Modelling and computationally efficient time domain linear equalisation of nonlinear bandlimited QPSK satellite channels

1990 ◽  
Vol 137 (6) ◽  
pp. 438 ◽  
Author(s):  
K. Konstantinides ◽  
K. Yao
Keyword(s):  
Time Domain ◽  
Computationally Efficient ◽  
Satellite Channels

scholarly journals Time Domain Room Acoustic Solver with Fourth-Order Explicit FEM Using Modified Time Integration

2020 ◽  
Vol 10 (11) ◽  
pp. 3750 ◽  
Author(s):  
Takumi Yoshida ◽  
Takeshi Okuzono ◽  
Kimihiro Sakagami
Keyword(s):  
Conventional Method ◽  
Fourth Order ◽  
Time Domain ◽  
Time Integration ◽  
Sound Field ◽  
Time Discretization ◽  
Computationally Efficient ◽  
Order Accuracy ◽  
Practical Applications ◽  
Acoustic Methods

This paper presents a proposal of a time domain room acoustic solver using novel fourth-order accurate explicit time domain finite element method (TD-FEM), with demonstration of its applicability for practical room acoustic problems. Although time domain wave acoustic methods have been extremely attractive in recent years as room acoustic design tools, a computationally efficient solver is demanded to reduce their overly large computational costs for practical applications. Earlier, the authors proposed an efficient room acoustic solver using explicit TD-FEM having fourth-order accuracy in both space and time using low-order discretization techniques. Nevertheless, this conventional method only achieves fourth-order accuracy in time when using only square or cubic elements. That achievement markedly impairs the benefits of FEM with geometrical flexibility. As described herein, that difficulty is solved by construction of a specially designed time-integration method for time discretization. The proposed method can use irregularly shaped elements while maintaining fourth-order accuracy in time without additional computational complexity compared to the conventional method. The dispersion and dissipation characteristics of the proposed method are examined respectively both theoretically and numerically. Moreover, the practicality of the method for solving room acoustic problems at kilohertz frequencies is presented via two numerical examples of acoustic simulations in a rectangular sound field including complex sound diffusers and in a complexly shaped concert hall.

Simulation of Heave-Induced Pressure Oscillations in Herschel-Bulkley Muds

SPE Journal ◽  
2017 ◽  
Vol 22 (05) ◽  
pp. 1635-1653 ◽  
Author(s):  
Timm Strecker ◽  
Ole Morten Aamo ◽  
Henrik Manum
Keyword(s):  
Flow Rate ◽  
Time Domain ◽  
Nonlinear Function ◽  
Domain Model ◽  
Nonlinear Phenomena ◽  
Computationally Efficient ◽  
Pressure Oscillations ◽  
Realistic Geometries ◽  
Practical Implications ◽  
Mud Flow

Summary Heave induces pressure oscillations when drilling offshore from floating rigs. A time-domain model is proposed to analyze and predict such pressure oscillations. The model considers the coupled dynamics of the mud and the drillstring, Herschel-Bulkley-type rheology, and realistic geometries. A computationally efficient method to evaluate friction as a nonlinear function of the mud-flow rate and drillstring velocity is discussed. In a simulation study, we illustrate several nonlinear phenomena that have important practical implications but were not included in previous, simpler models. In particular, muds with a yield point can increase the pressure amplitudes significantly, and severe downhole-pressure oscillations are not detectable from topside measurements in many cases.

scholarly journals Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks

2021 ◽  
Author(s):  
Bojian Yin ◽  
Federico Corradi ◽  
Sander M. Bohté
Keyword(s):  
Neural Networks ◽  
Time Domain ◽  
Recurrent Neural Networks ◽  
State Of The Art ◽  
Spiking Neurons ◽  
Recurrent Networks ◽  
Computationally Efficient ◽  
Hardware Implementations ◽  
Comparable Performance ◽  
The Time Domain

ABSTRACTInspired by more detailed modeling of biological neurons, Spiking neural networks (SNNs) have been investigated both as more biologically plausible and potentially more powerful models of neural computation, and also with the aim of extracting biological neurons’ energy efficiency; the performance of such networks however has remained lacking compared to classical artificial neural networks (ANNs). Here, we demonstrate how a novel surrogate gradient combined with recurrent networks of tunable and adaptive spiking neurons yields state-of-the-art for SNNs on challenging benchmarks in the time-domain, like speech and gesture recognition. This also exceeds the performance of standard classical recurrent neural networks (RNNs) and approaches that of the best modern ANNs. As these SNNs exhibit sparse spiking, we show that they theoretically are one to three orders of magnitude more computationally efficient compared to RNNs with comparable performance. Together, this positions SNNs as an attractive solution for AI hardware implementations.

scholarly journals A Frequency-Time Algorithm of Parameter Estimation for Sinusoidal Signal in Noise

2021 ◽  
Vol 21 (1) ◽  
pp. 33-38
Author(s):  
Peng Chen ◽  
Qin Chen ◽  
Zhijun Xie ◽  
Xiaohui Chen ◽  
Shaomei Zhao
Keyword(s):  
Parameter Estimation ◽  
Time Domain ◽  
Initial Phase ◽  
Linear Prediction ◽  
Estimation Algorithm ◽  
Time Algorithm ◽  
Sinusoidal Signal ◽  
Estimation Accuracy ◽  
Computationally Efficient ◽  
Parameter Estimation Algorithm

Abstract In this paper, a computationally efficient and high precision parameter estimation algorithm with frequency-time combination is proposed to improve the estimation performance for sinusoidal signal in noise, which takes the advantages of frequency- and time-domain algorithms. The noise influence is suppressed through spectrum analysis to get coarse frequency, and the fine frequency is obtained by denoising filtering and using linear prediction property. Then, estimation values of the amplitude and initial phase are obtained. The numerical results indicate that the proposed algorithm makes up for the shortcomings of frequency- and time-domain algorithms and improves the anti-interference performance and parameter estimation accuracy for sinusoidal signal.

An efficient multiscale method for time-domain waveform tomography

Geophysics ◽  
2009 ◽  
Vol 74 (6) ◽  
pp. WCC59-WCC68 ◽  
Author(s):  
Chaiwoot Boonyasiriwat ◽  
Paul Valasek ◽  
Partha Routh ◽  
Weiping Cao ◽  
Gerard T. Schuster ◽  
...  
Keyword(s):  
Time Domain ◽  
Multiscale Method ◽  
Staggered Grid ◽  
Multiscale Approach ◽  
Efficient Computation ◽  
Computationally Efficient ◽  
Order Accuracy ◽  
Velocity Models ◽  
Waveform Tomography ◽  
The Time Domain

This efficient multiscale method for time-domain waveform tomography incorporates filters that are more efficient than Hamming-window filters. A strategy for choosing optimal frequency bands is proposed to achieve computational efficiency in the time domain. A staggered-grid, explicit finite-difference method with fourth-order accuracy in space and second-order accuracy in time is used for forward modeling and the adjoint calculation. The adjoint method is utilized in inverting for an efficient computation of the gradient directions. In the multiscale approach, multifrequency data and multiple grid sizes are used to overcome somewhat the severe local minima problem of waveform tomography. The method is applied successfully to 1D and 2D heterogeneous models; it can accurately recover low- and high-wavenumber components of the velocity models. The inversion result for the 2D model demonstrates that the multiscale method is computationally efficient and converges faster than a conventional, single-scale method.

Parametric Rolling Simulations of Container Ships

2013 ◽  
Author(s):  
Anton Turk ◽  
Jasna Prpić-Oršić ◽  
Carlos Guedes Soares
Keyword(s):  
Time Domain ◽  
Degrees Of Freedom ◽  
Equations Of Motion ◽  
Full Range ◽  
Ship Motions ◽  
Computationally Efficient ◽  
Parametric Roll ◽  
Parametric Rolling ◽  
Strip Theory ◽  
The Time Domain

A hybrid nonlinear time domain seakeeping analysis is applied to the study of a container ship advancing at different headings and encounter frequencies. A time-domain nonlinear strip theory in six degrees-of-freedom has been extended to predict ship motions by solving the unsteady hydrodynamic problem in the frequency domain and the equations of motion in the time domain which allows introducing nonlinearities in the linear model. The code is used to make parametric roll predictions for various speeds and headings and the results are summarized in a very intuitive 2D and 3D polar plots showing the full range of the parametric rolling realizations. The method developed is fairly accurate, robust, very computationally efficient, and can predict nonlinear ship motions. It is well suited to be used as a tool in ship design or as part of a path optimization model.

scholarly journals (Quasi-)Real-Time Inversion of Airborne Time-Domain Electromagnetic Data via Artificial Neural Network

2020 ◽  
Vol 12 (20) ◽  
pp. 3440
Author(s):  
Peng Bai ◽  
Giulio Vignoli ◽  
Andrea Viezzoli ◽  
Jouni Nevalainen ◽  
Giuseppina Vacca
Keyword(s):  
Neural Network ◽  
Time Domain ◽  
Mineral Exploration ◽  
Time Inversion ◽  
Computationally Efficient ◽  
Electromagnetic Data ◽  
Time Domain Data ◽  
Novel Approach ◽  
True Resistivity ◽  
Time Domain Electromagnetic

The possibility to have results very quickly after, or even during, the collection of electromagnetic data would be important, not only for quality check purposes, but also for adjusting the location of the proposed flight lines during an airborne time-domain acquisition. This kind of readiness could have a large impact in terms of optimization of the Value of Information of the measurements to be acquired. In addition, the importance of having fast tools for retrieving resistivity models from airborne time-domain data is demonstrated by the fact that Conductivity-Depth Imaging methodologies are still the standard in mineral exploration. In fact, they are extremely computationally efficient, and, at the same time, they preserve a very high lateral resolution. For these reasons, they are often preferred to inversion strategies even if the latter approaches are generally more accurate in terms of proper reconstruction of the depth of the targets and of reliable retrieval of true resistivity values of the subsurface. In this research, we discuss a novel approach, based on neural network techniques, capable of retrieving resistivity models with a quality comparable with the inversion strategy, but in a fraction of the time. We demonstrate the advantages of the proposed novel approach on synthetic and field datasets.

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