Efficient, wide-band coupled structural-acoustic computations combining time and frequency domain finite elements/equivalent sources

2019 ◽  
Vol 146 (4) ◽  
pp. 3006-3006
Author(s):  
John B. Fahnline
Keyword(s):  
Finite Elements ◽  
Frequency Domain ◽  
Wide Band ◽  
Equivalent Sources

scholarly journals Spectrum Sensing in Cognitive Radio using Frequency Domain

2019 ◽  
Vol 9 (2) ◽  
pp. 3475-3477
Keyword(s):  
Cognitive Radio ◽  
Frequency Domain ◽  
Spectrum Sensing ◽  
Bandwidth Allocation ◽  
Dynamic Spectrum Access ◽  
Signal To Noise Ratio ◽  
Wide Band ◽  
Energy Detection ◽  
Secondary Users ◽  
Sensing Applications

An efficient bandwidth allocation and dynamic bandwidth access away from its previous limits is referred as cognitive radio (CR).The limited spectrum with inefficient usage requires the advances of dynamic spectrum access approach, where the secondary users are authorized to utilize the unused temporary licensed spectrum. For this reason it is essential to analyze the absence/presence of primary users for spectrum usage. So spectrum sensing is the main requirement and developed to sense the absence/ presence of a licensed user. This paper shows the design model of energy detection based spectrum sensing in frequency domain utilizing Binary Symmetric Channel (BSC) ,Additive white real Gaussian channel (AWGN), Rayleigh fading channel users for 16-Quadrature Amplitude Modulation(QAM) which is utilized for the wide band sensing applications at low Signal to noise Ratio(SNR) level to reduce the false error identification. The spectrum sensing techniques has least computational complexity. Simulink model for the energy detection based spectrum sensing using frequency domain in MATLAB 2014a.

Frequency domain approach for probabilistic flutter analysis using stochastic finite elements

Meccanica ◽  
2019 ◽  
Vol 54 (14) ◽  
pp. 2207-2225 ◽  
Author(s):  
Sandeep Kumar ◽  
Amit Kumar Onkar ◽  
Manjuprasad Maligappa
Keyword(s):  
Finite Elements ◽  
Frequency Domain ◽  
Flutter Analysis ◽  
Stochastic Finite Elements ◽  
Frequency Domain Approach

scholarly journals Wide-Band Audio Coding Based on Frequency-Domain Linear Prediction

2010 ◽  
Vol 2010 ◽  
pp. 1-14
Author(s):  
Petr Motlicek ◽  
Sriram Ganapathy ◽  
Hynek Hermansky ◽  
Harinath Garudadri
Keyword(s):  
Frequency Domain ◽  
Linear Prediction ◽  
Wide Band ◽  
Audio Coding

Deep 3D Convolution Neural Network Methods for Brain White Matter Hybrid Computational Simulations

2020 ◽  
Author(s):  
Xuehai Wu ◽  
Assimina A. Pelegri
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
White Matter ◽  
Finite Elements ◽  
Frequency Domain ◽  
Finite Element Methods ◽  
Material Properties ◽  
Output Data ◽  
Micro Scale ◽  
Brain White Matter

Abstract Material properties of brain white matter (BWM) show high anisotropy due to the complicated internal three-dimensional microstructure and variant interaction between heterogeneous brain-tissue (axon, myelin, and glia). From our previous study, finite element methods were used to merge micro-scale Representative Volume Elements (RVE) with orthotropic frequency domain viscoelasticity to an integral macro-scale BWM. Quantification of the micro-scale RVE with anisotropic frequency domain viscoelasticity is the core challenge in this study. The RVE behavior is expressed by a viscoelastic constitutive material model, in which the frequency-related viscoelastic properties are imparted as storage modulus and loss modulus for the composite comprised of axonal fibers and extracellular glia. Using finite elements to build RVEs with anisotropic frequency domain viscoelastic material properties is computationally very consuming and resource-draining. Additionally, it is very challenging to build every single RVE using finite elements since the architecture of each RVE is arbitrary in an infinite data set. The architecture information encoded in the voxelized location is employed as input data and is consequently incorporated into a deep 3D convolution neural network (CNN) model that cross-references the RVEs’ material properties (output data). The output data (RVEs’ material properties) is calculated in parallel using an in-house developed finite element method, which models RVE samples of axon-myelin-glia composites. This novel combination of the CNN-RVE method achieved a dramatic reduction in the computation time compared with directly using finite element methods currently present in the literature.

scholarly journals Frequency-domain modeling of unshielded multiconductor power cables for periodic excitation with new experimental protocol for wide band parameter identification

2019 ◽  
Vol 101 (2) ◽  
pp. 333-343 ◽  
Author(s):  
Tamiris G. Bade ◽  
James Roudet ◽  
Jean-Michel Guichon ◽  
Carlos A. F. Sartori ◽  
Patrick Kuo-Peng ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Frequency Domain ◽  
Wide Band ◽  
Domain Modeling ◽  
Power Cables ◽  
Periodic Excitation ◽  
Experimental Protocol ◽  
Band Parameter

scholarly journals Dispersion-reduced spline acoustic finite elements for frequency-domain analysis

2013 ◽  
Vol 34 (3) ◽  
pp. 221-224 ◽  
Author(s):  
Takeshi Okuzono ◽  
Toru Otsuru ◽  
Reiji Tomiku ◽  
Noriko Okamoto
Keyword(s):  
Finite Elements ◽  
Frequency Domain ◽  
Domain Analysis ◽  
Frequency Domain Analysis
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