6,932 research outputs found
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
A Hybrid Platform for Wideband Reconfigurable Nonlinear Metamaterials
Optical frequency conversion processes, such as second- and third-harmonic generations, are commonly realized in nonlinear optics, offering application opportunities in photonics, chemistry, material science, and biosensing. Limited by intrinsically weak nonlinear responses of bulk materials, complex phase-matching techniques are typically required to realize significant nonlinear frequency conversions. Frequency preserving nonlinear processes, such as the optical Kerr effect, have potential for computing applications, due to efficient optical-intensity-dependent operations. Metamaterials and metasurfaces of artificially engineered and properly ordered building block arrays have been introduced to manipulate linear and nonlinear light-matter interactions at the subwavelength scale. I leverage high-refractive-index phase-change materials (PCMs) germanium antimony telluride (GST) and antimony sulfide (Sb2S3) that inherently exhibit strongly active tunable large optical nonlinearities to enhance the tunability of nonlinear metamaterials and metasurfaces in this thesis. I also demonstrate enhanced optical nonlinearities in passive high-index silicon (Si)-based metasurfaces that have various mode engineering opportunities and are easy to fabricate with mature techniques. The objective of research in this thesis is to demonstrate a hybrid platform for wideband reconfigurable nonlinear photonic metamaterials. The platform is composed of hybridized reconfigurable PCMs GST and Sb2S3 as well as static Si nano-building blocks. In the PCM-based research part, I demonstrate wideband-tunable third-harmonic generation (THG) devices with subwavelength features using multiple crystallinity states of GST. I also demonstrate efficient fixed-band second-harmonic generation and THG switches with metamaterials based on and tuned by GST, respectively. Additionally, I numerically demonstrate all-Sb2S3 linear and THG metasurfaces for tunable focusing. These structures are of interest for on-chip nonlinear optical imaging, microscopy, and communication applications. The Si-based research part is focused on the use of high-quality-factor bound states in the continuum resonance phenomena and a deep learning technique to accelerate designing efficient nonlinear all-Si metasurfaces. These demonstrations have several potentials for addressing the existing challenges in nonlinear optical computing.Ph.D
EXAMINING PROTEIN CONFORMATIONAL DYNAMICS USING COMPUTATIONAL TECHNIQUES: STUDIES ON PHOSPHATIDYLINOSITOL-3-KINASE AND THE SODIUM-IODIDE SYMPORTER
Experimental biophysics techniques used to study proteins, polymers of amino acids that comprise most therapeutic targets of human disease, face limitations in their ability to interrogate the continual structural fluctuations exhibited by these macromolecules in the context of their myriad cellular functions. This dissertation aims to illustrate case studies that demonstrate how protein conformational dynamics can be characterized using computational methods, yielding novel insights into their functional regulation and activity. Towards this end, the work presented here describes two specific membrane proteins of therapeutic relevance: Phosphoinositide 3-kinase (PI3Kα), and the Na+/I- symporter (NIS).
The PI3KCA gene, encoding the catalytic subunit of the PI3Kα protein that phosphorylates phosphatidylinositol-4,5-bisphosphate (PIP2) to generate phosphatidylinositol-3,4,5-triphosphate (PIP3), is highly mutated in human cancer. As such, a deeper mechanistic understanding of PI3Kα could facilitate the development of novel chemotherapeutic approaches. The second chapter of this dissertation describes molecular dynamics (MD) simulations that were conducted to determine how PI3Kα conformations are influenced by physiological effectors and the nSH2 domain of a regulatory subunit, p85. The results reported here suggest that dynamic allostery plays a role in populating the catalytically competent conformation of PI3Kα.
NIS, a thirteen-helix transmembrane protein found in the thyroid and other tissues, transports iodide, a required constituent of thyroid hormones T3 and T4. Despite extensive experimental information and clinical data, many mechanistic details about NIS remain unresolved. The third chapter of this dissertation describes the results of unbiased and enhanced-sampling MD simulations of inwardly and outwardly open models of bound NIS under an enforced ion gradient. Simulations of NIS in the absence or presence of perchlorate are also described. The work presented in this dissertation aims to add to our mechanistic understanding of NIS ion transport and elucidate conformational states that occur between the inward and outward transitions of NIS in the absence and presence of bound Na+ and I- ions, which can provide valuable insight into its physiological activity and inform therapeutic interventions.
Taken together, these case studies demonstrate the ability of computational techniques to provide novel insights into the impact of structural dynamics on the functional regulation of therapeutically important biological macromolecules
Edge-resolved non-line-of-sight imaging
Over the past decade, the possibility of forming images of objects hidden from line-of-sight (LOS) view has emerged as an intriguing and potentially important expansion of computational imaging and computer vision technology. This capability could help soldiers anticipate danger in a tunnel system, autonomous vehicles avoid collision, and first responders safely traverse a building. In many scenarios where non-line-of-sight (NLOS) vision is desired, the LOS view is obstructed by a wall with a vertical edge. In this thesis we show that through modeling and computation, the impediment to LOS itself can be exploited for enhanced resolution of the hidden scene.
NLOS methods may be active, where controlled illumination of the hidden scene is used, or passive, relying only on already present light sources. In both active and passive NLOS imaging, measured light returns to the sensor after multiple diffuse bounces. Each bounce scatters light in all directions, eliminating directional information. When the scene is hidden behind a wall with a vertical edge, that edge occludes light as a function of its incident azimuthal angle around the edge. Measurements acquired on the floor adjacent to the occluding edge thus contain rich azimuthal information about the hidden scene. In this thesis, we explore several edge-resolved NLOS imaging systems that exploit the occlusion provided by a vertical edge. In addition to demonstrating novel edge-resolved NLOS imaging systems with real experimental data, this thesis includes modeling, performance bound analyses, and inversion algorithms for the proposed systems.
We first explore the use of a single vertical edge to form a 1D (in azimuthal angle) reconstruction of the hidden scene. Prior work demonstrated that temporal variation in a video of the floor may be used to image moving components of the hidden scene. In contrast, our algorithm reconstructs both moving and stationary hidden scenery from a single photograph, without assuming uniform floor albedo. We derive a forward model that describes the measured photograph as a nonlinear combination of the unknown floor albedo and the light from behind the wall. The inverse problem, which is the joint estimation of floor albedo and a 1D reconstruction of the hidden scene, is solved via optimization, where we introduce regularizers that help separate light variations in the measured photograph due to floor pattern and hidden scene, respectively.
Next, we combine the resolving power of a vertical edge with information from the relationship between intensity and radial distance to form 2D reconstructions from a single passive photograph. We derive a new forward model, accounting for radial falloff, and propose two inversion algorithms to form 2D reconstructions from a single photograph of the penumbra. The performances of both algorithms are demonstrated on experimental data corresponding to several different hidden scene configurations. A Cramer-Rao bound analysis further demonstrates the feasibility and limitations of this 2D corner camera.
Our doorway camera exploits the occlusion provided by the two vertical edges of a doorway for more robust 2D reconstruction of the hidden scene. This work provides and demonstrates a novel inversion algorithm to jointly estimate two views of change in the hidden scene, using the temporal difference between photographs acquired on the visible side of the doorway. A Cramer-Rao bound analysis is used to demonstrate the 2D resolving power of the doorway camera over other passive acquisition strategies and to motivate the novel biangular reconstruction grid.
Lastly, we present the active corner camera. Most existing active NLOS methods illuminate the hidden scene using a pulsed laser directed at a relay surface and collect time-resolved measurements of returning light. The prevailing approaches are inherently limited by the need for laser scanning, a process that is generally too slow to image hidden objects in motion. Methods that avoid laser scanning track the moving parts of the hidden scene as one or two point targets. In this work, based on more complete optical response modeling yet still without multiple illumination positions, we demonstrate accurate reconstructions of objects in motion and a `map’ of the stationary scenery behind them. This new ability to count, localize, and characterize the sizes of hidden objects in motion, combined with mapping of the stationary hidden scene could greatly improve indoor situational awareness in a variety of applications
Mars delivery service - development of the electro-mechanical systems of the Sample Fetch Rover for the Mars Sample Return Campaign
This thesis describes the development of the Sample Fetch Rover (SFR), studied for Mars Sample Return (MSR), an international campaign carried out in cooperation between the National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA). The focus of this document is the design of the electro-mechanical systems of the rover.
After placing this work into the general context of robotic planetary exploration and summarising the state of the art for what concerns Mars rovers, the architecture of the Mars Sample Return Campaign is presented. A complete overview of the current SFR architecture is provided, touching upon all the main subsystems of the spacecraft. For each area, it is discussed what are the design drivers, the chosen solutions and whether they use heritage technology (in particular from the ExoMars Rover) or new developments. This research focuses on two topics of particular interest, due to their relevance for the mission and the novelty of their design: locomotion and sample acquisition, which are discussed in depth.
The early SFR locomotion concepts are summarised, covering the initial trade-offs and discarded designs for higher traverse performance. Once a consolidated architecture was reached, the locomotion subsystem was developed further, defining the details of the suspension, actuators, deployment mechanisms and wheels. This technology is presented here in detail, including some key analysis and test results that support the design and demonstrate how it responds to the mission requirements.
Another major electro-mechanical system developed as part of this work is the one dedicated to sample tube acquisition. The concept of operations of this machinery was defined to be robust against the unknown conditions that characterise the mission. The design process led to a highly automated robotic system which is described here in its main components: vision system, robotic arm and tube storage
Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities
Traffic prediction plays a crucial role in alleviating traffic congestion
which represents a critical problem globally, resulting in negative
consequences such as lost hours of additional travel time and increased fuel
consumption. Integrating emerging technologies into transportation systems
provides opportunities for improving traffic prediction significantly and
brings about new research problems. In order to lay the foundation for
understanding the open research challenges in traffic prediction, this survey
aims to provide a comprehensive overview of traffic prediction methodologies.
Specifically, we focus on the recent advances and emerging research
opportunities in Artificial Intelligence (AI)-based traffic prediction methods,
due to their recent success and potential in traffic prediction, with an
emphasis on multivariate traffic time series modeling. We first provide a list
and explanation of the various data types and resources used in the literature.
Next, the essential data preprocessing methods within the traffic prediction
context are categorized, and the prediction methods and applications are
subsequently summarized. Lastly, we present primary research challenges in
traffic prediction and discuss some directions for future research.Comment: Published in Transportation Research Part C: Emerging Technologies
(TR_C), Volume 145, 202
Three-Dimensional Structural Analysis of Temple 16 and Rosalila at Copan Ruinas
Temple 16 is an ancient Maya structure located at the heart of the Copán Ruinas Acropolis in Western Honduras. Temple 16 contains several earlier structures within it that were built on top of each other throughout Copán’s history. One of these earlier structures, Rosalila, is one of the most culturally significant structures within the Acropolis due to its preservation. An intricate series of archeological tunnels have been excavated throughout Temple 16 to allow for its study. However, significant cracking has been observed within Rosalila and several tunnels have experienced partial collapse. This not only poses a life safety issue for those utilizing the tunnels, but also demonstrates the risk to invaluable cultural heritage. To this end, this thesis aims to provide a rigorous structural assessment of Temple 16 and the buried Rosalila structure, accounting for its complex 3D tunnel system, to understand the leading causes of tunnel collapse and structure deterioration.
Geometric data was collected of the acropolis, Temple 16, Rosalila, and the complex network of tunnels using a combination of ground-based lidar and uncrewed aerial systems. The resulting point clouds were vectorized to yield a series of connected surfaces, which were then meshed as a solid to facilitate finite element analysis. Analyses were conducted to understand both the current stress distribution within Temple 16 as well as to study the impact of various hypothetical tunnel backfilling scenarios to provide recommendations for preservation and tunnel safety. The generated finite element models were analyzed under three water saturation levels to account for the impact of heavy rainy seasons and water infiltration on the stress levels of the tunnels. From the analyses, sixty-three highly stressed areas were identified among the current tunnel system, with most of them being close or directly underneath Rosalila. From the tested hypothetical backfilling scenarios, it was found that, backfilling excavated sections can improve or worsen these stress concentrations depending on the location of the tunnel within the system. Finally, by analyzing Rosalila’s current geometry, it was observed that the structure experiences high levels of stress on its southern side due to its location within Temple 16. From this, it was concluded that fixing exposed areas of Rosalila that were affected by excavation on its southern side can significantly alleviate the existing deterioration and improve the stress flow in these areas.
Advisors: Christine E. Wittich & Richard L. Wood
Development of Novel Nano Platforms and Machine Learning Approaches for Raman Spectroscopy
In Raman spectroscopy, data analysis occupies a large amount of time and effort; thus, it is paramount to have the proper tools to extract the most meaning from the Raman analysis. This thesis explores improved ways to analyse Raman data mostly by using machine learning techniques available in Python. The substrate used throughout this thesis has been patterned through an electrohydrodynamic process that patterns micrometric pillars onto the substrate, which, after being gold coated, can generate surface-enhanced Raman scattering. An initial theoretical background was laid for the electrohydrodynamic process and additional observations regarding the fluid mechanics. Furthermore, when the structures are fabricated, and Raman measurements are taken, we show that it is possible to create an effective convolutional neural networks that systematically evaluate these patterns’ surface morphology and extracts features responsible for the surface-enhanced Raman scattering phenomenon. Being able to predict 90% of the time from optical microscope images and 99% of the time with atomic force microscopy images Additionally, a thorough machine learning analysis of the Raman literature was done. The best machine learning algorithms were put together into a script combined with a graphical user Interface that can run multiple commands such as principal component analysis and self-organizing maps, all in a centralised way. This way, we managed to consistently extract information from Raman and surface-enhanced Raman scattering spectra to open possibilities for precise peak analysis methods using a multi-Lorentzian fit algorithm
Computational modelling of the effect of side chain chemistry on the micro-structure and electrolyte interactions of mixed transport polymers
As we scale up our use of energy storage facilities to meet the demands of the future, the prob- lems associated with current energy storage technologies will grow to unacceptable levels. In this work I explore how we can develop high performing polymers for use as cathode materials in energy storage devices operating with aqueous electrolytes. Energy storage devices using these materials have the potential for low cost production and safe operation. Through a combination of atomistic simulation methods, this thesis relates aspects of the polymer chemistry to their microstructural properties, and subsequently to their ability to operate successfully as electrodes.Open Acces
Karst Hydrogeology in the Spray Mountains of Kananaskis, Alberta, Canada
Alpine karst aquifers serve as vital sources of groundwater and play an important role in supporting local ecosystems. This study investigated the hydrogeology of the Watridge Karst Spring in the southern Canadian Rocky Mountains, an example of a snowmelt-dominated alpine karst aquifer with rapid, long-distance flow. An annual water budget suggested a catchment area of around 20 km2 and dye tracer tests revealed groundwater velocities of up to 0.14 m s−1. The aquifer has a hierarchical conduit structure with underflow-overflow dynamics, and year-round discharge is sustained by fracture flow. This dual flow network allows the aquifer to behave similarly to a surface stream over long distances, but also as a large groundwater reservoir. An innovative approach was introduced to estimate groundwater response times to snowmelt in alpine karst springs using diurnal discharge and electrical conductivity fluctuations, expanding upon previous methods using spectral analysis and cross-correlation. A continuous record of response times was obtained throughout the entire snowmelt seasons of 2020 and 2021. These showed that dilution response time steadily increased alongside decreasing hydraulic head, while celerity remained constant. Geologically analogous karst catchments in the Rocky Mountains hold the potential for storing mountain water, which warrants further studies of groundwater flow in these systems. Climate change may impact the hydrological functioning of alpine karst springs, highlighting the importance of understanding these systems for sustainable water resource management
- …