Exoplanet Measurement to the Extreme: Novel Methods of Instrumentation and Data Extraction for Radial-velocity Spectrographs

The current generation of radial-velocity spectrographs are at the precipice of discovering the first Earth-like exoplanets orbiting in the habitable zones of nearby stars. Such detections require Doppler precision of approximately 10 cm/s, an order of magnitude better than the typical best-case measurement from the previous generation of instruments. Therefore, the radial-velocity community requires research and innovation from all angles to push our technology over the brink. This thesis presents multiple contributions to this field---ranging from the development of precision laser equipment to the implementation of advanced statistical data analysis algorithms---all in support of the EXtreme PREcision Spectrograph (EXPRES) with the goal of improving instrument precision and exoplanet detection capability. In Chapter 2, we demonstrate the effectiveness of quasi-chaotic high-amplitude agitation as an optimal form of modal noise mitigation in the optical fibers that feed into radial-velocity spectrographs. This technique is shown to improve radial-velocity error for a single-wavelength laser line from more than 10 m/s to less than 60 cm/s without affecting focal ratio degradation within the fiber. After development of an agitator based on this method for use with EXPRES, we find that combined radial-velocity precision across an entire laser frequency comb improves from 32.8 cm/s to 6.6 cm/s. In Chapter 3, I present aluminum nitride as a nonlinear optical material that can support frequency comb development from near-infrared to ultraviolet wavelengths. By injecting light from an aluminum nitride micro-ring into EXPRES, I demonstrate the material\u27s viability of producing resolvable comb lines throughout the bandpass of the instrument. I also prototype a 16 GHz electro-optic modulation comb in combination with an aluminum nitride waveguide as a device that could become a cheap broadband visible-wavelength astro-comb for radial-velocity spectrograph wavelength calibration. Finally, in Chapters 4 and 5, I present the EXPRES data extraction pipeline and the numerous novel algorithms that went into its design. Through the default version of the pipeline, including a flat-relative optimal extraction and chunk-by-chunk forward model radial-velocity measurement, we achieve 30 cm/s single-measurement precision on observations of stars with a signal-to-noise ratio of 250 measured at 550 nm. As demonstrated with 51 Peg b, the residual scatter of these observations after fitting with a single-planet Keplerian orbit is less than 90 cm/s. As alternatives to the default techniques, I also present my implementations of flat-relative spectro-perfectionism and B-spline regression stellar template forward modeling within the EXPRES pipeline. These methods provide comparable radial-velocity precision on observations of HD 3651 while also opening up many possibilities for future explorations with radial-velocity data analysis

Basic Science to Clinical Research: Segmentation of Ultrasound and Modelling in Clinical Informatics

The world of basic science is a world of minutia; it boils down to improving even a fraction of a percent over the baseline standard. It is a domain of peer reviewed fractions of seconds and the world of squeezing every last ounce of efficiency from a processor, a storage medium, or an algorithm. The field of health data is based on extracting knowledge from segments of data that may improve some clinical process or practice guideline to improve the time and quality of care. Clinical informatics and knowledge translation provide this information in order to reveal insights to the world of improving patient treatments, regimens, and overall outcomes. In my world of minutia, or basic science, the movement of blood served an integral role. The novel detection of sound reverberations map out the landscape for my research. I have applied my algorithms to the various anatomical structures of the heart and artery system. This serves as a basis for segmentation, active contouring, and shape priors. The algorithms presented, leverage novel applications in segmentation by using anatomical features of the heart for shape priors and the integration of optical flow models to improve tracking. The presented techniques show improvements over traditional methods in the estimation of left ventricular size and function, along with plaque estimation in the carotid artery. In my clinical world of data understanding, I have endeavoured to decipher trends in Alzheimer’s disease, Sepsis of hospital patients, and the burden of Melanoma using mathematical modelling methods. The use of decision trees, Markov models, and various clustering techniques provide insights into data sets that are otherwise hidden. Finally, I demonstrate how efficient data capture from providers can achieve rapid results and actionable information on patient medical records. This culminated in generating studies on the burden of illness and their associated costs. A selection of published works from my research in the world of basic sciences to clinical informatics has been included in this thesis to detail my transition. This is my journey from one contented realm to a turbulent one

Digital Image Processing

This book presents several recent advances that are related or fall under the umbrella of 'digital image processing', with the purpose of providing an insight into the possibilities offered by digital image processing algorithms in various fields. The presented mathematical algorithms are accompanied by graphical representations and illustrative examples for an enhanced readability. The chapters are written in a manner that allows even a reader with basic experience and knowledge in the digital image processing field to properly understand the presented algorithms. Concurrently, the structure of the information in this book is such that fellow scientists will be able to use it to push the development of the presented subjects even further

Volume II: Mining Innovation

Contemporary exploitation of natural raw materials by borehole, opencast, underground, seabed, and anthropogenic deposits is closely related to, among others, geomechanics, automation, computer science, and numerical methods. More and more often, individual fields of science coexist and complement each other, contributing to lowering exploitation costs, increasing production, and reduction of the time needed to prepare and exploit the deposit. The continuous development of national economies is related to the increasing demand for energy, metal, rock, and chemical resources. Very often, exploitation is carried out in complex geological and mining conditions, which are accompanied by natural hazards such as rock bursts, methane, coal dust explosion, spontaneous combustion, water, gas, and temperature. In order to conduct a safe and economically justified operation, modern construction materials are being used more and more often in mining to support excavations, both under static and dynamic loads. The individual production stages are supported by specialized computer programs for cutting the deposit as well as for modeling the behavior of the rock mass after excavation in it. Currently, the automation and monitoring of the mining works play a very important role, which will significantly contribute to the improvement of safety conditions. In this Special Issue of Energies, we focus on innovative laboratory, numerical, and industrial research that has a positive impact on the development of safety and exploitation in mining

Probing and Overcoming Extracellular Barriers to Inhaled Nanomedicine

Inhaled nanoparticles are a promising technology for delivering therapeutic molecules to the lungs to treat diseases such as cystic fibrosis (CF) and lung cancer. This dissertation focuses on characterizing and overcoming a critical extracellular barrier to inhaled nanomedicine: the mucus gel that coats the lung airway epithelium. Mucus is an adhesive meshwork that can trap particles and facilitate their removal from the lungs via mucociliary clearance. Although this defense mechanism protects the lungs from pathogens and particulate pollution, it can also prevent inhaled drug and gene nanoparticles from reaching their target. We therefore investigated strategies to improve particle penetration through human lung mucus. To measure nanoparticle transport, we used multiple particle tracking, a high resolution microscopy technique for quantifying movement of individual particles. First, we examined how particle size and surface chemistry affect mobility in respiratory mucus. We prepared polymeric nanoparticles densely coated with low molecular weight polyethylene glycol (PEG) to minimize muco-adhesion, and compared their transport to that of uncoated, muco-adhesive particles in respiratory mucus collected from endotracheal tubes of surgical patients without pulmonary comorbidities. We found that 100 and 200 nm diameter PEG-coated particles rapidly penetrated respiratory mucus, at rates exceeding their uncoated counterparts by more than one order of magnitude. In contrast, coated and uncoated particles 500 nm in diameter were sterically immobilized by the mucus mesh. These findings identify small size and adhesion-resistant surface as design criteria for therapeutic, respiratory-mucus-penetrating nanoparticles. Next, we studied viruses – nature’s nanoparticles – for CF lung gene therapy. We investigated whether CF sputum acts as a barrier to adeno-associated virus (AAV) gene vectors including AAV2, the serotype tested in CF clinical trials, and AAV1, a leading candidate for future trials. We found that sputum strongly impeded diffusion of AAV, regardless of serotype, and may thereby inhibit access to target cells. However, an AAV2 mutant engineered to have reduced heparin binding diffused twice as fast as AAV2 on average, presumably because of reduced adhesion to sputum. We also discovered that the mucolytic N-acetylcysteine could markedly enhance AAV diffusion. These studies offer strategies for increasing AAV penetration through sputum to improve clinical outcomes

Assessing impacts of flood events in urban areas to understand the resilience of the urban system

The introduction of the concept of urban resilience in managing risk of natural hazards in urban areas is closely related to pointing out suitable resilience assessments. In general, resilience can be defined as the ability of a given system to face and adapt itself to unexpected events, or stressful conditions. However, resilience can assume several meanings and it can also be applied to various different field of analysis. The technical literatures, indeed, offers a large set of definitions of resilience and many approaches have been developed so far to study this property. Topical relevance of resilience, especially in reference to natural hazards, is then combined with a broad scientific debate. In this general background, the thesis analyses urban resilience to flood risk through spatial analyses. Developing a conceptual definition of urban resilience, a methodological approach is presented to assess urban resilience of settlements located next to rivers. Assuming the configurational theory of Space Syntax to investigate the spatial layout, urban areas are analysed, in reference both to their spatial and functional features. Space Syntax is based on connections between the geometrical pattern of urban spaces (as well as spatial and visual relationships between the latter) and urban phenomena occurring within the said spaces. These connections are basically described by measures of topological centrality. Therefore, presence of flooded areas is examined according to its ability to affect spatial accessibility, consequently influencing the use of urban space. Applying the configurational approach, effects of flooded zones on spatial perception and human navigation are examined, towards evaluating consequences of floods on urban dynamics. All these aspects are related to the capability of flooded urban systems to mitigate effects of flooding, adapting itself to flood-induced consequences and preserving urban functions. This ability actually corresponds to the resilience of the considered urban systems. The proposed methodology consists of different stages of analysis. Syntactic features and urban morphology are considered, applying configurational techniques and statistical method to process syntactic data. As a result, a set of objective and quantitative measures are achieved, able to describe the degree of resilience of urban areas located on river banks, or rather, exposed to flood risk

The Telecommunications and Data Acquisition Report

Archival reports on developments in programs managed by JPL's Office of Telecommunications and Data Acquisition (TDA) are presented. Activities of the Deep Space Network (DSN) and its associated Ground Communications Facility (GCF) related to DSN advanced systems, systems implementation, and DSN operations are addressed. In addition, recent developments in the NASA SETI (Search for Extraterrestrial Intelligence) sky survey are summarized

Maintenance Management of Wind Turbines

“Maintenance Management of Wind Turbines” considers the main concepts and the state-of-the-art, as well as advances and case studies on this topic. Maintenance is a critical variable in industry in order to reach competitiveness. It is the most important variable, together with operations, in the wind energy industry. Therefore, the correct management of corrective, predictive and preventive politics in any wind turbine is required. The content also considers original research works that focus on content that is complementary to other sub-disciplines, such as economics, finance, marketing, decision and risk analysis, engineering, etc., in the maintenance management of wind turbines. This book focuses on real case studies. These case studies concern topics such as failure detection and diagnosis, fault trees and subdisciplines (e.g., FMECA, FMEA, etc.) Most of them link these topics with financial, schedule, resources, downtimes, etc., in order to increase productivity, profitability, maintainability, reliability, safety, availability, and reduce costs and downtime, etc., in a wind turbine. Advances in mathematics, models, computational techniques, dynamic analysis, etc., are employed in analytics in maintenance management in this book. Finally, the book considers computational techniques, dynamic analysis, probabilistic methods, and mathematical optimization techniques that are expertly blended to support the analysis of multi-criteria decision-making problems with defined constraints and requirements

Content Caching and Delivery in Heterogeneous Vehicular Networks

Connected and automated vehicles (CAVs), which enable information exchange and content delivery in real time, are expected to revolutionize current transportation systems for better driving safety, traffic efficiency, and environmental sustainability. However, the emerging CAV applications such as content delivery pose stringent requirements on latency, throughput, reliability, and global connectivity. The current wireless networks face significant challenges to satisfy the requirements due to scarce radio spectrum resources, inflexibility to dynamic traffic demands, and geographic-constrained fixed infrastructure deployment. To empower multifarious CAV content delivery, heterogeneous vehicular networks (HetVNets), which integrate the terrestrial networks with aerial networks formed by unmanned aerial vehicles (UAVs) and space networks constituting of low Earth orbit (LEO) satellites, can guarantee reliable, flexible, cost-effective, and globally seamless service provisioning. In addition, edge caching is a promising solution to facilitate content delivery by caching popular files in the HetVNet access points (APs) to relieve the backhaul traffic with a lower delivery delay. The main technical issues are: 1) to fully reveal the potential of HetVNets for content delivery performance enhancement, content caching scheme design in HetVNets should jointly consider network characteristics, vehicle mobility patterns, content popularity, and APs’ caching capacities; 2) to fully exploit the controllable mobility and agility of UAVs to support dynamic vehicular content demands, the caching scheme and trajectory design for UAVs should be jointly optimized, which has not been well addressed due to their intricate inter-coupling relationships; and 3) for caching-based content delivery in HetVNets, a cooperative content delivery scheme should be designed to enable the cooperation among different network segments with ingenious utilization of heterogeneous network resources. In this thesis, we design the content caching and delivery schemes in the caching-enabled HetVNet to address the three technical issues. First, we study the content caching in HetVNets with fixed terrestrial APs including cellular base stations (CBSs), Wi-Fi roadside units (RSUs), and TV white space (TVWS) stations. To characterize the intermittent network connection caused by limited network coverage and high vehicle mobility, we establish an on-off model with service interruptions to describe the vehicular content delivery process. Content coding then is leveraged to resist the impact of unstable network connections and enhance caching efficiency. By jointly considering file characteristics and network conditions, the content placement is formulated as an integer linear programming (ILP) problem. Adopting the idea of the student admission model, the ILP problem is then transformed into a many-to-one matching problem between content files and HetVNet APs and solved by our proposed stable-matching-based caching scheme. Simulation results demonstrate that the proposed scheme can achieve near-optimal performances in terms of delivery delay and offloading ratio with a low complexity. Second, UAV-aided caching is considered to assist vehicular content delivery in aerial-ground vehicular networks (AGVN) and a joint caching and trajectory optimization (JCTO) problem is investigated to jointly optimize content caching, content delivery, and UAV trajectory. To enable real-time decision-making in highly dynamic vehicular networks, we propose a deep supervised learning scheme to solve the JCTO problem. Specifically, we first devise a clustering-based two-layered (CBTL) algorithm to solve the JCTO problem offline. With a given content caching policy, we design a time-based graph decomposition method to jointly optimize content delivery and UAV trajectory, with which we then leverage the particle swarm optimization algorithm to optimize the content caching. We then design a deep supervised learning architecture of the convolutional neural network (CNN) to make online decisions. With the CNN-based model, a function mapping the input network information to output decisions can be intelligently learnt to make timely inferences. Extensive trace-driven experiments are conducted to demonstrate the efficiency of CBTL in solving the JCTO problem and the superior learning performance with the CNN-based model. Third, we investigate caching-assisted cooperative content delivery in space-air-ground integrated vehicular networks (SAGVNs), where vehicular content requests can be cooperatively served by multiple APs in space, aerial, and terrestrial networks. In specific, a joint optimization problem of vehicle-to-AP association, bandwidth allocation, and content delivery ratio, referred to as the ABC problem, is formulated to minimize the overall content delivery delay while satisfying vehicular quality-of-service (QoS) requirements. To address the tightly-coupled optimization variables, we propose a load- and mobility-aware ABC (LMA-ABC) scheme to solve the joint optimization problem as follows. We first decompose the ABC problem to optimize the content delivery ratio. Then the impact of bandwidth allocation on the achievable delay performance is analyzed, and an effect of diminishing delay performance gain is revealed. Based on the analysis results, the LMA-ABC scheme is designed with the consideration of user fairness, load balancing, and vehicle mobility. Simulation results demonstrate that the proposed LMA-ABC scheme can significantly reduce the cooperative content delivery delay compared to the benchmark schemes. In summary, we have investigated the content caching in terrestrial networks with fixed APs, joint caching and trajectory optimization in the AGVN, and caching-assisted cooperative content delivery in the SAGVN. The proposed schemes and theoretical results should provide useful guidelines for future research in the caching scheme design and efficient utilization of network resources in caching-enabled heterogeneous wireless networks