Synthesis and characterization of Tellurium oxide glasses for photonic applications

Tellurite glasses are photonic materials of special interest to the branch of optoelectronic and communication, due to its important optical properties such as high refractive index, broad IR transmittance, low phonon energy etc. Tellurite glasses are solutions to the search of potential candidates for nonlinear optical devices. Low phonon energy makes it an efficient host for dopant ions like rare earths, allowing a better environment for radiative transitions. The dopant ions maintain majority of their individual properties in the glass matrix. Tellurites are less toxic than chalcogenides, more chemically and thermally stable which makes them a highly suitable fiber material for nonlinear applications in the midinfrared and they are of increased research interest in applications like laser, amplifier, sensor etc. Low melting point and glass transition temperature helps tellurite glass preparation easier than other glass families.In order to probe into the versatility of tellurite glasses in optoelectronic industry; we have synthesized and undertaken various optical studies on tellurite glasses. We have proved that the highly nonlinear tellurite glasses are suitable candidates in optical limiting, with comparatively lower optical limiting threshold. Tuning the optical properties of glasses is an important factor in the optoelectronic research. We have found that thermal poling is an efficient mechanism in tuning the optical properties of these materials. Another important nonlinear phenomenon found in zinc tellurite glasses is their ability to switch from reverse saturable absorption to saturable absorption in the presence of lanthanide ions. The proposed thesis to be submitted will have seven chapters.

Applications of Microwave Antenna Array for Wireless Power Transmission and Radar Imaging in Complex Environment

Thesis (Ph.D.)--University of Washington, 2016-08

Applications of a Collaborative Open Object Oriented Learning Environment to Electronics, Computing and Mathematics Education

A lightweight Java application suite has been developed and deployed allowing collaborative learning between students and tutors at remote locations. Students can engage in group activities online and also collaborate with tutors. A generic Java framework has been developed and applied to electronics, computing and mathematics education. The applications are respectively: (a) a digital circuit simulator, which allows students to collaborate in building simple or complex electronic circuits; (b) a Java programming environment where the paradigm is behavioural-based robotics, and (c) a differential equation solver useful in modelling of any complex and nonlinear dynamic system. Each student sees a common shared window on which may be added text or graphical objects and which can then be shared online. A built-in chat room supports collaborative dialogue. Students can work either in collaborative groups or else in teams as directed by the tutor. This paper summarises the technical architecture of the system as well as the pedagogical implications of the suite. A report of student evaluation is also presented distilled from use over a period of twelve months. We intend this suite to facilitate learning between groups at one or many institutions and to facilitate international collaboration. We also intend to use the suite as a tool to research the establishment and behaviour of collaborative learning groups. We shall make our software freely available to interested researchers.

Superregular matrices and applications to convolutional codes

The main results of this paper are twofold: the first one is a matrix theoretical result. We say that a matrix is superregular if all of its minors that are not trivially zero are nonzero. Given a a×b, a ≥ b, superregular matrix over a field, we show that if all of its rows are nonzero then any linear combination of its columns, with nonzero coefficients, has at least a−b + 1 nonzero entries. Secondly, we make use of this result to construct convolutional codes that attain the maximum possible distance for some fixed parameters of the code, namely, the rate and the Forney indices. These results answer some open questions on distances and constructions of convolutional codes posted in the literature.

Conception d’un mécanisme déployable à grand ratio d’expansion et de son système d’actionnement par roues d’inertie pour applications spatiales

Cette thèse propose de développer des mécanismes déployables pour applications spatiales ainsi que des modes d’actionnement permettant leur déploiement et le contrôle de l’orientation en orbite de l’engin spatial les supportant. L’objectif étant de permettre le déploiement de surfaces larges pour des panneaux solaires, coupoles de télécommunication ou sections de station spatiale, une géométrie plane simple en triangle est retenue afin de pouvoir être assemblée en différents types de surfaces. Les configurations à membrures rigides proposées dans la littérature pour le déploiement de solides symétriques sont optimisées et adaptées à l’expansion d’une géométrie ouverte, telle une coupole. L’optimisation permet d’atteindre un ratio d’expansion plan pour une seule unité de plus de 5, mais présente des instabilités lors de l’actionnement d’un prototype. Le principe de transmission du mouvement d’un étage à l’autre du mécanisme est revu afin de diminuer la sensibilité des performances du mécanisme à la géométrie de ses membrures internes. Le nouveau modèle, basé sur des courroies crantées, permet d’atteindre des ratios d’expansion plans supérieurs à 20 dans certaines configurations. L’effet des principaux facteurs géométriques de conception est étudié afin d’obtenir une relation simple d’optimisation du mécanisme plan pour adapter ce dernier à différents contextes d’applications. La géométrie identique des faces triangulaires de chaque surface déployée permet aussi l’empilement de ces faces pour augmenter la compacité du mécanisme. Une articulation spécialisée est conçue afin de permettre le dépliage des faces puis leur déploiement successivement. Le déploiement de grandes surfaces ne se fait pas sans influencer lourdement l’orientation et potentiellement la trajectoire de l’engin spatial, aussi, différentes stratégies de contrôle de l’orientation novatrices sont proposées. Afin de tirer profit d’une grande surface, l’actionnement par masses ponctuelles en périphérie du mécanisme est présentée, ses équations dynamiques sont dérivées et simulées pour en observer les performances. Celles-ci démontrent le potentiel de cette stratégie de réorientation, sans obstruction de l’espace central du satellite de base, mais les performances restent en deçà de l’effet d’une roue d’inertie de masse équivalente. Une stratégie d’actionnement redondant par roue d’inertie est alors présentée pour différents niveaux de complexité de mécanismes dont toutes les articulations sont passives, c’est-à-dire non actionnées. Un mécanisme à quatre barres plan est simulé en boucle fermée avec un contrôleur simple pour valider le contrôle d’un mécanisme ciseau commun. Ces résultats sont étendus à la dérivation des équations dynamiques d’un mécanisme sphérique à quatre barres, qui démontre le potentiel de l’actionnement par roue d’inertie pour le contrôle de la configuration et de l’orientation spatiale d’un tel mécanisme. Un prototype à deux corps ayant chacun une roue d’inertie et une seule articulation passive les reliant est réalisé et contrôlé grâce à un suivi par caméra des modules. Le banc d’essai est détaillé, ainsi que les défis que l’élimination des forces externes ont représenté dans sa conception. Les résultats montrent que le système est contrôlable en orientation et en configuration. La thèse se termine par une étude de cas pour l’application des principaux systèmes développés dans cette recherche. La collecte de débris orbitaux de petite et moyenne taille est présentée comme un problème n’ayant pas encore eu de solution adéquate et posant un réel danger aux missions spatiales à venir. L’unité déployable triangulaire entraînée par courroies est dupliquée de manière à former une coupole de plusieurs centaines de mètres de diamètre et est proposée comme solution pour capturer et ralentir ces catégories de débris. Les paramètres d’une mission à cette fin sont détaillés, ainsi que le potentiel de réorientation que les roues d’inertie permettent en plus du contrôle de son déploiement. Près de 2000 débris pourraient être retirés en moins d’un an en orbite basse à 819 km d’altitude.

CAUSAL INFERENCE WITH A CONTINUOUS TREATMENT AND OUTCOME: ALTERNATIVE ESTIMATORS FOR PARAMETRIC DOSE-RESPONSE FUNCTIONS WITH APPLICATIONS.

Causal inference with a continuous treatment is a relatively under-explored problem. In this dissertation, we adopt the potential outcomes framework. Potential outcomes are responses that would be seen for a unit under all possible treatments. In an observational study where the treatment is continuous, the potential outcomes are an uncountably infinite set indexed by treatment dose. We parameterize this unobservable set as a linear combination of a finite number of basis functions whose coefficients vary across units. This leads to new techniques for estimating the population average dose-response function (ADRF). Some techniques require a model for the treatment assignment given covariates, some require a model for predicting the potential outcomes from covariates, and some require both. We develop these techniques using a framework of estimating functions, compare them to existing methods for continuous treatments, and simulate their performance in a population where the ADRF is linear and the models for the treatment and/or outcomes may be misspecified. We also extend the comparisons to a data set of lottery winners in Massachusetts. Next, we describe the methods and functions in the R package causaldrf using data from the National Medical Expenditure Survey (NMES) and Infant Health and Development Program (IHDP) as examples. Additionally, we analyze the National Growth and Health Study (NGHS) data set and deal with the issue of missing data. Lastly, we discuss future research goals and possible extensions.

Tensor Completion for Multidimensional Inverse Problems with Applications to Magnetic Resonance Relaxometry

This thesis deals with tensor completion for the solution of multidimensional inverse problems. We study the problem of reconstructing an approximately low rank tensor from a small number of noisy linear measurements. New recovery guarantees, numerical algorithms, non-uniform sampling strategies, and parameter selection algorithms are developed. We derive a fixed point continuation algorithm for tensor completion and prove its convergence. A restricted isometry property (RIP) based tensor recovery guarantee is proved. Probabilistic recovery guarantees are obtained for sub-Gaussian measurement operators and for measurements obtained by non-uniform sampling from a Parseval tight frame. We show how tensor completion can be used to solve multidimensional inverse problems arising in NMR relaxometry. Algorithms are developed for regularization parameter selection, including accelerated k-fold cross-validation and generalized cross-validation. These methods are validated on experimental and simulated data. We also derive condition number estimates for nonnegative least squares problems. Tensor recovery promises to significantly accelerate N-dimensional NMR relaxometry and related experiments, enabling previously impractical experiments. Our methods could also be applied to other inverse problems arising in machine learning, image processing, signal processing, computer vision, and other fields.

Bioanalytical applications of microfluidic devices

The first part of the thesis describes a new patterning technique--microfluidic contact printing--that combines several of the desirable aspects of microcontact printing and microfluidic patterning and addresses some of their important limitations through the integration of a track-etched polycarbonate (PCTE) membrane. Using this technique, biomolecules (e.g., peptides, polysaccharides, and proteins) were printed in high fidelity on a receptor modified polyacrylamide hydrogel substrate. The patterns obtained can be controlled through modifications of channel design and secondary programming via selective membrane wetting. The protocols support the printing of multiple reagents without registration steps and fast recycle times. The second part describes a non-enzymatic, isothermal method to discriminate single nucleotide polymorphisms (SNPs). SNP discrimination using alkaline dehybridization has long been neglected because the pH range in which thermodynamic discrimination can be done is quite narrow. We found, however, that SNPs can be discriminated by the kinetic differences exhibited in the dehybridization of PM and MM DNA duplexes in an alkaline solution using fluorescence microscopy. We combined this method with multifunctional encoded hydrogel particle array (fabricated by stop-flow lithography) to achieve fast kinetics and high versatility. This approach may serve as an effective alternative to temperature-based method for analyzing unamplified genomic DNA in point-of-care diagnostic.

Transactions and schema evolution in a persistent object-oriented programming system

Applications are subject of a continuous evolution process with a profound impact on their underlining data model, hence requiring frequent updates in the applications' class structure and database structure as well. This twofold problem, schema evolution and instance adaptation, usually known as database evolution, is addressed in this thesis. Additionally, we address concurrency and error recovery problems with a novel meta-model and its aspect-oriented implementation. Modern object-oriented databases provide features that help programmers deal with object persistence, as well as all related problems such as database evolution, concurrency and error handling. In most systems there are transparent mechanisms to address these problems, nonetheless the database evolution problem still requires some human intervention, which consumes much of programmers' and database administrators' work effort. Earlier research works have demonstrated that aspect-oriented programming (AOP) techniques enable the development of flexible and pluggable systems. In these earlier works, the schema evolution and the instance adaptation problems were addressed as database management concerns. However, none of this research was focused on orthogonal persistent systems. We argue that AOP techniques are well suited to address these problems in orthogonal persistent systems. Regarding the concurrency and error recovery, earlier research showed that only syntactic obliviousness between the base program and aspects is possible. Our meta-model and framework follow an aspect-oriented approach focused on the object-oriented orthogonal persistent context. The proposed meta-model is characterized by its simplicity in order to achieve efficient and transparent database evolution mechanisms. Our meta-model supports multiple versions of a class structure by applying a class versioning strategy. Thus, enabling bidirectional application compatibility among versions of each class structure. That is to say, the database structure can be updated because earlier applications continue to work, as well as later applications that have only known the updated class structure. The specific characteristics of orthogonal persistent systems, as well as a metadata enrichment strategy within the application's source code, complete the inception of the meta-model and have motivated our research work. To test the feasibility of the approach, a prototype was developed. Our prototype is a framework that mediates the interaction between applications and the database, providing them with orthogonal persistence mechanisms. These mechanisms are introduced into applications as an {\it aspect} in the aspect-oriented sense. Objects do not require the extension of any super class, the implementation of an interface nor contain a particular annotation. Parametric type classes are also correctly handled by our framework. However, classes that belong to the programming environment must not be handled as versionable due to restrictions imposed by the Java Virtual Machine. Regarding concurrency support, the framework provides the applications with a multithreaded environment which supports database transactions and error recovery. The framework keeps applications oblivious to the database evolution problem, as well as persistence. Programmers can update the applications' class structure because the framework will produce a new version for it at the database metadata layer. Using our XML based pointcut/advice constructs, the framework's instance adaptation mechanism is extended, hence keeping the framework also oblivious to this problem. The potential developing gains provided by the prototype were benchmarked. In our case study, the results confirm that mechanisms' transparency has positive repercussions on the programmer's productivity, simplifying the entire evolution process at application and database levels. The meta-model itself also was benchmarked in terms of complexity and agility. Compared with other meta-models, it requires less meta-object modifications in each schema evolution step. Other types of tests were carried out in order to validate prototype and meta-model robustness. In order to perform these tests, we used an OO7 small size database due to its data model complexity. Since the developed prototype offers some features that were not observed in other known systems, performance benchmarks were not possible. However, the developed benchmark is now available to perform future performance comparisons with equivalent systems. In order to test our approach in a real world scenario, we developed a proof-of-concept application. This application was developed without any persistence mechanisms. Using our framework and minor changes applied to the application's source code, we added these mechanisms. Furthermore, we tested the application in a schema evolution scenario. This real world experience using our framework showed that applications remains oblivious to persistence and database evolution. In this case study, our framework proved to be a useful tool for programmers and database administrators. Performance issues and the single Java Virtual Machine concurrent model are the major limitations found in the framework.

GPRM: a high performance programming framework for manycore processors

Processors with large numbers of cores are becoming commonplace. In order to utilise the available resources in such systems, the programming paradigm has to move towards increased parallelism. However, increased parallelism does not necessarily lead to better performance. Parallel programming models have to provide not only flexible ways of defining parallel tasks, but also efficient methods to manage the created tasks. Moreover, in a general-purpose system, applications residing in the system compete for the shared resources. Thread and task scheduling in such a multiprogrammed multithreaded environment is a significant challenge. In this thesis, we introduce a new task-based parallel reduction model, called the Glasgow Parallel Reduction Machine (GPRM). Our main objective is to provide high performance while maintaining ease of programming. GPRM supports native parallelism; it provides a modular way of expressing parallel tasks and the communication patterns between them. Compiling a GPRM program results in an Intermediate Representation (IR) containing useful information about tasks, their dependencies, as well as the initial mapping information. This compile-time information helps reduce the overhead of runtime task scheduling and is key to high performance. Generally speaking, the granularity and the number of tasks are major factors in achieving high performance. These factors are even more important in the case of GPRM, as it is highly dependent on tasks, rather than threads. We use three basic benchmarks to provide a detailed comparison of GPRM with Intel OpenMP, Cilk Plus, and Threading Building Blocks (TBB) on the Intel Xeon Phi, and with GNU OpenMP on the Tilera TILEPro64. GPRM shows superior performance in almost all cases, only by controlling the number of tasks. GPRM also provides a low-overhead mechanism, called “Global Sharing”, which improves performance in multiprogramming situations. We use OpenMP, as the most popular model for shared-memory parallel programming as the main GPRM competitor for solving three well-known problems on both platforms: LU factorisation of Sparse Matrices, Image Convolution, and Linked List Processing. We focus on proposing solutions that best fit into the GPRM’s model of execution. GPRM outperforms OpenMP in all cases on the TILEPro64. On the Xeon Phi, our solution for the LU Factorisation results in notable performance improvement for sparse matrices with large numbers of small blocks. We investigate the overhead of GPRM’s task creation and distribution for very short computations using the Image Convolution benchmark. We show that this overhead can be mitigated by combining smaller tasks into larger ones. As a result, GPRM can outperform OpenMP for convolving large 2D matrices on the Xeon Phi. Finally, we demonstrate that our parallel worksharing construct provides an efficient solution for Linked List processing and performs better than OpenMP implementations on the Xeon Phi. The results are very promising, as they verify that our parallel programming framework for manycore processors is flexible and scalable, and can provide high performance without sacrificing productivity.

STATISTICAL METHODS FOR ADVANCED ECONOMETRIC MODELS WITH APPLICATIONS TO VEHICLE HOLDING AND SPEED QUANTILES DISTRIBUTION

This dissertation proposes statistical methods to formulate, estimate and apply complex transportation models. Two main problems are part of the analyses conducted and presented in this dissertation. The first method solves an econometric problem and is concerned with the joint estimation of models that contain both discrete and continuous decision variables. The use of ordered models along with a regression is proposed and their effectiveness is evaluated with respect to unordered models. Procedure to calculate and optimize the log-likelihood functions of both discrete-continuous approaches are derived, and difficulties associated with the estimation of unordered models explained. Numerical approximation methods based on the Genz algortithm are implemented in order to solve the multidimensional integral associated with the unordered modeling structure. The problems deriving from the lack of smoothness of the probit model around the maximum of the log-likelihood function, which makes the optimization and the calculation of standard deviations very difficult, are carefully analyzed. A methodology to perform out-of-sample validation in the context of a joint model is proposed. Comprehensive numerical experiments have been conducted on both simulated and real data. In particular, the discrete-continuous models are estimated and applied to vehicle ownership and use models on data extracted from the 2009 National Household Travel Survey. The second part of this work offers a comprehensive statistical analysis of free-flow speed distribution; the method is applied to data collected on a sample of roads in Italy. A linear mixed model that includes speed quantiles in its predictors is estimated. Results show that there is no road effect in the analysis of free-flow speeds, which is particularly important for model transferability. A very general framework to predict random effects with few observations and incomplete access to model covariates is formulated and applied to predict the distribution of free-flow speed quantiles. The speed distribution of most road sections is successfully predicted; jack-knife estimates are calculated and used to explain why some sections are poorly predicted. Eventually, this work contributes to the literature in transportation modeling by proposing econometric model formulations for discrete-continuous variables, more efficient methods for the calculation of multivariate normal probabilities, and random effects models for free-flow speed estimation that takes into account the survey design. All methods are rigorously validated on both real and simulated data.

Dynamic Programming Approaches for Estimating and Applying Large-scale Discrete Choice Models

People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.

Solutions of Tikhonov functional equations and applications to multiplication operators on Szegö spaces

We consider a natural representation of solutions for Tikhonov functional equations. This will be done by applying the theory of reproducing kernels to the approximate solutions of general bounded linear operator equations (when defined from reproducing kernel Hilbert spaces into general Hilbert spaces), by using the Hilbert-Schmidt property and tensor product of Hilbert spaces. As a concrete case, we shall consider generalized fractional functions formed by the quotient of Bergman functions by Szegö functions considered from the multiplication operators on the Szegö spaces.

DNA charge neutralisation by linear polymers I: irreversible binding

We develop a deterministic mathematical model to describe the way in which polymers bind to DNA by considering the dynamics of the gap distribution that forms when polymers bind to a DNA plasmid. In so doing, we generalise existing theory to account for overlaps and binding cooperativity whereby the polymer binding rate depends on the size of the overlap The proposed mean-field models are then solved using a combination of numerical and asymptotic methods. We find that overlaps lead to higher coverage and hence higher charge neutralisations, results which are more in line with recent experimental observations. Our work has applications to gene therapy where polymers are used to neutralise the negative charges of the DNA phosphate backbone, allowing condensation prior to delivery into the nucleus of an abnormal cell.