Inverse Design of Three-Dimensional Frequency Selective Structures and Metamaterials using Multi-Objective Lazy Ant Colony Optimization

With the rise of big data and the “internet of things,” wireless signals permeate today’s environment more than ever before. As the demand for information and security continues to expand, the need for filtering a crowded signal space will become increasingly important. Although existing devices can achieve this with additional components, such as in-line filters and low noise amplifiers, these approaches introduce additional bulk, cost and complexity. An alternative, low-cost solution to filtering these signals can be achieved through the use of Frequency Selective Surfaces (FSSs), which are commonly used in antennas, polarizers, radomes, and intelligent architecture. FSSs typically consist of a doubly-periodic array of unit cells, which acts as a spatial electromagnetic filter that selectively rejects or transmits electromagnetic waves, based on the unit cell’s geometry and material properties. Unlike traditional analog filters, spatial filters must also account for the polarization and incidence angle of signals; thus, an ideal FSS maintains a given frequency response for all polarizations and incidence angles. Traditional FSS designs have ranged from planar structures with canonical shapes to miniaturized and multi-layer designs using fractals and other space-filling geometries. More recently, FSS research has expanded into three-dimensional (3D) designs, which have demonstrated enhanced fields of view over traditional planar and multi-layer designs. To date, nearly all FSSs still suffer from significant shifts in resonant frequencies or onset of grating lobes at incidence angles beyond 60 degrees in one or more polarizations. Additionally, while recent advances in additive manufacturing techniques have made fully 3D FSS designs increasingly popular, design tools to exploit these fabrication methods to develop FSSs with ultra-wide Fields of View (FOV) do not currently exist. In this dissertation, a Multi-Objective Lazy Ant Colony Optimization (MOLACO) scheme will be introduced and applied to the problem of 3D FSS design for extreme FOVs. The versatility of this algorithm will further be demonstrated through application to the design of meander line antennas, optical antennas, and phase-gradient metasurfaces

Reconstruction Methods for Free-Breathing Dynamic Contrast-Enhanced MRI

Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is a valuable diagnostic tool due to the combination of anatomical and physiological information it provides. However, the sequential sampling of MRI presents an inherent tradeoff between spatial and temporal resolution. Compressed Sensing (CS) methods have been applied to undersampled MRI to reconstruct full-resolution images at sub-Nyquist sampling rates. In exchange for shorter data acquisition times, CS-MRI requires more computationally intensive iterative reconstruction methods. We present several model-based image reconstruction (MBIR) methods to improve the spatial and temporal resolution of MR images and/or the computational time for multi-coil MRI reconstruction. We propose efficient variable splitting (VS) methods for support-constrained MRI reconstruction, image reconstruction and denoising with non-circulant boundary conditions, and improved temporal regularization for breast DCE-MRI. These proposed VS algorithms decouple the system model and sparsity terms of the convex optimization problem. By leveraging matrix structures in the system model and sparsifying operator, we perform alternating minimization over a list of auxiliary variables, each of which can be performed efficiently. We demonstrate the computational benefits of our proposed VS algorithms compared to similar proposed methods. We also demonstrate convergence guarantees for two proposed methods, ADMM-tridiag and ADMM-FP-tridiag. With simulation experiments, we demonstrate lower error in spatial and temporal dimensions for these VS methods compared to other object models. We also propose a method for indirect motion compensation in 5D liver DCE-MRI. 5D MRI separates temporal changes due to contrast from anatomical changes due to respiratory motion into two distinct dimensions. This work applies a pre-computed motion model to perform motion-compensated regularization across the respiratory dimension and improve the conditioning of this highly sparse 5D reconstruction problem. We demonstrate a proof of concept using a digital phantom with contrast and respiratory changes, and we show preliminary results for motion model-informed regularization on in vivo patient data.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138498/1/mtle_1.pd

Image Processing Using FPGAs

This book presents a selection of papers representing current research on using field programmable gate arrays (FPGAs) for realising image processing algorithms. These papers are reprints of papers selected for a Special Issue of the Journal of Imaging on image processing using FPGAs. A diverse range of topics is covered, including parallel soft processors, memory management, image filters, segmentation, clustering, image analysis, and image compression. Applications include traffic sign recognition for autonomous driving, cell detection for histopathology, and video compression. Collectively, they represent the current state-of-the-art on image processing using FPGAs

The Cosmic Infrared Background ExpeRiments: Probing Large-Scale Structure Formation using Near-Infrared Sounding Rocket Payloads

The ensemble emission from all sources outside of the Milky Way is known as the extragalactic background light (EBL). At optical and near-infrared (NIR) wavelengths, the EBL is primarily stellar emission tracing back to the Epoch of Reionization (EOR) at redshifts z \u3e 6 when the first luminous structures formed. Given the large uncertainties in our understanding of the EOR, measurements of the EBL provide an important probe of the galaxies that were responsible for reionization. Direct observations of the EBL are challenging due to contamination from bright local foregrounds. In recent years, intensity mapping has emerged as a successful technique in which EBL fluctuations are measured on large angular scales where the known foreground contributions are minimal or well-modeled. To isolate the signals from EOR structures, intensity mapping can be applied to optical and NIR data to probe rest-frame UV emission in galaxies at z \u3e 6. Multiple intensity mapping studies including the first Cosmic Infrared Background ExpeRiment (CIBER-1) have found that the optical/NIR EBL intensity and its large-scale fluctuations exceed predictions from galaxy models. The excess is above EOR level and also persists at wavelengths \u3c 1 μm where we do not expect to see reionization signals. To explain the excess, a number of astrophysical sources have been proposed including intra-halo light (IHL) from low-mass stars at the outskirts of galaxies. Observations at 1.1 and 1.8 μm from CIBER-1 second and third flights suggest that the excess can be best described by a level of IHL comparable to the integrated light from known galaxy populations. While this result is intriguing, given CIBER-1 spectral coverage, the IHL and EOR components could not be distinguished, prompting interest in a new mission, CIBER-2. CIBER-2 is designed to disentangle the IHL and EOR signals using broader spectral coverage from 0.5 - 2.0 μm in six wavebands and larger light-gathering power. The wavebands are selected to provide 21 auto- and cross-spectra to probe the Lyman break that can distinguish EOR contributions from low-redshift foregrounds. CIBER-2 comprises a 28.5-cm telescope cooled to \u3c 100K using liquid nitrogen, and three HAWAII-2RG detectors coupled with dual- band filters to obtain data in six wavebands simultaneously. CIBER-2 is planned for four flights on the Black Brant IX sounding rocket, with the first flight in mid-2021. In this dissertation, I present my work on the CIBER-2 design, characterization and payload integration, as well as constructing the EBL fluctuation power spectra from the data taken from CIBER-1’s final flight. This analysis relies on a previously developed pipeline for use with earlier flights, but has been revised to capture the hardware changes in the final flight and the corresponding systematic uncertainties. I will also outline the expected development of CIBER-2 post-flight analysis and highlight the advantage of CIBER-2 data for EOR studies

A semidiscrete version of the Citti-Petitot-Sarti model as a plausible model for anthropomorphic image reconstruction and pattern recognition

In his beautiful book [66], Jean Petitot proposes a sub-Riemannian model for the primary visual cortex of mammals. This model is neurophysiologically justified. Further developments of this theory lead to efficient algorithms for image reconstruction, based upon the consideration of an associated hypoelliptic diffusion. The sub-Riemannian model of Petitot and Citti-Sarti (or certain of its improvements) is a left-invariant structure over the group $SE(2)$ of rototranslations of the plane. Here, we propose a semi-discrete version of this theory, leading to a left-invariant structure over the group $SE(2,N)$, restricting to a finite number of rotations. This apparently very simple group is in fact quite atypical: it is maximally almost periodic, which leads to much simpler harmonic analysis compared to $SE(2).$ Based upon this semi-discrete model, we improve on previous image-reconstruction algorithms and we develop a pattern-recognition theory that leads also to very efficient algorithms in practice.Comment: 123 pages, revised versio

High resolution solar observations in the context of space weather prediction

Space weather has a great impact on the Earth and human life. It is important to study and monitor active regions on the solar surface and ultimately to predict space weather based on the Sun\u27s activity. In this study, a system that uses the full power of speckle masking imaging by parallel processing to obtain high-spatial resolution images of the solar surface in near real-time has been developed and built. The application of this system greatly improves the ability to monitor the evolution of solar active regions and to predict the adverse effects of space weather. The data obtained by this system have also been used to study fine structures on the solar surface and their effects on the upper solar atmosphere. A solar active region has been studied using high resolution data obtained by speckle masking imaging. Evolution of a pore in an active region presented. Formation of a rudimentary penumbra is studied. The effects of the change of the magnetic fields on the upper level atmosphere is discussed. Coronal Mass Ejections (CMEs) have a great impact on space weather. To study the relationship between CMEs and filament disappearance, a list of 431 filament and prominence disappearance events has been compiled. Comparison of this list with CME data obtained by satellite has shown that most filament disappearances seem to have no corresponding CME events. Even for the limb events, only thirty percent of filament disappearances are associated with CMEs. A CME event that was observed on March 20, 2000 has been studied in detail. This event did not show the three-parts structure of typical CMEs. The kinematical and morphological properties of this event were examined

Computational Methods and Graphical Processing Units for Real-time Control of Tomographic Adaptive Optics on Extremely Large Telescopes.

Ground based optical telescopes suffer from limited imaging resolution as a result of the effects of atmospheric turbulence on the incoming light. Adaptive optics technology has so far been very successful in correcting these effects, providing nearly diffraction limited images. Extremely Large Telescopes will require more complex Adaptive Optics configurations that introduce the need for new mathematical models and optimal solvers. In addition, the amount of data to be processed in real time is also greatly increased, making the use of conventional computational methods and hardware inefficient, which motivates the study of advanced computational algorithms, and implementations on parallel processors. Graphical Processing Units (GPUs) are massively parallel processors that have so far demonstrated a very high increase in speed compared to CPUs and other devices, and they have a high potential to meet the real-time restrictions of adaptive optics systems. This thesis focuses on the study and evaluation of existing proposed computational algorithms with respect to computational performance, and their implementation on GPUs. Two basic methods, one direct and one iterative are implemented and tested and the results presented provide an evaluation of the basic concept upon which other algorithms are based, and demonstrate the benefits of using GPUs for adaptive optics

Interferometric observations to analyze circumstellar environments and planetary formation

Protoplanetary disks have a rich structure, with different physics playing a role in different regions of the disk. They are under constant evolution, due to a combination of various physical and chemical processes, e.g., accretion, photo-evaporation, gas-dust interactions, grain growth, and the interaction with protoplanets. The dynamic ranges involved span orders of magnitudes on spatial scales, orbital times, temperatures, and dust- or gas-densities. The extreme dynamic ranges involved in the structure and composition of these objects mean that very different observational techniques have to be combined together to probe their various regions. This work makes use of new interferometric and spectroscopic measurements in the infrared, together with published mid-infrared images and spectral energy distribution fluxes from UV to mm-wavelength, to instruct a new comprehension of the well-known IRS48 object, and uncover part of the delicate balance of physical processes at stake. This PhD reports the first direct imaging of the full extents of a polycyclic aromatic hydrocarbon and very small grains ring in a young circumstellar disk, presents a revised model for the IRS48 object to explain the rich and complex dust- and gas-environment observed from near-infrared to centimeter wavelengths. Also, the spectral type of the spectroscopic binary MWC361 is determined. This will lead to a precise characterization of the stellar parameters of this binary, opening a new window on the studying of the disappearance of the circumsecondary disk of the binary, while the circumprimary disk is still present. The leitmotif throughout this thesis is the understanding of the last moments of circumstellar disks, and the search for the processes which dissipate them. This particular step of the disk-evolution is one the most mysterious to date, yet it sets critical constraints on the by-product of circumstellar disks, exoplanets

Multi-GPU Acceleration of Iterative X-ray CT Image Reconstruction

X-ray computed tomography is a widely used medical imaging modality for screening and diagnosing diseases and for image-guided radiation therapy treatment planning. Statistical iterative reconstruction (SIR) algorithms have the potential to significantly reduce image artifacts by minimizing a cost function that models the physics and statistics of the data acquisition process in X-ray CT. SIR algorithms have superior performance compared to traditional analytical reconstructions for a wide range of applications including nonstandard geometries arising from irregular sampling, limited angular range, missing data, and low-dose CT. The main hurdle for the widespread adoption of SIR algorithms in multislice X-ray CT reconstruction problems is their slow convergence rate and associated computational time. We seek to design and develop fast parallel SIR algorithms for clinical X-ray CT scanners. Each of the following approaches is implemented on real clinical helical CT data acquired from a Siemens Sensation 16 scanner and compared to the straightforward implementation of the Alternating Minimization (AM) algorithm of O’Sullivan and Benac [1]. We parallelize the computationally expensive projection and backprojection operations by exploiting the massively parallel hardware architecture of 3 NVIDIA TITAN X Graphical Processing Unit (GPU) devices with CUDA programming tools and achieve an average speedup of 72X over a straightforward CPU implementation. We implement a multi-GPU based voxel-driven multislice analytical reconstruction algorithm called Feldkamp-Davis-Kress (FDK) [2] and achieve an average overall speedup of 1382X over the baseline CPU implementation by using 3 TITAN X GPUs. Moreover, we propose a novel adaptive surrogate-function based optimization scheme for the AM algorithm, resulting in more aggressive update steps in every iteration. On average, we double the convergence rate of our baseline AM algorithm and also improve image quality by using the adaptive surrogate function. We extend the multi-GPU and adaptive surrogate-function based acceleration techniques to dual-energy reconstruction problems as well. Furthermore, we design and develop a GPU-based deep Convolutional Neural Network (CNN) to denoise simulated low-dose X-ray CT images. Our experiments show significant improvements in the image quality with our proposed deep CNN-based algorithm against some widely used denoising techniques including Block Matching 3-D (BM3D) and Weighted Nuclear Norm Minimization (WNNM). Overall, we have developed novel fast, parallel, computationally efficient methods to perform multislice statistical reconstruction and image-based denoising on clinically-sized datasets