A quasi-real-time inertialess microwave holographic imaging system

This thesis records the theoretical analysis and hardware development of a laboratory microwave imaging system which uses holographic principles. The application of an aperture synthesis technique and the electronic commutation of all antennae has resulted in a compact and economic assembly - which requires no moving parts and which, consequently, has a high field mapping speed potential. The relationship of this microwave holographic system to other established techniques is examined theoretically and the performance of the imaging system is demonstrated using conventional optically- and numerically-based reconstruction of the measured holograms. The high mapping speed potential of this system has allowed the exploitation of an imaging mode not usually associated with microwave holography. In particular, a certain antenna array specification leads to a versatile imaging system which corresponds closely in the laboratory scale to the widely used synthetic aperture radar principle. It is envisaged that the microwave holographic implementation of this latter principle be used as laboratory instrumentation in the elucidation of the interaction of hydrodynamic and electromagnetic waves. Some simple demonstrations of this application have been presented, and the concluding chapter also describes a suitable hardware specification. This thesis has also emphasised the hardware details of the imaging system since the development of the microwave and other electronic components represented a substantial part of this research and because the potential applications of the imaging principle have been found to be intimately linked to the tolerances of the various microwave components. Bibliography: pages 122-132

Studies of BONuS12 Radial GEM Detector and TCS Beam Spin Asymmetry in CLAS12

The Barely Offshell Nucleon Structure (BONuS12) experiment adopted the concept of spectator tagging technique to study the nearly-free neutron structure function F2n in the CLAS12 of Jefferson Lab. A novel Radial Time Projection Chamber (RTPC) detector was built, tested and integrated into the CLAS12 system to detect a back-moving low momentum tagged proton in d(e, ep)X deep-inelastic scattering. It was a 40 cm long gaseous detector consisting of 3 layers of cylindrical GEM foils for the charge amplification, with the data readout directly from the surrounding padboard. The RTPC detected the recoiling spectator proton, in coincidence with the scattered electron in the CLAS12. Nucleon structure functions are directly related to the partonic functions, quarks momentum distribution in one dimension. A Generalized Parton Distribution (GPD) came to the lime-light as it encodes the information of both longitudinal momentum and transverse position of partons inside the nucleons. Factorization of hard process such as DVCS allows to access GPDs. Timelike Compton Scattering (TCS), γp → γ∗p, is another process that allows to access the GPDs. TCS is studied experimentally in the CLAS12 of Jefferson lab using the quasi-real photoproduction of time-like photon which eventually decays to lepton pair. This dissertation presents the concept of spectator tagging in BONuS12, and the research and development efforts during the BONuS12 preparation leading up to the successful data-taking during spring and summer 2020. In addition, analysis framework to extract the beam spin asymmetry of TCS events through the CLAS12 Run group A data is presented

FPGA-based High Performance Diagnostics For Fusion

High performance diagnostics are an important aspect of fusion research. Increasing shot-lengths paired with the requirement for higher accuracy and speed make it mandatory to employ new technology to cope with the increasing demands on digitization and data handling. Field programmable gate arrays (FPGAs) are well known in high performance applications. Their ability to handle multiple fast data streams whilst remaining programmable make them an ideal tool for diagnostic development. Both the improvement of old and the design of new diagnostics can benefit from FPGA-technology and increase the amount of accessible physics significantly. In this work the developments on two FPGA-based diagnostics are presented. In the first part a new open-hardware low-cost FPGA-based digitizer is presented for the MAST-Upgrade (MAST-U) integral electron density interferometer. The system is shown to have an optically limited phase accuracy and a detection bandwidth of over 3.5 MHz. Data is acquired continuously at 20 MS/s and streamed to an acquisition PC via optical fiber. By employing a dual-FPGA approach real-time processing of the density signal can be achieved despite severly limited resources, thus providing a control signal for the MAST-U plasma control system system with less than 8 μs latency. Due to MAST-U being still inoperable, in-situ testing has been conducted on the ASDEX Upgrade, where fast wave physics up to 3.5 MHz could first be observed. The second part presents developments to the Synthetic Aperture Microwave Imaging (SAMI) diagnostic. In addition to improving the utilization of long shot-lengths and enabling dual-polarized acquisition the system has been enhanced to continuously acquire active probing profiles for 2D Doppler back-scattering (DBS), a technique recently developed using SAMI. The aim is to measure pitch angle profiles to derive the edge current density. SAMI has been transferred to the NSTX-Upgrade and integrated into the experiment’s infrastructure, where it has been acquiring data since May 2016. As part of this move an investigation into near-field effects on SAMI’s image reconstruction algorithms was conducted

Geometric Inhomogeneous Random Graphs for Algorithm Engineering

The design and analysis of graph algorithms is heavily based on the worst case. In practice, however, many algorithms perform much better than the worst case would suggest. Furthermore, various problems can be tackled more efficiently if one assumes the input to be, in a sense, realistic. The field of network science, which studies the structure and emergence of real-world networks, identifies locality and heterogeneity as two frequently occurring properties. A popular model that captures these properties are geometric inhomogeneous random graphs (GIRGs), which is a generalization of hyperbolic random graphs (HRGs). Aside from their importance to network science, GIRGs can be an immensely valuable tool in algorithm engineering. Since they convincingly mimic real-world networks, guarantees about quality and performance of an algorithm on instances of the model can be transferred to real-world applications. They have model parameters to control the amount of heterogeneity and locality, which allows to evaluate those properties in isolation while keeping the rest fixed. Moreover, they can be efficiently generated which allows for experimental analysis. While realistic instances are often rare, generated instances are readily available. Furthermore, the underlying geometry of GIRGs helps to visualize the network, e.g.,~for debugging or to improve understanding of its structure. The aim of this work is to demonstrate the capabilities of geometric inhomogeneous random graphs in algorithm engineering and establish them as routine tools to replace previous models like the Erd\H{o}s-R{\\u27e}nyi model, where each edge exists with equal probability. We utilize geometric inhomogeneous random graphs to design, evaluate, and optimize efficient algorithms for realistic inputs. In detail, we provide the currently fastest sequential generator for GIRGs and HRGs and describe algorithms for maximum flow, directed spanning arborescence, cluster editing, and hitting set. For all four problems, our implementations beat the state-of-the-art on realistic inputs. On top of providing crucial benchmark instances, GIRGs allow us to obtain valuable insights. Most notably, our efficient generator allows us to experimentally show sublinear running time of our flow algorithm, investigate the solution structure of cluster editing, complement our benchmark set of arborescence instances with a density for which there are no real-world networks available, and generate networks with adjustable locality and heterogeneity to reveal the effects of these properties on our algorithms

Excellentia Eminentia Effectio

"In these pages you will learn about the fascinating research endeavors that each of our faculty members is undertaking. We have divided their research into the broad categories of health, sustainability, information, and systems. While we recognize the imperfect nature of categorizing research that, by its very nature may be interdisciplinary or transdisciplinary, we nonetheless believe it will be helpful as a way to see the depth and breadth of our research endeavors within each grouping. As you read the profiles on these pages, I know you will begin to appreciate that, taken as a whole, the research spectrum at Columbia Engineering is exceptional and that, as our professors go about their work, they are at the cusp of making breakthroughs that will have a major impact on the way we live our lives today and tomorrow.

Recent advances in directional statistics

Mainstream statistical methodology is generally applicable to data observed in Euclidean space. There are, however, numerous contexts of considerable scientific interest in which the natural supports for the data under consideration are Riemannian manifolds like the unit circle, torus, sphere and their extensions. Typically, such data can be represented using one or more directions, and directional statistics is the branch of statistics that deals with their analysis. In this paper we provide a review of the many recent developments in the field since the publication of Mardia and Jupp (1999), still the most comprehensive text on directional statistics. Many of those developments have been stimulated by interesting applications in fields as diverse as astronomy, medicine, genetics, neurology, aeronautics, acoustics, image analysis, text mining, environmetrics, and machine learning. We begin by considering developments for the exploratory analysis of directional data before progressing to distributional models, general approaches to inference, hypothesis testing, regression, nonparametric curve estimation, methods for dimension reduction, classification and clustering, and the modelling of time series, spatial and spatio-temporal data. An overview of currently available software for analysing directional data is also provided, and potential future developments discussed.Comment: 61 page

Numerical modelling of additive manufacturing process for stainless steel tension testing samples

Nowadays additive manufacturing (AM) technologies including 3D printing grow rapidly and they are expected to replace conventional subtractive manufacturing technologies to some extents. During a selective laser melting (SLM) process as one of popular AM technologies for metals, large amount of heats is required to melt metal powders, and this leads to distortions and/or shrinkages of additively manufactured parts. It is useful to predict the 3D printed parts to control unwanted distortions and shrinkages before their 3D printing. This study develops a two-phase numerical modelling and simulation process of AM process for 17-4PH stainless steel and it considers the importance of post-processing and the need for calibration to achieve a high-quality printing at the end. By using this proposed AM modelling and simulation process, optimal process parameters, material properties, and topology can be obtained to ensure a part 3D printed successfully