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

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 “random variables”) strongly and nonlinearly impact voltages and currents on mission-critical circuits or receivers (often termed “observables”). 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

Functional and Biochemical Alterations of the Medial Frontal Cortex in Obsessive-Compulsive Disorder

Context: The medial frontal cortex (MFC), including the dorsal anterior cingulate (dAC) and supplementary motor area (SMA), is critical for adaptive and inhibitory control of behaviour. Abnormally high MFC activity has been a consistent finding in functional neuroimaging studies of obsessive-compulsive disorder (OCD). However, the precise regions and the neural alterations associated with this abnormality remain unclear. Objective: To examine the functional and biochemical properties of the MFC in patients with OCD. Design: Cross-sectional design combining volume localized proton magnetic resonance spectroscopy (1H-MRS) and functional MRI (fMRI) with an inhibitory control paradigm (the Multi-Source Interference Task; MSIT) designed to activate the MFC. Setting: Healthy control participants and OCD patients recruited from the general community. Participants: Nineteen OCD patients (10 male, and 9 female) and nineteen age, gender, education and intelligence-matched healthy control participants. Main Outcome Measures: Psychometric measures of symptom severity, MSIT behavioural performance, blood-oxygen-level-dependent (BOLD) activation and 1H-MRS brain metabolite concentrations. Results: MSIT behavioural performance did not differ between OCD patients and control subjects. Reaction-time interference and response errors were correlated with BOLD activation in the dAC region in both groups. Relative to control subjects, OCD patients showed hyper- activation of the SMA during high response-conflict (incongruent > congruent) trials and hyper-activation of the rostral anterior cingulate (rAC) region during low response- conflict (incongruent < congruent) trials. OCD patients also showed reduced levels of neuronal N-acetylaspartate in the dAC region, which was negatively correlated with their BOLD activation of the region. Conclusions: Our findings suggest that hyper-activation of the medial frontal cortex in OCD patients may be a compensatory response to neural pathology in the region. This relationship may partly explain the nature of inhibitory control deficits that are frequently seen in this group and may serve as a focus of future treatment studies

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

Prevalence of cancer in relation to signs of periodontal inflammation

Funding Information: The study was supported by the Swedish Ministry of Health and Social Affairs (grant F84/ 189) and Karolinska Institutet, Stockholm, Sweden, and by grants from The Finnish Society of Sciences and Letters, the Finnish Medical Society, Finland, and the King Gustav V´s and Queen Victoria’ s Freemason´s Foundation, Sweden. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Publisher Copyright: © 2022 Meurman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.We investigated the associations between periodontal inflammation (gingivitis and periodontitis) and all-kind malignancies, specifically breast and prostate cancer, in a cohort followed-up for 30 years. The study hypothesis was based on the oral inflammation vs. systemic health paradigm. A sample of 2,168 subjects from an original cohort of 105,718 individuals from the greater Stockholm area in Sweden that had been followed since 1985 was investigated. Swedish national health registers were used in the study. Chi-square tests and logistic multiple regression analyses were conducted. The results showed that periodontitis was significantly associated with any cancer after adjusting for gender, age, income, and education (p = 0.015). The probability of getting cancer increased on average by 38% if the patient had periodontitis vs. had not; the odds ratio was 1.380 (95% confidence interval l.066-1.786). No significant association was observed between periodontitis and breast cancer (p = 0.608), while the association between periodontitis and prostate cancer tended towards significance (p = 0.082). However, no statistically significant difference was found between the observed and the calculated distribution of any cancer in gingivitis groups (p = 0.079). Thus, the study hypothesis was partly confirmed by showing a statistically significant association between periodontitis and any cancer.Peer reviewe

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