System Theoretic Approach for the Impulse Response of Linear Time-Variant Systems

2018 ◽  
pp. 95-101
Author(s):  
Joachim Speidel
Keyword(s):  
Impulse Response ◽  
Linear Time ◽  
Theoretic Approach

scholarly journals Causality and Information Transfer Between the Solar Wind and the Magnetosphere–Ionosphere System

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 390
Author(s):  
Pouya Manshour ◽  
Georgios Balasis ◽  
Giuseppe Consolini ◽  
Constantinos Papadimitriou ◽  
Milan Paluš
Keyword(s):  
Solar Wind ◽  
Information Transfer ◽  
Linear Time ◽  
Vertical Component ◽  
Causal Link ◽  
Theoretic Approach ◽  
Geomagnetic Disturbances ◽  
Magnetospheric Substorms ◽  
Solar Wind Parameters ◽  
Information Theoretic Approach

An information-theoretic approach for detecting causality and information transfer is used to identify interactions of solar activity and interplanetary medium conditions with the Earth’s magnetosphere–ionosphere systems. A causal information transfer from the solar wind parameters to geomagnetic indices is detected. The vertical component of the interplanetary magnetic field (Bz) influences the auroral electrojet (AE) index with an information transfer delay of 10 min and the geomagnetic disturbances at mid-latitudes measured by the symmetric field in the H component (SYM-H) index with a delay of about 30 min. Using a properly conditioned causality measure, no causal link between AE and SYM-H, or between magnetospheric substorms and magnetic storms can be detected. The observed causal relations can be described as linear time-delayed information transfer.

Computing Impulse Response of Room Acoustics Using the Ray-Tracing Method in Time Domain

2010 ◽  
Vol 35 (4) ◽  
pp. 505-519 ◽  
Author(s):  
Adil Alpkocak ◽  
Malik Sis
Keyword(s):  
Impulse Response ◽  
Linear Time ◽  
Simulation Software ◽  
Room Acoustics ◽  
New Approach ◽  
Linear Time Invariant ◽  
Linear Time Invariant System ◽  
Time Invariant ◽  
Time Invariant System

AbstractThis paper proposes a new approach for calculating the impulse response of room acoustics. Impulse response provides unique characterization of any discrete lineartime invariant (LTI) systems. We assume that the room is a linear time-invariant system and the impulse response is calculated simply by sending a Dirac Impulse into the system as input and getting the response from the output. Then, the output of the system is represented as a sum of time-shifted weighted impulse responses. Both mathematical justifications for the proposed method and results from simulation software developed to evaluate the proposed approach are presented in detail.

Transient Motions of Floating Bodies at Zero Forward Speed

1987 ◽  
Vol 31 (03) ◽  
pp. 164-176 ◽  
Author(s):  
Robert F. Beck ◽  
Stergios Liapis
Keyword(s):  
Integral Equations ◽  
Impulse Response ◽  
Response Function ◽  
Time Domain ◽  
Linear Time ◽  
Impulse Response Function ◽  
Time Domain Analysis ◽  
The Body ◽  
Floating Body ◽  
Forward Speed

Linear, time-domain analysis is used to solve the radiation problem for the forced motion of a floating body at zero forward speed. The velocity potential due to an impulsive velocity (a step change in displacement) is obtained by the solution of a pair of integral equations. The integral equations are solved numerically for bodies of arbitrary shape using discrete segments on the body surface. One of the equations must be solved by time stepping, but the kernel matrix is identical at each step and need only be inverted once. The Fourier transform of the impulse-response function gives the more conventional added-mass and damping in the frequency domain. The results for arbitrary motions may be found as a convolution of the impulse response function and the time derivatives of the motion. Comparisons are shown between the time-domain computations and published results for a sphere in heave, a sphere in sway, and a right circular cylinder in heave. Theoretical predictions and experimental results for the heave motion of a sphere released from an initial displacement are also given. In all cases the comparisons are excellent.

Discuss about Linear System Transfer Function and Optical Transfer Function

2013 ◽  
Vol 275-277 ◽  
pp. 756-760 ◽  
Author(s):  
Yuan Sheng Wang ◽  
Gui Ying Lu ◽  
Bo Li
Keyword(s):  
Transfer Function ◽  
Differential Equations ◽  
Impulse Response ◽  
Linear Time ◽  
Optical Transfer Function ◽  
Invariant System ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Input And Output ◽  
Optical Transfer

Transfer function (TF) for linear time invariant system and optical transfer function (OTF) for linear space shaft invariant system are compared and contrasted. TF is the unilateral Laplace transform of system’s one-dimensional unit impulse response, dimensions of the input and output may be same or different, TF can be used to describe a variety of filter or to express solution of linear differential equations accurately. But OTF is the Fourier transform of system’s two-dimensional impulse response, dimensions of the input and output must be same, OTF can be used only to describe a low-pass filter or to express solution of the linear univariate partial differential equations approximately. Their eigenfunctions are similar complex exponential functions. OTF has stricter requirements and application conditions than TF. It is helpful to the readers’ understanding of TF and OTF.

A Simple Iterative Procedure for the Identification of the Unknown Parameters of a Linear Time Varying Discrete System

1963 ◽  
Vol 85 (2) ◽  
pp. 227-235 ◽  
Author(s):  
Harold J. Kushner
Keyword(s):  
Impulse Response ◽  
Discrete System ◽  
Linear Time ◽  
Time Varying ◽  
Unknown Parameters ◽  
Random Drift ◽  
Process Dynamics ◽  
Linear Discrete System ◽  
Single Input ◽  
Time Variations

A new “steepest descent” approach to the “adaptive control system” problem of the determination of the process dynamics of a time varying system is analyzed in considerable detail. The unknown parameters are the parameters of the impulse response of a linear discrete system. The identification procedure is a first-order iterative process and is designed to operate with the natural inputs of the system to be identified. After each new (single) input, new estimates of all the unknown parameters are computed. The method is computationally simple and, in its analysis, the effects of additive noise in the observations (of both input and output), random drift with time, or neglected parameters of the impulse response are handled with relative ease and become transparent. Time variations are taken directly into account, thus eliminating the necessity of the assumption of stationarity over a period of time.

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