽  
pp. 13-24
Author(s):  
Martinianus Tshimologo Tibinyane ◽  
Teresia Kaulihowa
Keyword(s):  
Economic Growth ◽  
Interest Rate ◽  
Impulse Response ◽  
Response Function ◽  
Open Economy ◽  
Small Open Economy ◽  
Impulse Response Function ◽  
Policy Instrument ◽  
Variance Decomposition ◽  
Currency Board

This paper analyses the effect of the prime interest rate as a monetary policy instrument to stimulate economic growth in Namibia, a small open economy that is constrained by currency board operations. A Vector Autoregressive Model (VAR) was used for the period 1980–2019. The result shows that Namibia’s prime interest rate has no significant effect on economic growth. This finding remains robust and consistent when impulse response function and variance decomposition are employed. The impulse response function indicates a shock on the prime interest rate exhibits an inverse relationship. However, this effect is insignificant in both short and long-run scenarios. The variance decomposition indicates that the prime interest rate has a strongly exogenous impact, implying it has a weak influence on GDP growth. Policy implication indicates that small open economies under currency board operations need to identify different policy responses to circumvent external shocks and addresses their development needs.

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