Quantum Electromechanics with Two Tone Drive

In the field of mechanics, it is a long standing goal to measure quantum behavior in ever larger and more massive objects. It may now seem like an obvious conclusion, but until recently it was not clear whether a macroscopic mechanical resonator -- built up from nearly 10¹³ atoms -- could be fully described as an ideal quantum harmonic oscillator. With recent advances in the fields of opto- and electro-mechanics, such systems offer a unique advantage in probing the quantum noise properties of macroscopic electrical and mechanical devices, properties that ultimately stem from Heisenberg's uncertainty relations. Given the rapid progress in device capabilities, landmark results of quantum optics are now being extended into the regime of macroscopic mechanics.

The purpose of this dissertation is to describe three experiments -- motional sideband asymmetry, back-action evasion (BAE) detection, and mechanical squeezing -- that are directly related to the topic of measuring quantum noise with mechanical detection. These measurements all share three pertinent features: they explore quantum noise properties in a macroscopic electromechanical device driven by a minimum of two microwave drive tones, hence the title of this work: "Quantum electromechanics with two tone drive".

In the following, we will first introduce a quantum input-output framework that we use to model the electromechanical interaction and capture subtleties related to interpreting different microwave noise detection techniques. Next, we will discuss the fabrication and measurement details that we use to cool and probe these devices with coherent and incoherent microwave drive signals. Having developed our tools for signal modeling and detection, we explore the three-wave mixing interaction between the microwave and mechanical modes, whereby mechanical motion generates motional sidebands corresponding to up-down frequency conversions of microwave photons. Because of quantum vacuum noise, the rates of these processes are expected to be unequal. We will discuss the measurement and interpretation of this asymmetric motional noise in a electromechanical device cooled near the ground state of motion.

Next, we consider an overlapped two tone pump configuration that produces a time-modulated electromechanical interaction. By careful control of this drive field, we report a quantum non-demolition (QND) measurement of a single motional quadrature. Incorporating a second pair of drive tones, we directly measure the measurement back-action associated with both classical and quantum noise of the microwave cavity. Lastly, we slightly modify our drive scheme to generate quantum squeezing in a macroscopic mechanical resonator. Here, we will focus on data analysis techniques that we use to estimate the quadrature occupations. We incorporate Bayesian spectrum fitting and parameter estimation that serve as powerful tools for incorporating many known sources of measurement and fit error that are unavoidable in such work.

A Continuum Model for Slip-Twinning Interactions in Magnesium and Magnesium Alloys

Due to their high specific strength and low density, magnesium and magnesium-based alloys have gained great technological importance in recent years. However, their underlying hexagonal crystal structure furnishes Mg and its alloys with a complex mechanical behavior because of their comparably smaller number of energetically favorable slip systems. Besides the commonly studied slip mechanism, another way to accomplish general deformation is through the additional mechanism of deformation-induced twinning. The main aim of this thesis research is to develop an efficient continuum model to understand and ultimately predict the material response resulting from the interaction between these two mechanisms.

The constitutive model we present is based on variational constitutive updates of plastic slips and twin volume fractions and accounts for the related lattice reorientation mechanisms. The model is applied to single- and polycrystalline pure magnesium. We outline the finite-deformation plasticity model combining basal, pyramidal, and prismatic dislocation activity as well as a convexification based approach for deformation twinning. A comparison with experimental data from single-crystal tension-compression experiments validates the model and serves for parameter identification. The extension to polycrystals via both Taylor-type modeling and finite element simulations shows a characteristic stress-strain response that agrees well with experimental observations for polycrystalline magnesium. The presented continuum model does not aim to represent the full details of individual twin-dislocation interactions, yet it is sufficiently efficient to allow for finite element simulations while qualitatively capturing the underlying microstructural deformation mechanisms.

Mechanism initiated by nanoabsorber for UV nanosecond-pulse-driven damage of dielectric coatings

A model of plasma formation induced by UV nanosecond pulselaser interaction with SiO2 thin film based on nanoabsorber is proposed. The model considers the temperature dependence of band gap. The numerical results show that during the process of nanosecond pulsed-laser interaction with SiO2 thin film, foreign inclusion which absorbs a fraction of incident radiation heats the surrounding host material through heat conduction causing the decrease of the band gap and consequently, the transformation of the initial transparent matrix into an absorptive medium around the inclusion, thus facilitates optical damage. Qualitative comparison with experiments is also provided. (C) 2008 Optical Society of America.

Defining plausible migration rates based on historical tagging data: a Bayesian mark-recapture model applied to English sole (Parophrys vetulus)

A generalized Bayesian population dynamics model was developed for analysis of historical mark-recapture studies. The Bayesian approach builds upon existing maximum likelihood methods and is useful when substantial uncertainties exist in the data or little information is available about auxiliary parameters such as tag loss and reporting rates. Movement rates are obtained through Markov-chain Monte-Carlo (MCMC) simulation, which are suitable for use as input in subsequent stock assessment analysis. The mark-recapture model was applied to English sole (Parophrys vetulus) off the west coast of the United States and Canada and migration rates were estimated to be 2% per month to the north and 4% per month to the south. These posterior parameter distributions and the Bayesian framework for comparing hypotheses can guide fishery scientists in structuring the spatial and temporal complexity of future analyses of this kind. This approach could be easily generalized for application to other species and more data-rich fishery analyses.

Incorporating fishery observer data into an integrated catch-at-age and multiyear tagging model for estimating mortality rates and abundance

Tagging experiments are a useful tool in fisheries for estimating mortality rates and abundance of fish. Unfortunately, nonreporting of recovered tags is a common problem in commercial fisheries which, if unaccounted for, can render these estimates meaningless. Observers are often employed to monitor a portion of the catches as a means of estimating reporting rates. In our study, observer data were incorporated into an integrated model for multiyear tagging and catch data to provide joint estimates of mortality rates (natural and f ishing), abundance, and reporting rates. Simulations were used to explore model performance under a range of scenarios (e.g., different parameter values, parameter constraints, and numbers of release and recapture years). Overall, results indicated that all parameters can be estimated with reasonable accuracy, but that fishing mortality, reporting rates, and abundance can be estimated with much higher precision than natural mortality. An example of how the model can be applied to provide guidance on experimental design for a large-scale tagging study is presented. Such guidance can contribute to the successful and cost-effective management of tagging programs for commercial fisheries.

Food conversion efficiency and the von Bertalanffy growth function 1: a modification of Pauly's model

A simple modification of Pauly's model for relating food conversion efficiency (K sub(1)) and body weight is proposed. The key parameter is an index to how efficiently food can be absorbed; the other parameter is related to the surface-limiting growth, an important component of von Bertalanff's and Pauly's theories of fish growth.

An environmentally based growth model that uses finite difference calculus with maximum likelihood method: its application to the brackish water bivalve Corbicula japonica in Lake Abashiri, Japan

We present a growth analysis model that combines large amounts of environmental data with limited amounts of biological data and apply it to Corbicula japonica. The model uses the maximum-likelihood method with the Akaike information criterion, which provides an objective criterion for model selection. An adequate distribution for describing a single cohort is selected from available probability density functions, which are expressed by location and scale parameters. Daily relative increase rates of the location parameter are expressed by a multivariate logistic function with environmental factors for each day and categorical variables indicating animal ages as independent variables. Daily relative increase rates of the scale parameter are expressed by an equation describing the relationship with the daily relative increase rate of the location parameter. Corbicula japonica grows to a modal shell length of 0.7 mm during the first year in Lake Abashiri. Compared with the attain-able maximum size of about 30 mm, the growth of juveniles is extremely slow because their growth is less susceptible to environmental factors until the second winter. The extremely slow growth in Lake Abashiri could be a geographical genetic variation within C. japonica.

Dynamical behavior of damped driven coupled single electron simple harmonic oscillators

Coherent coupling between a large number of qubits is the goal for scalable approaches to solid state quantum information processing. Prototype systems can be characterized by spectroscopic techniques. Here, we use pulsed-continuous wave microwave spectroscopy to study the behavior of electrons trapped at defects within the gate dielectric of a sol-gel-based high-k silicon MOSFET. Disorder leads to a wide distribution in trap properties, allowing more than 1000 traps to be individually addressed in a single transistor within the accessible frequency domain. Their dynamical behavior is explored by pulsing the microwave excitation over a range of times comparable to the phase coherence time and the lifetime of the electron in the trap. Trap occupancy is limited to a single electron, which can be manipulated by resonant microwave excitation and the resulting change in trap occupancy is detected by the change in the channel current of the transistor. The trap behavior is described by a classical damped driven simple harmonic oscillator model, with the phase coherence, lifetime and coupling strength parameters derived from a continuous wave (CW) measurement only. For pulse times shorter than the phase coherence time, the energy exchange between traps, due to the coupling, strongly modulates the observed drain current change. This effect could be exploited for 2-qubit gate operation. The very large number of resonances observed in this system would allow a complex multi-qubit quantum mechanical circuit to be realized by this mechanism using only a single transistor.

Physiological modelling for improved reliability in silhouette-driven gradient-based hand tracking

We present a gradient-based motion capture system that robustly tracks a human hand, based on abstracted visual information - silhouettes. Despite the ambiguity in the visual data and despite the vulnerability of gradient-based methods in the face of such ambiguity, we minimise problems related to misfit by using a model of the hand's physiology, which is entirely non-visual, subject-invariant, and assumed to be known a priori. By modelling seven distinct aspects of the hand's physiology we derive prior densities which are incorporated into the tracking system within a Bayesian framework. We demonstrate how the posterior is formed, and how our formulation leads to the extraction of the maximum a posteriori estimate using a gradient-based search. Our results demonstrate an enormous improvement in tracking precision and reliability, while also achieving near real-time performance. © 2009 IEEE.

Parameter learning for POMDP spoken dialogue models

The partially observable Markov decision process (POMDP) provides a popular framework for modelling spoken dialogue. This paper describes how the expectation propagation algorithm (EP) can be used to learn the parameters of the POMDP user model. Various special probability factors applicable to this task are presented, which allow the parameters be to learned when the structure of the dialogue is complex. No annotations, neither the true dialogue state nor the true semantics of user utterances, are required. Parameters optimised using the proposed techniques are shown to improve the performance of both offline transcription experiments as well as simulated dialogue management performance. ©2010 IEEE.

An overview of Sequential Monte Carlo methods for parameter estimation in general state-space models

Nonlinear non-Gaussian state-space models arise in numerous applications in control and signal processing. Sequential Monte Carlo (SMC) methods, also known as Particle Filters, provide very good numerical approximations to the associated optimal state estimation problems. However, in many scenarios, the state-space model of interest also depends on unknown static parameters that need to be estimated from the data. In this context, standard SMC methods fail and it is necessary to rely on more sophisticated algorithms. The aim of this paper is to present a comprehensive overview of SMC methods that have been proposed to perform static parameter estimation in general state-space models. We discuss the advantages and limitations of these methods. © 2009 IFAC.

Parameter measurement for heavy-vehicle fuel consumption modelling

A mathematical model is developed to predict the energy consumption of a heavy vehicle. It includes the important factors of heavy-vehicle energy consumption, namely engine and drivetrain performances, losses due to accessories, aerodynamic drag, rolling resistance, road gradients, and driver behaviour. Novel low-cost testing methods were developed to determine engine and drivetrain characteristics. A simple drive cycle was used to validate the model. The model is able to predict the fuel use for a 371 tractor-semitrailer vehicle over a 4 km drive cycle within 1 per cent. This paper demonstrates that accurate and reliable vehicle benchmarking and model parameter measurement can be achieved without expensive equipment overheads, e.g. engine and chassis dynamometers.

Baroclinic waves in an air-filled thermally driven rotating annulus.

In this study an experimental investigation of baroclinic waves in air in a differentially heated rotating annulus is presented. Air has a Prandtl number of 0.707, which falls within a previously unexplored region of parameter space for baroclinic instability. The flow regimes encountered include steady waves, periodic amplitude vacillations, modulated amplitude vacillations, and either monochromatic or mixed wave number weak waves, the latter being characterized by having amplitudes less than 5% of the applied temperature contrast. The distribution of these flow regimes in parameter space are presented in a regime diagram. It was found that the progression of transitions between different regimes is, as predicted by recent numerical modeling results, in the opposite sense to that usually found in experiments with high Prandtl number liquids. No hysteresis in the flow type, with respect to variations in the rotation rate, was found in this investigation.

Reinforcement learning for parameter estimation in statistical spoken dialogue systems

Reinforcement techniques have been successfully used to maximise the expected cumulative reward of statistical dialogue systems. Typically, reinforcement learning is used to estimate the parameters of a dialogue policy which selects the system's responses based on the inferred dialogue state. However, the inference of the dialogue state itself depends on a dialogue model which describes the expected behaviour of a user when interacting with the system. Ideally the parameters of this dialogue model should be also optimised to maximise the expected cumulative reward. This article presents two novel reinforcement algorithms for learning the parameters of a dialogue model. First, the Natural Belief Critic algorithm is designed to optimise the model parameters while the policy is kept fixed. This algorithm is suitable, for example, in systems using a handcrafted policy, perhaps prescribed by other design considerations. Second, the Natural Actor and Belief Critic algorithm jointly optimises both the model and the policy parameters. The algorithms are evaluated on a statistical dialogue system modelled as a Partially Observable Markov Decision Process in a tourist information domain. The evaluation is performed with a user simulator and with real users. The experiments indicate that model parameters estimated to maximise the expected reward function provide improved performance compared to the baseline handcrafted parameters. © 2011 Elsevier Ltd. All rights reserved.

The mechanical integrity of fuel pin cladding in a pulsed-beam accelerator driven subcritical reactor

The Accelerator Driven Subcritical Reactor (ADSR) is one of the reactor designs proposed for future nuclear energy production. Interest in the ADSR arises from its enhanced and intrinsic safety characteristics, as well as its potential ability to utilize the large global reserves of thorium and to burn legacy actinide waste from other reactors and decommissioned nuclear weapons. The ADSR concept is based on the coupling of a particle accelerator and a subcritical core by means of a neutron spallation target interface. One of the candidate accelerator technologies receiving increasing attention, the Fixed Field Alternating Gradient (FFAG) accelerator, generates a pulsed proton beam. This paper investigates the impact of pulsed proton beam operation on the mechanical integrity of the fuel pin cladding. A pulsed beam induces repetitive temperature changes in the reactor core which lead to cyclic thermal stresses in the cladding. To perform the thermal analysis aspects of this study a code that couples the neutron kinetics of a subcritical core to a cylindrical geometry heat transfer model was developed. This code, named PTS-ADS, enables temperature variations in the cladding to be calculated. These results are then used to perform thermal fatigue analysis and to predict the stress-life behaviour of the cladding. © 2011 Elsevier Ltd. All rights reserved.