A mass spectrometry study of XCO+, X = Si, Ge : Is SiCO+ a main group carbonyl? Comments on the bonding in ground state SiCO and the Si,C,O (+) potential energy surface

The cation\[Si,C,O](+) has been generated by 1) the electron ionisation (EI) of tetramethoxysilane and 2) chemical ionisation (CI) of a mixture of silane and carbon monoxide. Collisional activation (CA) experiments performed for mass-selected \[Si,C,O](+), generated by using both methods, indicate that the structure is not inserted OSiC+; however, a definitive structural assignment as Si+-CO, Si+-OC or some cyclic variant is impossible based on these results alone. Neutralisation-reionisation (+NR+) experiments for EI-generated \[Si,C,O](+) reveal a small peak corresponding to SiC+, but no detectable SiO+ signal, and thus establishes the existence of the Si+-CO isomer. CCSD(T)//B3LYP calculations employing a triple-zeta basis set have been used to explore the doublet and quartet potential-energy surfaces of the cation, as well as some important neutral states The results suggest that both Si+-CO and Si+ - OC isomers are feasible; however, the global minimum is (2)Pi SiCO+. Isomeric (2)Pi SiOC+ is 12.1 kcal mol(-1) less stable than (2)Pi SiCO+, and all quartet isomers are much higher in energy. The corresponding neutrals Si-CO and Si-OC are also feasible, but the lowest energy Si - OC isomer ((3)A") is bound by only 1.5 kcal mol(-1). We attribute most, if nor all, of the recovery signal in the +NR' experiment to SiCO+ survivor ions. The nature of the bonding in the lowest energy isomers of Si+ -(CO,OC) is interpreted with the aid of natural bond order analyses, and the ground stale bonding of SiCO+ is discussed in relation to classical analogues such as metal carbonyls and ketenes.

Vertical displacement measurement using fibre Bragg grating (FBG) sensors for structural health monitoring of bridges

This thesis developed a practical, cost effective, easy-to-use method for measuring the vertical displacements of bridges using fiber Bragg grating (FBG) sensors, which includes the curvature and inclination approaches. These approaches were validated by the numerical simulation tests on a full scale bridge and the laboratory-based tests. In doing so, a novel frictionless FBG inclination sensor with extremely high sensitivity and resolution has also been developed and validated.

Algebraic attacks on SOBER-t32 and SOBER-t16 without stuttering

This paper presents algebraic attacks on SOBER-t32 and SOBER-t16 without stuttering. For unstuttered SOBER-t32, two different attacks are implemented. In the first attack, we obtain multivariate equations of degree 10. Then, an algebraic attack is developed using a collection of output bits whose relation to the initial state of the LFSR can be described by low-degree equations. The resulting system of equations contains 2^69 equations and monomials, which can be solved using the Gaussian elimination with the complexity of 2^196.5. For the second attack, we build a multivariate equation of degree 14. We focus on the property of the equation that the monomials which are combined with output bit are linear. By applying the Berlekamp-Massey algorithm, we can obtain a system of linear equations and the initial states of the LFSR can be recovered. The complexity of attack is around O(2^100) with 2^92 keystream observations. The second algebraic attack is applicable to SOBER-t16 without stuttering. The attack takes around O(2^85) CPU clocks with 2^78 keystream observations.

Structure- and composition-dependent electron field emission from nitrogenated carbon nanotips

The electron field emission (EFE) properties of nitrogenated carbon nanotips (NCNTPs) were studied under high-vacuum conditions. The NCNTPs were prepared in a plasma-assisted hot filament chemical vapor deposition system using CH4 and N2 as the carbon and nitrogen sources, respectively. The work functions of NCNTPs were measured using x-ray photoelectron spectroscopy. The morphological and structural properties of NCNTPs were studied by field emission scanning electron microscopy, micro-Raman spectroscopy, and x-ray photoelectron spectroscopy. The field enhancement factors of NCNTPs were calculated using relevant EFE models based on the Fowler-Nordheim approximation. Analytical characterization and modeling results were used to establish the relations between the EFE properties of NCNTPs and their morphology, structure, and composition. It is shown that the EFE properties of NCNTPs can be enhanced by the reduction of oxygen termination on the surface as well as by increasing the ratio of the NCNTP height to the radius of curvature at its top. These results also suggest that a significant amount of electrons is emitted from other surface areas besides the NCNTP tops, contrary to the common belief. The outcomes of this study advance our knowledge on the electron emission properties of carbonnanomaterials and contribute to the development of the next-generation of advanced applications in the fields of micro- and opto-electronics.

Computational model of membrane fission catalyzed by ESCRT-III

ESCRT-III proteins catalyze membrane fission during multi vesicular body biogenesis, budding of some enveloped viruses and cell division. We suggest and analyze a novel mechanism of membrane fission by the mammalian ESCRT-III subunits CHMP2 and CHMP3. We propose that the CHMP2-CHMP3 complexes self-assemble into hemi-spherical dome-like structures within the necks of the initial membrane buds generated by CHMP4 filaments. The dome formation is accompanied by the membrane attachment to the dome surface, which drives narrowing of the membrane neck and accumulation of the elastic stresses leading, ultimately, to the neck fission. Based on the bending elastic model of lipid bilayers, we determine the degree of the membrane attachment to the dome enabling the neck fission and compute the required values of the protein-membrane binding energy. We estimate the feasible values of this energy and predict a high efficiency for the CHMP2-CHMP3 complexes in mediating membrane fission. We support the computational model by electron tomography imaging of CHMP2-CHMP3 assemblies in vitro. We predict a high efficiency for the CHMP2-CHMP3 complexes in mediating membrane fission.

Structure and assembly of immature HIV

The major structural components of HIV are synthesized as a 55-kDa polyprotein, Gag. Particle formation is driven by the self-assembly of Gag into a curved hexameric lattice, the structure of which is poorly understood. We used cryoelectron tomography and contrast-transfer-function corrected subtomogram averaging to study the structure of the assembled immature Gag lattice to approximate to 17-angstrom resolution. Gag is arranged in the immature virus as a single, continuous, but incomplete hexameric lattice whose curvature is mediated without a requirement for pentameric defects. The resolution of the structure allows positioning of individual protein domains. High-resolution crystal structures were fitted into the reconstruction to locate protein-protein interfaces involved in Gag assembly, and to identify the structural transformations associated with virus maturation. The results of this study suggest a concept for the formation of nonsymmetrical enveloped viruses of variable sizes.

Short utterance PLDA speaker verification using SN-WLDA and variance modelling techniques

This paper proposes a combination of source-normalized weighted linear discriminant analysis (SN-WLDA) and short utterance variance (SUV) PLDA modelling to improve the short utterance PLDA speaker verification. As short-length utterance i-vectors vary with the speaker, session variations and phonetic content of the utterance (utterance variation), a combined approach of SN-WLDA projection and SUV PLDA modelling is used to compensate the session and utterance variations. Experimental studies have found that a combination of SN-WLDA and SUV PLDA modelling approach shows an improvement over baseline system (WCCN[LDA]-projected Gaussian PLDA (GPLDA)) as this approach effectively compensates the session and utterance variations.

Design of a wind turbine - battery energy storage scheme to achieve power dispatchability

Battery energy storage system (BESS) is to be incorporated in a wind farm to achieve constant power dispatch. The design of the BESS is based on the forecasted wind speed, and the technique assumes the distribution of the error between the forecasted and actual wind speeds is Gaussian. It is then shown that although the error between the predicted and actual wind powers can be evaluated, it is non-Gaussian. With the known distribution in the error of the predicted wind power, the capacity of the BESS can be determined in terms of the confident level in meeting specified constant power dispatch commitment. Furthermore, a short-term power dispatch strategy is also developed which takes into account the state of charge (SOC) of the BESS. The proposed approach is useful in the planning of the wind farm-BESS scheme and in the operational planning of the wind power generating station.

Bayesian models for spatio-temporal assessment of disease

This thesis has contributed to the advancement of knowledge in disease modelling by addressing interesting and crucial issues relevant to modelling health data over space and time. The research has led to the increased understanding of spatial scales, temporal scales, and spatial smoothing for modelling diseases, in terms of their methodology and applications. This research is of particular significance to researchers seeking to employ statistical modelling techniques over space and time in various disciplines. A broad class of statistical models are employed to assess what impact of spatial and temporal scales have on simulated and real data.

Bayesian hierarchical models for analysing spatial point-based data at a grid level: A comparison of approaches

Spatial data are now prevalent in a wide range of fields including environmental and health science. This has led to the development of a range of approaches for analysing patterns in these data. In this paper, we compare several Bayesian hierarchical models for analysing point-based data based on the discretization of the study region, resulting in grid-based spatial data. The approaches considered include two parametric models and a semiparametric model. We highlight the methodology and computation for each approach. Two simulation studies are undertaken to compare the performance of these models for various structures of simulated point-based data which resemble environmental data. A case study of a real dataset is also conducted to demonstrate a practical application of the modelling approaches. Goodness-of-fit statistics are computed to compare estimates of the intensity functions. The deviance information criterion is also considered as an alternative model evaluation criterion. The results suggest that the adaptive Gaussian Markov random field model performs well for highly sparse point-based data where there are large variations or clustering across the space; whereas the discretized log Gaussian Cox process produces good fit in dense and clustered point-based data. One should generally consider the nature and structure of the point-based data in order to choose the appropriate method in modelling a discretized spatial point-based data.

An evaluation of crowd counting methods, features and regression models

Existing crowd counting algorithms rely on holistic, local or histogram based features to capture crowd properties. Regression is then employed to estimate the crowd size. Insufficient testing across multiple datasets has made it difficult to compare and contrast different methodologies. This paper presents an evaluation across multiple datasets to compare holistic, local and histogram based methods, and to compare various image features and regression models. A K-fold cross validation protocol is followed to evaluate the performance across five public datasets: UCSD, PETS 2009, Fudan, Mall and Grand Central datasets. Image features are categorised into five types: size, shape, edges, keypoints and textures. The regression models evaluated are: Gaussian process regression (GPR), linear regression, K nearest neighbours (KNN) and neural networks (NN). The results demonstrate that local features outperform equivalent holistic and histogram based features; optimal performance is observed using all image features except for textures; and that GPR outperforms linear, KNN and NN regression

Data fusion and tracking with multiple UAVs

This chapter describes decentralized data fusion algorithms for a team of multiple autonomous platforms. Decentralized data fusion (DDF) provides a useful basis with which to build upon for cooperative information gathering tasks for robotic teams operating in outdoor environments. Through the DDF algorithms, each platform can maintain a consistent global solution from which decisions may then be made. Comparisons will be made between the implementation of DDF using two probabilistic representations. The first, Gaussian estimates and the second Gaussian mixtures are compared using a common data set. The overall system design is detailed, providing insight into the overall complexity of implementing a robust DDF system for use in information gathering tasks in outdoor UAV applications.

Learned stochastic mobility prediction for planning with control uncertainty on unstructured terrain

Motion planning for planetary rovers must consider control uncertainty in order to maintain the safety of the platform during navigation. Modelling such control uncertainty is difficult due to the complex interaction between the platform and its environment. In this paper, we propose a motion planning approach whereby the outcome of control actions is learned from experience and represented statistically using a Gaussian process regression model. This mobility prediction model is trained using sample executions of motion primitives on representative terrain, and predicts the future outcome of control actions on similar terrain. Using Gaussian process regression allows us to exploit its inherent measure of prediction uncertainty in planning. We integrate mobility prediction into a Markov decision process framework and use dynamic programming to construct a control policy for navigation to a goal region in a terrain map built using an on-board depth sensor. We consider both rigid terrain, consisting of uneven ground, small rocks, and non-traversable rocks, and also deformable terrain. We introduce two methods for training the mobility prediction model from either proprioceptive or exteroceptive observations, and report results from nearly 300 experimental trials using a planetary rover platform in a Mars-analogue environment. Our results validate the approach and demonstrate the value of planning under uncertainty for safe and reliable navigation.

A pseudo-marginal sequential Monte Carlo algorithm for random effects models in Bayesian sequential design

A computationally efficient sequential Monte Carlo algorithm is proposed for the sequential design of experiments for the collection of block data described by mixed effects models. The difficulty in applying a sequential Monte Carlo algorithm in such settings is the need to evaluate the observed data likelihood, which is typically intractable for all but linear Gaussian models. To overcome this difficulty, we propose to unbiasedly estimate the likelihood, and perform inference and make decisions based on an exact-approximate algorithm. Two estimates are proposed: using Quasi Monte Carlo methods and using the Laplace approximation with importance sampling. Both of these approaches can be computationally expensive, so we propose exploiting parallel computational architectures to ensure designs can be derived in a timely manner. We also extend our approach to allow for model uncertainty. This research is motivated by important pharmacological studies related to the treatment of critically ill patients.

Including nonequilibrium interface kinetics in a continuum model for melting nanoscaled particles

The melting temperature of a nanoscaled particle is known to decrease as the curvature of the solid-melt interface increases. This relationship is most often modelled by a Gibbs--Thomson law, with the decrease in melting temperature proposed to be a product of the curvature of the solid-melt interface and the surface tension. Such a law must break down for sufficiently small particles, since the curvature becomes singular in the limit that the particle radius vanishes. Furthermore, the use of this law as a boundary condition for a Stefan-type continuum model is problematic because it leads to a physically unrealistic form of mathematical blow-up at a finite particle radius. By numerical simulation, we show that the inclusion of nonequilibrium interface kinetics in the Gibbs--Thomson law regularises the continuum model, so that the mathematical blow up is suppressed. As a result, the solution continues until complete melting, and the corresponding melting temperature remains finite for all time. The results of the adjusted model are consistent with experimental findings of abrupt melting of nanoscaled particles. This small-particle regime appears to be closely related to the problem of melting a superheated particle.