Analysis of Cone-Beam CT using prior information

Treatment plans for conformal radiotherapy are based on an initial CT scan. The aim is to deliver the prescribed dose to the tumour, while minimising exposure to nearby organs. Recent advances make it possible to also obtain a Cone-Beam CT (CBCT) scan, once the patient has been positioned for treatment. A statistical model will be developed to compare these CBCT scans with the initial CT scan. Changes in the size, shape and position of the tumour and organs will be detected and quantified. Some progress has already been made in segmentation of prostate CBCT scans [1],[2],[3]. However, none of the existing approaches have taken full advantage of the prior information that is available. The planning CT scan is expertly annotated with contours of the tumour and nearby sensitive objects. This data is specific to the individual patient and can be viewed as a snapshot of spatial information at a point in time. There is an abundance of studies in the radiotherapy literature that describe the amount of variation in the relevant organs between treatments. The findings from these studies can form a basis for estimating the degree of uncertainty. All of this information can be incorporated as an informative prior into a Bayesian statistical model. This model will be developed using scans of CT phantoms, which are objects with known geometry. Thus, the accuracy of the model can be evaluated objectively. This will also enable comparison between alternative models.

Object tracking via non-Euclidean geometry : a Grassmann approach

A robust visual tracking system requires an object appearance model that is able to handle occlusion, pose, and illumination variations in the video stream. This can be difficult to accomplish when the model is trained using only a single image. In this paper, we first propose a tracking approach based on affine subspaces (constructed from several images) which are able to accommodate the abovementioned variations. We use affine subspaces not only to represent the object, but also the candidate areas that the object may occupy. We furthermore propose a novel approach to measure affine subspace-to-subspace distance via the use of non-Euclidean geometry of Grassmann manifolds. The tracking problem is then considered as an inference task in a Markov Chain Monte Carlo framework via particle filtering. Quantitative evaluation on challenging video sequences indicates that the proposed approach obtains considerably better performance than several recent state-of-the-art methods such as Tracking-Learning-Detection and MILtrack.

Changing resonator geometry to boost sound power decouples size and song frequency in a small insect

Despite their small size, some insects, such as crickets, can produce high amplitude mating songs by rubbing their wings together. By exploiting structural resonance for sound radiation, crickets broadcast species-specific songs at a sharply tuned frequency. Such songs enhance the range of signal transmission, contain information about the signaler's quality, and allow mate choice. The production of pure tones requires elaborate structural mechanisms that control and sustain resonance at the species-specific frequency. Tree crickets differ sharply from this scheme. Although they use a resonant system to produce sound, tree crickets can produce high amplitude songs at different frequencies, varying by as much as an octave. Based on an investigation of the driving mechanism and the resonant system, using laser Doppler vibrometry and finite element modeling, we show that it is the distinctive geometry of the crickets' forewings (the resonant system) that is responsible for their capacity to vary frequency. The long, enlarged wings enable the production of high amplitude songs; however, as a mechanical consequence of the high aspect ratio, the resonant structures have multiple resonant modes that are similar in frequency. The drive produced by the singing apparatus cannot, therefore, be locked to a single frequency, and different resonant modes can easily be engaged, allowing individual males to vary the carrier frequency of their songs. Such flexibility in sound production, decoupling body size and song frequency, has important implications for conventional views of mate choice, and offers inspiration for the design of miniature, multifrequency, resonant acoustic radiators.

Constraining complex aquifer geometry with geophysics (2-D ERT and MRS measurements) for stochastic modelling of groundwater flow

Stochastic modelling is a useful way of simulating complex hard-rock aquifers as hydrological properties (permeability, porosity etc.) can be described using random variables with known statistics. However, very few studies have assessed the influence of topological uncertainty (i.e. the variability of thickness of conductive zones in the aquifer), probably because it is not easy to retrieve accurate statistics of the aquifer geometry, especially in hard rock context. In this paper, we assessed the potential of using geophysical surveys to describe the geometry of a hard rock-aquifer in a stochastic modelling framework. The study site was a small experimental watershed in South India, where the aquifer consisted of a clayey to loamy-sandy zone (regolith) underlain by a conductive fissured rock layer (protolith) and the unweathered gneiss (bedrock) at the bottom. The spatial variability of the thickness of the regolith and fissured layers was estimated by electrical resistivity tomography (ERT) profiles, which were performed along a few cross sections in the watershed. For stochastic analysis using Monte Carlo simulation, the generated random layer thickness was made conditional to the available data from the geophysics. In order to simulate steady state flow in the irregular domain with variable geometry, we used an isoparametric finite element method to discretize the flow equation over an unstructured grid with irregular hexahedral elements. The results indicated that the spatial variability of the layer thickness had a significant effect on reducing the simulated effective steady seepage flux and that using the conditional simulations reduced the uncertainty of the simulated seepage flux. As a conclusion, combining information on the aquifer geometry obtained from geophysical surveys with stochastic modelling is a promising methodology to improve the simulation of groundwater flow in complex hard-rock aquifers. (C) 2013 Elsevier B.V. All rights reserved.

Fast numerical methods for mixed, singular Helmholtz boundary value problems and Laplace eigenvalue problems - with applications to antenna design, sloshing, electromagnetic scattering and spectral geometry

This thesis presents a novel class of algorithms for the solution of scattering and eigenvalue problems on general two-dimensional domains under a variety of boundary conditions, including non-smooth domains and certain "Zaremba" boundary conditions - for which Dirichlet and Neumann conditions are specified on various portions of the domain boundary. The theoretical basis of the methods for the Zaremba problems on smooth domains concern detailed information, which is put forth for the first time in this thesis, about the singularity structure of solutions of the Laplace operator under boundary conditions of Zaremba type. The new methods, which are based on use of Green functions and integral equations, incorporate a number of algorithmic innovations, including a fast and robust eigenvalue-search algorithm, use of the Fourier Continuation method for regularization of all smooth-domain Zaremba singularities, and newly derived quadrature rules which give rise to high-order convergence even around singular points for the Zaremba problem. The resulting algorithms enjoy high-order convergence, and they can tackle a variety of elliptic problems under general boundary conditions, including, for example, eigenvalue problems, scattering problems, and, in particular, eigenfunction expansion for time-domain problems in non-separable physical domains with mixed boundary conditions.

High dimensional imagery geometry and it's applications

Because of information digitalization and the correspondence of digits and the coordinates, Information Science and high-dimensional space have consanguineous relations. With the transforming from the information issues to the point analysis in high-dimensional space, we proposed a novel computational theory, named High dimensional imagery geometry (HDIG). Some computational algorithms of HDIG have been realized using software, and how to combine with groups of simple operators in some 2D planes to implement the geometrical computations in high-dimensional space is demonstrated in this paper. As the applications, two kinds of experiments of HDIG, which are blurred image restoration and pattern recognition ones, are given, and the results are satisfying.

Epipolar geometry estimation based on evolutionary agents

This paper presents a novel approach based on the use of evolutionary agents for epipolar geometry estimation. In contrast to conventional nonlinear optimization methods, the proposed technique employs each agent to denote a minimal subset to compute the fundamental matrix, and considers the data set of correspondences as a 1D cellular environment, in which the agents inhabit and evolve. The agents execute some evolutionary behavior, and evolve autonomously in a vast solution space to reach the optimal (or near optima) result. Then three different techniques are proposed in order to improve the searching ability and computational efficiency of the original agents. Subset template enables agents to collaborate more efficiently with each other, and inherit accurate information from the whole agent set. Competitive evolutionary agent (CEA) and finite multiple evolutionary agent (FMEA) apply a better evolutionary strategy or decision rule, and focus on different aspects of the evolutionary process. Experimental results with both synthetic data and real images show that the proposed agent-based approaches perform better than other typical methods in terms of accuracy and speed, and are more robust to noise and outliers.

An Experimental Assessment of the Effects of Stator Vane Tip Clearance and Back Swept Blading on an Automotive Variable Geometry Turbocharger

Off-design performance is of key importance now in the design of automotive turbocharger turbines. Due to automotive drive cycles, a turbine that can extract more energy at high pressure ratios and lower rotational speeds is desirable. Typically a radial turbine provides peak efficiency at U/C values of 0.7, but at high pressure ratios and low rotational speeds, the U/C value will be low and the rotor will experience high values of positive incidence at the inlet. The positive incidence causes high blade loading resulting in additional tip leakage flow in the rotor as well as flow separation on the suction surface of the blade. An experimental assessment has been performed on a scaled automotive VGS (variable geometry system). Three different stator vane positions have been analyzed: minimum, 25%, and maximum flow position. The first tests were to establish whether positioning the endwall clearance on the hub or shroud side of the stator vanes produced a different impact on turbine efficiency. Following this, a back swept rotor was tested to establish the potential gains to be achieved during off-design operation. A single passage CFD model of the test rig was developed and used to provide information on the flow features affecting performance in both the stator vanes and turbine. It was seen that off-design performance was improved by implementing clearance on the hub side of the stator vanes rather than on the shroud side. Through CFD analysis and tests, it was seen that two leakage vortices form, one at the leading edge and one after the spindle of the stator vane. The vortices affect the flow angle at the inlet to the rotor, in the hub region. The flow angle is shifted to more negative values of incidence, which is beneficial at the off-design conditions but detrimental at the design point. The back swept rotor was tested with the hub side stator vane clearance configuration. The efficiency and MFR were increased at the minimum and 25% stator vane position. At the design point, the efficiency and MFR were decreased. The CFD investigation showed that the incidence angle was improved at the off-design conditions for the back swept rotor. This reduction in the positive incidence angle, along with the improvement caused by the stator vane tip leakage flow, reduced flow separation on the suction surface of the rotor. At the design point, both the tip leakage flow of the stator vanes and the back swept blade angle caused flow separation on the pressure surface of the rotor. This resulted in additional blockage at the throat of the rotor reducing MFR and efficiency.

Influence of test methodology and probe geometry on nanoscale fatigue mechanisms of diamond-like carbon thin film

The aim of this paper is to investigate the mechanism of nanoscale fatigue using nano-impact and multiple-loading cycle nanoindentation tests, and compare it to previously reported findings of nanoscale fatigue using integrated stiffness and depth sensing approach. Two different film loading mechanism, loading history and indenter shapes are compared to comprehend the influence of test methodology on the nanoscale fatigue failure mechanisms of DLC film. An amorphous 100 nm thick DLC film was deposited on a 500 μm silicon substrate using sputtering of graphite target in pure argon atmosphere. Nano-impact and multiple-load cycle indentations were performed in the load range of 100 μN to 1000 μN and 0.1 mN to 100 mN, respectively. Both test types were conducted using conical and Berkovich indenters. Results indicate that for the case of conical indenter, the combination of nano-impact and multiple-loading cycle nanoindentation tests provide information on the life and failure mechanism of DLC film, which is comparable to the previously reported findings using the integrated stiffness and depth sensing approach. However, the comparison of results is sensitive to the applied load, loading mechanism, test-type and probe geometry. The loading mechanism and load history is therefore critical which also leads to two different definitions of film failure. The choice of exact test methodology, load and probe geometry should therefore be dictated by the in-service tribological conditions, and where necessary both test methodologies can be used to provide better insights of failure mechanism. Molecular dynamics (MD) simulations of the elastic response of nanoindentation is reported, which indicates that the elastic modulus of the film measured using MD simulation was higher than that experimentally measured. This difference is attributed to the factors related to the presence of material defects, crystal structure, residual stress, indenter geometry and loading/unloading rate differences between the MD and experimental results.

A Numerical and Experimental Assessment of the Use of a Turbine Utilizing Splitter Blades for an Automotive Variable Geometry Turbocharger

Automotive manufacturers require improved part load engine performance to further improve fuel economy. For a swing vane VGS (Variable Geometry Stator) turbine this means a more closed stator vane, to deal with the low MFRs (Mass Flow Rates), high PRs (Pressure Ratios) and low rotor rotational speeds. During these conditions the turbine is operating at low velocity ratios. As more energy is available at high pressure ratios and during lower turbocharger rotational speeds, a turbine which is efficient at these conditions is desirable. Another key aspect for automotive manufacturers is engine responsiveness. High inertia designs result in “turbo lag” which means an increased time before the target boost pressure is reached. Therefore, designs with improved performance at low velocity ratios, reduced inertia or an increased swallowing capacity are the current targets for turbocharger manufacturers.

To try to meet these design targets a CFD (Computational Fluid Dynamics) study was performed on a turbine wheel using splitter blades. A number of parameters were investigated. These included splitter blade merdional length, blade number and blade angle distribution.

The numerical study was performed on a scaled automotive VGS. Three different stator vane positions have been analysed. A single passage CFD model was developed and used to provide information on the flow features affecting performance in both the stator vanes and turbine.

Following the CFD investigation the design with the best compromise in terms of performance, inertia and increased MFP (Mass Flow Parameter) was selected for manufacture and testing. Tests were performed on a scaled, low temperature turbine test rig. The aerodynamic flow path of the gas stand was the same as that investigated during the CFD. The test results revealed a design which had similar performance at the closed stator vane positions when compared to the baseline wheel. At the maximum MFR stator vane condition a drop of −0.6% pts in efficiency was seen. However, 5.5% increase in MFP was obtained with the additional benefit of a drop in rotor inertia of 3.7%, compared to the baseline wheel.

The Rossiter-McLaughlin effect reloaded: Probing the 3D spin-orbit geometry, differential stellar rotation, and the spatially-resolved stellar spectrum of star-planet systems

When a planet transits its host star, it blocks regions of the stellar surface from view; this causes a distortion of the spectral lines and a change in the line-of-sight (LOS) velocities, known as the Rossiter-McLaughlin (RM) effect. Since the LOS velocities depend, in part, on the stellar rotation, the RM waveform is sensitive to the star-planet alignment (which provides information on the system’s dynamical history). We present a new RM modelling technique that directly measures the spatially-resolved stellar spectrum behind the planet. This is done by scaling the continuum flux of the (HARPS) spectra by the transit light curve, and then subtracting the infrom the out-of-transit spectra to isolate the starlight behind the planet. This technique does not assume any shape for the intrinsic local profiles. In it, we also allow for differential stellar rotation and centre-to-limb variations in the convective blueshift. We apply this technique to HD 189733 and compare to 3D magnetohydrodynamic (MHD) simulations. We reject rigid body rotation with high confidence (>99% probability), which allows us to determine the occulted stellar latitudes and measure the stellar inclination. In turn, we determine both the sky-projected (λ ≈ −0.4 ± 0.2◦) and true 3D obliquity (ψ ≈ 7+12 −4 ◦ ). We also find good agreement with the MHD simulations, with no significant centre-to-limb variations detectable in the local profiles. Hence, this technique provides a new powerful tool that can probe stellar photospheres, differential rotation, determine 3D obliquities, and remove sky-projection biases in planet migration theories. This technique can be implemented with existing instrumentation, but will become even more powerful with the next generation of high-precision radial velocity spectrographs.

Object segregation and local gist vision using low-level geometry

Multi-scale representations of lines, edges and keypoints on the basis of simple, complex and end-stopped cells can be used for object categorisation and recognition (Rodrigues and du Buf, 2009 BioSystems 95 206-226). These representations are complemented by saliency maps of colour, texture, disparity and motion information, which also serve to model extremely fast gist vision in parallel with object segregation. We present a low-level geometry model based on a single type of self-adjusting grouping cell, with a circular array of dendrites connected to edge cells located at several angles.

Aitchison Geometry for Probability and Likelihood as a new approach to mathematical statistics

The Aitchison vector space structure for the simplex is generalized to a Hilbert space structure A2(P) for distributions and likelihoods on arbitrary spaces. Central notations of statistics, such as Information or Likelihood, can be identified in the algebraical structure of A2(P) and their corresponding notions in compositional data analysis, such as Aitchison distance or centered log ratio transform. In this way very elaborated aspects of mathematical statistics can be understood easily in the light of a simple vector space structure and of compositional data analysis. E.g. combination of statistical information such as Bayesian updating, combination of likelihood and robust M-estimation functions are simple additions/ perturbations in A2(Pprior). Weighting observations corresponds to a weighted addition of the corresponding evidence. Likelihood based statistics for general exponential families turns out to have a particularly easy interpretation in terms of A2(P). Regular exponential families form finite dimensional linear subspaces of A2(P) and they correspond to finite dimensional subspaces formed by their posterior in the dual information space A2(Pprior). The Aitchison norm can identified with mean Fisher information. The closing constant itself is identified with a generalization of the cummulant function and shown to be Kullback Leiblers directed information. Fisher information is the local geometry of the manifold induced by the A2(P) derivative of the Kullback Leibler information and the space A2(P) can therefore be seen as the tangential geometry of statistical inference at the distribution P. The discussion of A2(P) valued random variables, such as estimation functions or likelihoods, give a further interpretation of Fisher information as the expected squared norm of evidence and a scale free understanding of unbiased reasoning