Caretta caretta (Loggerhead Sea Turtle). Nest Architecture.

Researchers compared nest architecture in loggerhead sea turtles at natural beaches in Florida, USA and Brazil to determine how similarities and differences in female morphology and reproductive output in these two populations are reflected in the structure of the nest.

Full-range parallel Fourier-domain optical coherence tomography using sinusoidal phase-modulating interferometry

We demonstrate a full-range parallel Fourier-domain optical coherence tomography (FD-OCT) in which a tomogram free of mirror images as well as DC and autocorrelation terms is obtained in parallel. The phase and amplitude of two-dimensional spectral interferograms are accurately detected by using sinusoidal phase-modulating interferometry and a two-dimensional CCD camera, which allows for the reconstruction of two-dimensional complex spectral interferograms. By line-by-line inverse Fourier transformation of the two-dimensional complex spectral interferogram, a full-range parallel FD-OCT is realized. Tomographic images of two separated glass coverslips obtained with our method are presented as a proof-of-principle experiment.

Advanced Monte Carlo Simulation and Machine Learning for Frequency Domain Optical Coherence Tomography

Optical Coherence Tomography(OCT) is a popular, rapidly growing imaging technique with an increasing number of bio-medical applications due to its noninvasive nature. However, there are three major challenges in understanding and improving an OCT system: (1) Obtaining an OCT image is not easy. It either takes a real medical experiment or requires days of computer simulation. Without much data, it is difficult to study the physical processes underlying OCT imaging of different objects simply because there aren't many imaged objects. (2) Interpretation of an OCT image is also hard. This challenge is more profound than it appears. For instance, it would require a trained expert to tell from an OCT image of human skin whether there is a lesion or not. This is expensive in its own right, but even the expert cannot be sure about the exact size of the lesion or the width of the various skin layers. The take-away message is that analyzing an OCT image even from a high level would usually require a trained expert, and pixel-level interpretation is simply unrealistic. The reason is simple: we have OCT images but not their underlying ground-truth structure, so there is nothing to learn from. (3) The imaging depth of OCT is very limited (millimeter or sub-millimeter on human tissues). While OCT utilizes infrared light for illumination to stay noninvasive, the downside of this is that photons at such long wavelengths can only penetrate a limited depth into the tissue before getting back-scattered. To image a particular region of a tissue, photons first need to reach that region. As a result, OCT signals from deeper regions of the tissue are both weak (since few photons reached there) and distorted (due to multiple scatterings of the contributing photons). This fact alone makes OCT images very hard to interpret.

This thesis addresses the above challenges by successfully developing an advanced Monte Carlo simulation platform which is 10000 times faster than the state-of-the-art simulator in the literature, bringing down the simulation time from 360 hours to a single minute. This powerful simulation tool not only enables us to efficiently generate as many OCT images of objects with arbitrary structure and shape as we want on a common desktop computer, but it also provides us the underlying ground-truth of the simulated images at the same time because we dictate them at the beginning of the simulation. This is one of the key contributions of this thesis. What allows us to build such a powerful simulation tool includes a thorough understanding of the signal formation process, clever implementation of the importance sampling/photon splitting procedure, efficient use of a voxel-based mesh system in determining photon-mesh interception, and a parallel computation of different A-scans that consist a full OCT image, among other programming and mathematical tricks, which will be explained in detail later in the thesis.

Next we aim at the inverse problem: given an OCT image, predict/reconstruct its ground-truth structure on a pixel level. By solving this problem we would be able to interpret an OCT image completely and precisely without the help from a trained expert. It turns out that we can do much better. For simple structures we are able to reconstruct the ground-truth of an OCT image more than 98% correctly, and for more complicated structures (e.g., a multi-layered brain structure) we are looking at 93%. We achieved this through extensive uses of Machine Learning. The success of the Monte Carlo simulation already puts us in a great position by providing us with a great deal of data (effectively unlimited), in the form of (image, truth) pairs. Through a transformation of the high-dimensional response variable, we convert the learning task into a multi-output multi-class classification problem and a multi-output regression problem. We then build a hierarchy architecture of machine learning models (committee of experts) and train different parts of the architecture with specifically designed data sets. In prediction, an unseen OCT image first goes through a classification model to determine its structure (e.g., the number and the types of layers present in the image); then the image is handed to a regression model that is trained specifically for that particular structure to predict the length of the different layers and by doing so reconstruct the ground-truth of the image. We also demonstrate that ideas from Deep Learning can be useful to further improve the performance.

It is worth pointing out that solving the inverse problem automatically improves the imaging depth, since previously the lower half of an OCT image (i.e., greater depth) can be hardly seen but now becomes fully resolved. Interestingly, although OCT signals consisting the lower half of the image are weak, messy, and uninterpretable to human eyes, they still carry enough information which when fed into a well-trained machine learning model spits out precisely the true structure of the object being imaged. This is just another case where Artificial Intelligence (AI) outperforms human. To the best knowledge of the author, this thesis is not only a success but also the first attempt to reconstruct an OCT image at a pixel level. To even give a try on this kind of task, it would require fully annotated OCT images and a lot of them (hundreds or even thousands). This is clearly impossible without a powerful simulation tool like the one developed in this thesis.

Distributed Optimal Control of Cyber-Physical Systems: Controller Synthesis, Architecture Design and System Identification

The centralized paradigm of a single controller and a single plant upon which modern control theory is built is no longer applicable to modern cyber-physical systems of interest, such as the power-grid, software defined networks or automated highways systems, as these are all large-scale and spatially distributed. Both the scale and the distributed nature of these systems has motivated the decentralization of control schemes into local sub-controllers that measure, exchange and act on locally available subsets of the globally available system information. This decentralization of control logic leads to different decision makers acting on asymmetric information sets, introduces the need for coordination between them, and perhaps not surprisingly makes the resulting optimal control problem much harder to solve. In fact, shortly after such questions were posed, it was realized that seemingly simple decentralized optimal control problems are computationally intractable to solve, with the Wistenhausen counterexample being a famous instance of this phenomenon. Spurred on by this perhaps discouraging result, a concerted 40 year effort to identify tractable classes of distributed optimal control problems culminated in the notion of quadratic invariance, which loosely states that if sub-controllers can exchange information with each other at least as quickly as the effect of their control actions propagates through the plant, then the resulting distributed optimal control problem admits a convex formulation.

The identification of quadratic invariance as an appropriate means of "convexifying" distributed optimal control problems led to a renewed enthusiasm in the controller synthesis community, resulting in a rich set of results over the past decade. The contributions of this thesis can be seen as being a part of this broader family of results, with a particular focus on closing the gap between theory and practice by relaxing or removing assumptions made in the traditional distributed optimal control framework. Our contributions are to the foundational theory of distributed optimal control, and fall under three broad categories, namely controller synthesis, architecture design and system identification.

We begin by providing two novel controller synthesis algorithms. The first is a solution to the distributed H-infinity optimal control problem subject to delay constraints, and provides the only known exact characterization of delay-constrained distributed controllers satisfying an H-infinity norm bound. The second is an explicit dynamic programming solution to a two player LQR state-feedback problem with varying delays. Accommodating varying delays represents an important first step in combining distributed optimal control theory with the area of Networked Control Systems that considers lossy channels in the feedback loop. Our next set of results are concerned with controller architecture design. When designing controllers for large-scale systems, the architectural aspects of the controller such as the placement of actuators, sensors, and the communication links between them can no longer be taken as given -- indeed the task of designing this architecture is now as important as the design of the control laws themselves. To address this task, we formulate the Regularization for Design (RFD) framework, which is a unifying computationally tractable approach, based on the model matching framework and atomic norm regularization, for the simultaneous co-design of a structured optimal controller and the architecture needed to implement it. Our final result is a contribution to distributed system identification. Traditional system identification techniques such as subspace identification are not computationally scalable, and destroy rather than leverage any a priori information about the system's interconnection structure. We argue that in the context of system identification, an essential building block of any scalable algorithm is the ability to estimate local dynamics within a large interconnected system. To that end we propose a promising heuristic for identifying the dynamics of a subsystem that is still connected to a large system. We exploit the fact that the transfer function of the local dynamics is low-order, but full-rank, while the transfer function of the global dynamics is high-order, but low-rank, to formulate this separation task as a nuclear norm minimization problem. Finally, we conclude with a brief discussion of future research directions, with a particular emphasis on how to incorporate the results of this thesis, and those of optimal control theory in general, into a broader theory of dynamics, control and optimization in layered architectures.

Phase mapping of domain kinetics in lithium niobate by digital holographic interferometry

The phase mapping of domain kinetics under the uniform steady-state electric field is achieved and investigated in the LiNbO3 crystals by digital holographic interferometry. We obtained the sequences of reconstructed three-dimensional and two-dimensional wave-field phase distributions during the electric poling in the congruent and near stoichiometric LiNbO3 crystals. The phase mapping of individual domain nucleation and growth in the two crystals are obtained. It is found that both longitudinal and lateral domain growths are not linear during the electric poling. The phase mapping of domain wall motions in the two crystals is also obtained. Both the phase relaxation and the pinning-depinning mechanism are observed during the domain wall motion. The residual phase distribution is observed after the high-speed domain wall motion. The corresponding analyses and discussions are proposed to explain the phenomena.

Subwavelength imaging with two symmetrical interfaces by dielectric-tube photonic crystals

As distinct from coated photonic crystals, in this paper we propose a novel one that is made of dielectric tubes arranged in a close-packet square lattice. Without metallic cores, this structure is low-loss and convenient to fabricate. A left-handed frequency region is found in the second band by dispersion characteristic analysis. Without inactive modes for the transverse electric mode, negative refraction and subwavelength imaging are demonstrated by the finite-difference time-domain simulations with two symmetrical interfaces, i.e. Gamma X and Gamma M.

Tunable electronic transport characteristics of surface-architecture-controlled ZnO nanowire field effect transistors.

Surface-architecture-controlled ZnO nanowires were grown using a vapor transport method on various ZnO buffer film coated c-plane sapphire substrates with or without Au catalysts. The ZnO nanowires that were grown showed two different types of geometric properties: corrugated ZnO nanowires having a relatively smaller diameter and a strong deep-level emission photoluminescence (PL) peak and smooth ZnO nanowires having a relatively larger diameter and a weak deep-level emission PL peak. The surface morphology and size-dependent tunable electronic transport properties of the ZnO nanowires were characterized using a nanowire field effect transistor (FET) device structure. The FETs made from smooth ZnO nanowires with a larger diameter exhibited negative threshold voltages, indicating n-channel depletion-mode behavior, whereas those made from corrugated ZnO nanowires with a smaller diameter had positive threshold voltages, indicating n-channel enhancement-mode behavior.

TWO TUTORIAL EXAMPLES OF MULTIVARIABLE CONTROL SYSTEM DESIGN.

Two tutorial examples are presented which illustrate different methods of designing practical multivariable control systems using frequency-domain techniques. In the first case eigenvector alignment techniques are used to manipulate and shape the generalized Nyquist diagrams, while in the second case LQG theory in conjunction with singular value plots is employed. In both cases the designs are carried out on a modern computer-aided control-system design package.

Adjoint linearised Euler solver in the frequency domain for jet noise modelling

The paper is devoted to extending the new efficient frequency-domain method of adjoint Green's function calculation to curvilinear multi-block RANS domains for middle and farfield sound computations. Numerical details of the method such as grids, boundary conditions and convergence acceleration are discussed. Two acoustic source models are considered in conjunction with the method and acoustic modelling results are presented for a benchmark low-Reynolds-number jet case.

Bayesian estimation of simultaneous musical notes based on frequency domain modelling

This paper proposes a Bayesian method for polyphonic music description. The method first divides an input audio signal into a series of sections called snapshots, and then estimates parameters such as fundamental frequencies and amplitudes of the notes contained in each snapshot. The parameter estimation process is based on a frequency domain modelling and Gibbs sampling. Experimental results obtained from audio signals of test note patterns are encouraging; the accuracy is better than 80% for the estimation of fundamental frequencies in terms of semitones and instrument names when the number of simultaneous notes is two.

90° domain dynamics and relaxation in thin ferroelectric/ferroelastic films

We investigated the dynamics and relaxation of 90° domains in 60-nm-thick lead-zirconium titanate (PbZr0.3 T0.7 O3) films, with enhanced piezoresponse force microscopy. We show that under opposite electric fie ld, ferroelectric domains are reversibly switched while ferroelastic domains reorganize in a nonreversible way. Moreover, we show that the relaxation-time constant of 90° domains is two orders of magnitude shorter than for the previously reported 180° domains relaxation. Furthermore, we demonstrate the influence of geometry and scale on the relaxation process. Finally, we propose a relaxation mechanism for ferroelastic-ferroelectric systems, with implications for devices based on these materials. © 2010 The American Physical Society.

On the use of combined compact scheme for numerical solution of the two-layer oceanic shallow water equations

Many types of oceanic physical phenomena have a wide range in both space and time. In general, simplified models, such as shallow water model, are used to describe these oceanic motions. The shallow water equations are widely applied in various oceanic and atmospheric extents. By using the two-layer shallow water equations, the stratification effects can be considered too. In this research, the sixth-order combined compact method is investigated and numerically implemented as a high-order method to solve the two-layer shallow water equations. The second-order centered, fourth-order compact and sixth-order super compact finite difference methods are also used to spatial differencing of the equations. The first part of the present work is devoted to accuracy assessment of the sixth-order super compact finite difference method (SCFDM) and the sixth-order combined compact finite difference method (CCFDM) for spatial differencing of the linearized two-layer shallow water equations on the Arakawa's A-E and Randall's Z numerical grids. Two general discrete dispersion relations on different numerical grids, for inertia-gravity and Rossby waves, are derived. These general relations can be used for evaluation of the performance of any desired numerical scheme. For both inertia-gravity and Rossby waves, minimum error generally occurs on Z grid using either the sixth-order SCFDM or CCFDM methods. For the Randall's Z grid, the sixth-order CCFDM exhibits a substantial improvement , for the frequency of the barotropic and baroclinic modes of the linear inertia-gravity waves of the two layer shallow water model, over the sixth-order SCFDM. For the Rossby waves, the sixth-order SCFDM shows improvement, for the barotropic and baroclinic modes, over the sixth-order CCFDM method except on Arakawa's C grid. In the second part of the present work, the sixth-order CCFDM method is used to solve the one-layer and two-layer shallow water equations in their nonlinear form. In one-layer model with periodic boundaries, the performance of the methods for mass conservation is compared. The results show high accuracy of the sixth-order CCFDM method to simulate a complex flow field. Furthermore, to evaluate the performance of the method in a non-periodic domain the sixth-order CCFDM is applied to spatial differencing of vorticity-divergence-mass representation of one-layer shallow water equations to solve a wind-driven current problem with no-slip boundary conditions. The results show good agreement with published works. Finally, the performance of different schemes for spatial differencing of two-layer shallow water equations on Z grid with periodic boundaries is investigated. Results illustrate the high accuracy of combined compact method.

Characterize dynamic conformational space of human CCR5 extracellular domain by molecular modeling and molecular dynamics simulation

The chemokine receptor CCR5 is the receptor for several chemokines and major coreceptor for R5 human immunodeficiency virus type-1 strains entry into cell. Three-dimensional models of CCR5 were built by using homology modeling approach and 1 ns molecular dynamics (MD) simulation, because studies of site-directed mutagenesis and chimeric receptors have indicated that the N-terminus (Nt) and extracellular loops (ECLs) of CCR5 are important for ligands binding and viral fusion and entry, special attention was focused on disulfide bond function, conformational flexibility, hydrogen bonding, electrostatic interactions, and solvent-accessible surface area of Nt and ECLs of this protein part. We found that the extracellular segments of CCR5 formed a well-packet globular domain with complex interactions occurred between them in a majority of time of MID simulation, but Nt region could protrude from this domain sometimes. The disulfide bond Cys20-Cys269 is essential in controlling specific orientation of Nt region and maintaining conformational integrity of extracellular domain. RMS comparison analysis between conformers revealed the ECL1 of CCR5 stays relative rigid, whereas the ECL2 and Nt are rather flexible. Solvent-accessible surface area calculations indicated that the charged residues within Nt and ECL2 are often exposed to solvent. Integrating these results with available experimental data, a two-step gp120-CCR5 binding mechanism was proposed. The dynamic interaction of CCR5 extracellular domain with gp120 was emphasized. (C) 2004 Elsevier B.V. All rights reserved.

Regenerative polymeric bus architecture for board-level optical interconnects

A scalable multi-channel optical regenerative bus architecture based on the use of polymer waveguides is presented for the first time. The architecture offers high-speed interconnection between electrical cards allowing regenerative bus extension with multiple segments and therefore connection of an arbitrary number of cards onto the bus. In a proof-ofprinciple demonstration, a 4-channel 3-card polymeric bus module is designed and fabricated on standard FR4 substrates. Low insertion losses (≤ -15 dB) and low crosstalk values (< -30 dB) are achieved for the fabricated samples while better than ± 6 μm -1 dB alignment tolerances are obtained. 10 Gb/s data communication with a bit-error-rate (BER) lower than 10-12 is demonstrated for the first time between card interfaces on two different bus modules using a prototype 3R regenerator. © 2012 Optical Society of America.

A 40 Gb/s scalable optical polymer backplane using a regenerative shared bus architecture

The first multi-channel optical backplane demonstrator using on-board multimode polymer waveguides and a scalable shared-bus regenerative architecture is reported. The system allows bus extension by cascading multiple polymeric bus modules, and enables error-free 4×10 Gb/s interconnection between any two card interfaces on the bus.