scholarly journals A Modified AH-FDTD Unconditionally Stable Method Based on High-Order Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-7
Author(s):  
Zheng Pan ◽  
Zhikai Fu ◽  
Lihua Shi ◽  
Zhengyu Huang ◽  
Zheng Sun
Keyword(s):  
Signal Reconstruction ◽  
Fdtd Method ◽  
Coarse Grid ◽  
High Order ◽  
Stable Method ◽  
Unconditionally Stable ◽  
Dispersion Error ◽  
Time Frequency ◽  
Simulation Results ◽  
Order Algorithm

The unconditionally stable method, Associated-Hermite FDTD, has attracted more and more attentions in computational electromagnetic for its time-frequency compact property. Because of the fewer orders of AH basis needed in signal reconstruction, the computational efficiency can be improved further. In order to further improve the accuracy of the traditional AH-FDTD, a high-order algorithm is introduced. Using this method, the dispersion error induced by the space grid can be reduced, which makes it possible to set coarser grid. The simulation results show that, on the condition of coarse grid, the waveforms obtained from the proposed method are matched well with the analytic result, and the accuracy of the proposed method is higher than the traditional AH-FDTD. And the efficiency of the proposed method is higher than the traditional FDTD method in analysing 2D waveguide problems with fine-structure.

scholarly journals Time-frequency representations of high order harmonics

2001 ◽  
Vol 8 (10) ◽  
pp. 529 ◽  
Author(s):  
Mette Gaarde
Keyword(s):  
High Order ◽  
Time Frequency

The Costs of Ignoring High-Order Correlations in Populations of Model Neurons

2006 ◽  
Vol 18 (3) ◽  
pp. 660-682 ◽  
Author(s):  
Melchi M. Michel ◽  
Robert A. Jacobs
Keyword(s):  
Model Neuron ◽  
Joint Probability ◽  
High Order ◽  
Neural Population ◽  
Neural Responses ◽  
Significant Information ◽  
Statistical Correlations ◽  
Neural Populations ◽  
Simulation Results ◽  
Dependence Tree

Investigators debate the extent to which neural populations use pairwise and higher-order statistical dependencies among neural responses to represent information about a visual stimulus. To study this issue, three statistical decoders were used to extract the information in the responses of model neurons about the binocular disparities present in simulated pairs of left-eye and right-eye images: (1) the full joint probability decoder considered all possible statistical relations among neural responses as potentially important; (2) the dependence tree decoder also considered all possible relations as potentially important, but it approximated high-order statistical correlations using a computationally tractable procedure; and (3) the independent response decoder, which assumed that neural responses are statistically independent, meaning that all correlations should be zero and thus can be ignored. Simulation results indicate that high-order correlations among model neuron responses contain significant information about binocular disparities and that the amount of this high-order information increases rapidly as a function of neural population size. Furthermore, the results highlight the potential importance of the dependence tree decoder to neuroscientists as a powerful but still practical way of approximating high-order correlations among neural responses.

An unconditionally stable FDTD method based on wave equation

2008 ◽  
Vol 51 (2) ◽  
pp. 529-532 ◽  
Author(s):  
Hai Lin ◽  
Gaofeng Wang ◽  
Feng Liang
Keyword(s):  
Wave Equation ◽  
Fdtd Method ◽  
Unconditionally Stable

scholarly journals Compressive Sensing in Signal Processing: Algorithms and Transform Domain Formulations

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Irena Orović ◽  
Vladan Papić ◽  
Cornel Ioana ◽  
Xiumei Li ◽  
Srdjan Stanković
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Compressive Sensing ◽  
Signal Reconstruction ◽  
Signal Acquisition ◽  
Transform Domain ◽  
Hermite Transform ◽  
Time Frequency ◽  
Reconstruction Methods ◽  
Fourier Transform Domain

Compressive sensing has emerged as an area that opens new perspectives in signal acquisition and processing. It appears as an alternative to the traditional sampling theory, endeavoring to reduce the required number of samples for successful signal reconstruction. In practice, compressive sensing aims to provide saving in sensing resources, transmission, and storage capacities and to facilitate signal processing in the circumstances when certain data are unavailable. To that end, compressive sensing relies on the mathematical algorithms solving the problem of data reconstruction from a greatly reduced number of measurements by exploring the properties of sparsity and incoherence. Therefore, this concept includes the optimization procedures aiming to provide the sparsest solution in a suitable representation domain. This work, therefore, offers a survey of the compressive sensing idea and prerequisites, together with the commonly used reconstruction methods. Moreover, the compressive sensing problem formulation is considered in signal processing applications assuming some of the commonly used transformation domains, namely, the Fourier transform domain, the polynomial Fourier transform domain, Hermite transform domain, and combined time-frequency domain.

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