31 research outputs found
On the Compression of Translation Operator Tensors in FMM-FFT-Accelerated SIE Simulators via Tensor Decompositions
Tensor decomposition methodologies are proposed to reduce the memory
requirement of translation operator tensors arising in the fast multipole
method-fast Fourier transform (FMM-FFT)-accelerated surface integral equation
(SIE) simulators. These methodologies leverage Tucker, hierarchical Tucker
(H-Tucker), and tensor train (TT) decompositions to compress the FFT'ed
translation operator tensors stored in three-dimensional (3D) and
four-dimensional (4D) array formats. Extensive numerical tests are performed to
demonstrate the memory saving achieved by and computational overhead introduced
by these methodologies for different simulation parameters. Numerical results
show that the H-Tucker-based methodology for 4D array format yields the maximum
memory saving while Tucker-based methodology for 3D array format introduces the
minimum computational overhead. For many practical scenarios, all methodologies
yield a significant reduction in the memory requirement of translation operator
tensors while imposing negligible/acceptable computational overhead
Uncertainty Quantification for Electromagnetic Analysis via Efficient Collocation Methods.
Electromagnetic (EM) devices and systems often are fraught by uncertainty in their geometry, configuration, and excitation. These uncertainties (often termed ârandom variablesâ) strongly and nonlinearly impact voltages and currents on mission-critical circuits or receivers (often termed âobservablesâ). To ensure the functionality of such circuits or receivers, this dependency should be statistically characterized.
In this thesis, efficient collocation methods for uncertainty quantification in EM analysis are presented. First, a Stroud-based stochastic collocation method is introduced to statistically characterize electromagnetic compatibility and interference (EMC/EMI) phenomena on electrically large and complex platforms. Second, a multi-element probabilistic collocation (ME-PC) method suitable for characterizing rapidly varying and/or discontinuous observables is presented. Its applications to the statistical characterization of EMC/EMI phenomena on electrically and complex platforms and transverse magnetic wave propagation in complex mine environments are demonstrated. In addition, the ME-PC method is applied to the statistical characterization of EM wave propagation in complex mine environments with the aid of a novel fast multipole method and fast Fourier transform-accelerated surface integral equation solver -- the first-ever full-wave solver capable of characterizing EM wave propagation in hundreds of wavelengths long mine tunnels. Finally, an iterative high-dimensional model representation technique is proposed to statistically characterize EMC/EMI observables that involve a large number of random variables. The application of this technique to the genetic algorithm based optimization of EM devices is presented as well.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/100086/1/acyucel_1.pd
Computation of Electromagnetic Fields Scattered From Objects With Uncertain Shapes Using Multilevel Monte Carlo Method
Computational tools for characterizing electromagnetic scattering from
objects with uncertain shapes are needed in various applications ranging from
remote sensing at microwave frequencies to Raman spectroscopy at optical
frequencies. Often, such computational tools use the Monte Carlo (MC) method to
sample a parametric space describing geometric uncertainties. For each sample,
which corresponds to a realization of the geometry, a deterministic
electromagnetic solver computes the scattered fields. However, for an accurate
statistical characterization the number of MC samples has to be large. In this
work, to address this challenge, the continuation multilevel Monte Carlo
(CMLMC) method is used together with a surface integral equation solver. The
CMLMC method optimally balances statistical errors due to sampling of the
parametric space, and numerical errors due to the discretization of the
geometry using a hierarchy of discretizations, from coarse to fine. The number
of realizations of finer discretizations can be kept low, with most samples
computed on coarser discretizations to minimize computational cost.
Consequently, the total execution time is significantly reduced, in comparison
to the standard MC scheme.Comment: 25 pages, 10 Figure
A Depth-Adaptive Filtering Method for Effective GPR Tree Roots Detection in Tropical Area
This study presents a technique for processing Stepfrequency continuous wave
(SFCW) ground penetrating radar (GPR) data to detect tree roots. SFCW GPR is
portable and enables precise control of energy levels, balancing depth and
resolution trade-offs. However, the high-frequency components of the
transmission band suffers from poor penetrating capability and generates noise
that interferes with root detection. The proposed time-frequency filtering
technique uses a short-time Fourier transform (STFT) to track changes in
frequency spectrum density over time. To obtain the filter window, a weighted
linear regression (WLR) method is used. By adopting a conversion method that is
a variant of the chirp Z-Transform (CZT), the timefrequency window filters out
frequency samples that are not of interest when doing the frequency-to-time
domain data conversion. The proposed depth-adaptive filter window can
selfadjust to different scenarios, making it independent of soil information
and effectively determines subsurface tree roots. The technique is successfully
validated using SFCW GPR data from actual sites in a tropical area with
different soil moisture levels, and the two-dimensional (2D) radar map of
subsurface root systems is highly improved compared to existing methods.Comment: 10 pages, 12 figures, Accepted by IEEE TI
Accurate Tree Roots Positioning and Sizing over Undulated Ground Surfaces by Common Offset GPR Measurements
Tree roots detection is a popular application of the Ground-penetrating radar
(GPR). Normally, the ground surface above the tree roots is assumed to be flat,
and standard processing methods based on hyperbolic fitting are applied to the
hyperbolae reflection patterns of tree roots for detection purposes. When the
surface of the land is undulating (not flat), these typical hyperbolic fitting
methods becomes inaccurate. This is because, the reflection patterns change
with the uneven ground surfaces. When the soil surface is not flat, it is
inaccurate to use the peak point of an asymmetric reflection pattern to
identify the depth and horizontal position of the underground target. The
reflection patterns of the complex shapes due to extreme surface variations
results in analysis difficulties. Furthermore, when multiple objects are buried
under an undulating ground, it is hard to judge their relative positions based
on a B-scan that assumes a flat ground. In this paper, a roots fitting method
based on electromagnetic waves (EM) travel time analysis is proposed to take
into consideration the realistic undulating ground surface. A wheel-based (WB)
GPR and an antenna-height-fixed (AHF) GPR System are presented, and their
corresponding fitting models are proposed. The effectiveness of the proposed
method is demonstrated and validated through numerical examples and field
experiments.Comment: 11 pages, 6 figures, accepted by IEEE TI
DMRF-UNet: A Two-Stage Deep Learning Scheme for GPR Data Inversion under Heterogeneous Soil Conditions
Traditional ground-penetrating radar (GPR) data inversion leverages iterative
algorithms which suffer from high computation costs and low accuracy when
applied to complex subsurface scenarios. Existing deep learning-based methods
focus on the ideal homogeneous subsurface environments and ignore the
interference due to clutters and noise in real-world heterogeneous
environments. To address these issues, a two-stage deep neural network (DNN),
called DMRF-UNet, is proposed to reconstruct the permittivity distributions of
subsurface objects from GPR B-scans under heterogeneous soil conditions. In the
first stage, a U-shape DNN with multi-receptive-field convolutions (MRF-UNet1)
is built to remove the clutters due to inhomogeneity of the heterogeneous soil.
Then the denoised B-scan from the MRF-UNet1 is combined with the noisy B-scan
to be inputted to the DNN in the second stage (MRF-UNet2). The MRF-UNet2 learns
the inverse mapping relationship and reconstructs the permittivity distribution
of subsurface objects. To avoid information loss, an end-to-end training method
combining the loss functions of two stages is introduced. A wide range of
subsurface heterogeneous scenarios and B-scans are generated to evaluate the
inversion performance. The test results in the numerical experiment and the
real measurement show that the proposed network reconstructs the
permittivities, shapes, sizes, and locations of subsurface objects with high
accuracy. The comparison with existing methods demonstrates the superiority of
the proposed methodology for the inversion under heterogeneous soil conditions
3DInvNet: A Deep Learning-Based 3D Ground-Penetrating Radar Data Inversion
The reconstruction of the 3D permittivity map from ground-penetrating radar
(GPR) data is of great importance for mapping subsurface environments and
inspecting underground structural integrity. Traditional iterative 3D
reconstruction algorithms suffer from strong non-linearity, ill-posedness, and
high computational cost. To tackle these issues, a 3D deep learning scheme,
called 3DInvNet, is proposed to reconstruct 3D permittivity maps from GPR
C-scans. The proposed scheme leverages a prior 3D convolutional neural network
with a feature attention mechanism to suppress the noise in the C-scans due to
subsurface heterogeneous soil environments. Then a 3D U-shaped encoder-decoder
network with multi-scale feature aggregation modules is designed to establish
the optimal inverse mapping from the denoised C-scans to 3D permittivity maps.
Furthermore, a three-step separate learning strategy is employed to pre-train
and fine-tune the networks. The proposed scheme is applied to numerical
simulation as well as real measurement data. The quantitative and qualitative
results show the network capability, generalizability, and robustness in
denoising GPR C-scans and reconstructing 3D permittivity maps of subsurface
objects