スパースアレイ・レーダにおける信号処理技術

Tohoku University佐藤源之課

Development of a Grade Control Technique Optimizing Dilution and Ore Loss Trade-off in Lateritic Bauxite Deposits

This thesis focusses on the development of new techniques to improve the resource estimation of laterite-type bauxite deposits. Contributions of the thesis include (1) a methodology to variogram-free modelling of the ore boundaries using multiple-point statistics, (2) an approach to automate the parameter tuning process for multiple-point statistical algorithms and (3) a grade control technique to minimise the economic losses due to dilution and ore loss

Sparse Ground Penetrating Radar Acquisition: Implication for Buried Landmine Localization and Reconstruction

The effectiveness of the ground penetrating radar (GPR) imaging process and its capability of correctly reconstructing buried objects is strictly bounded to a correct acquisition strategy, both in terms of data density and regularity. In some GPR applications, such as landmine detection, these requirements may not be fulfiled due to logistical limitations and environmental obstacles. In the light of autonomous platform, possibly driven by a positioning device, the knowledge of the maximum affordable grid irregularity is essential. This experimental work, employing a data set acquired at a landmine test site, provides a demonstration that the same information content could be maintained even with a sparser data grid, compared to the commonly adopted requirements, mitigating the pressing demand for a precise samples positioning

Modelling function-valued processes with complex structure

PhD ThesisExisting approaches to functional principal component analysis (FPCA) usually rely on nonparametric estimation of the covariance structure. When function-valued processes are observed on a multidimensional domain, the nonparametric estimation suffers from the curse of dimensionality, forcing FPCA methods to make restrictive assumptions such as covariance separability. In this thesis, we discuss a general Bayesian framework on modelling function-valued processes by using a Gaussian process (GP) as a prior, enabling us to handle nonseparable and/or nonstationary covariance structure. The nonstationarity is introduced by a convolution-based approach through a varying kernel, whose parameters vary along the input space and are estimated via a local empirical Bayesian method. For the varying anisotropy matrix, we propose to use a spherical parametrisation, leading to unconstrained and interpretable parameters and allowing for interaction between coordinate directions in the covariance function. The unconstrained nature allows the parameters to be modelled as a nonparametric function of time, spatial location and even additional covariates. In the spirit of FPCA, the Bayesian framework can decompose the function-valued processes using the eigenvalues and eigensurfaces calculated from the estimated covariance structure. A finite number of the eigensurfaces can be used to extract some of the most important information involved in data with complex covariance structure. We also extend the methods to handle multivariate function-valued processes. The estimated covariance structure is shown to be important to analyse joint variation in the data and is further used in our proposed multiple functional partial least squares regression model. We show that the interaction between the scalar response variable and function-valued covariates can be explained by fewer terms than in a regression model which uses multivariate functional principal components. Simulation studies and applications to real data show that our proposed approaches provide new insights into the data and excellent prediction results

Volumetric MRI Reconstruction from 2D Slices in the Presence of Motion

Despite recent advances in acquisition techniques and reconstruction algorithms, magnetic resonance imaging (MRI) remains challenging in the presence of motion. To mitigate this, ultra-fast two-dimensional (2D) MRI sequences are often used in clinical practice to acquire thick, low-resolution (LR) 2D slices to reduce in-plane motion. The resulting stacks of thick 2D slices typically provide high-quality visualizations when viewed in the in-plane direction. However, the low spatial resolution in the through-plane direction in combination with motion commonly occurring between individual slice acquisitions gives rise to stacks with overall limited geometric integrity. In further consequence, an accurate and reliable diagnosis may be compromised when using such motion-corrupted, thick-slice MRI data. This thesis presents methods to volumetrically reconstruct geometrically consistent, high-resolution (HR) three-dimensional (3D) images from motion-corrupted, possibly sparse, low-resolution 2D MR slices. It focuses on volumetric reconstructions techniques using inverse problem formulations applicable to a broad field of clinical applications in which associated motion patterns are inherently different, but the use of thick-slice MR data is current clinical practice. In particular, volumetric reconstruction frameworks are developed based on slice-to-volume registration with inter-slice transformation regularization and robust, complete-outlier rejection for the reconstruction step that can either avoid or efficiently deal with potential slice-misregistrations. Additionally, this thesis describes efficient Forward-Backward Splitting schemes for image registration for any combination of differentiable (not necessarily convex) similarity measure and convex (not necessarily smooth) regularization with a tractable proximal operator. Experiments are performed on fetal and upper abdominal MRI, and on historical, printed brain MR films associated with a uniquely long-term study dating back to the 1980s. The results demonstrate the broad applicability of the presented frameworks to achieve robust reconstructions with the potential to improve disease diagnosis and patient management in clinical practice

Radar Imaging in Challenging Scenarios from Smart and Flexible Platforms

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